Methods of improving quantified values for measured analytes in a biological sample

Abstract

Methods for improving the accuracy and precision of analyte measurements in an expired biological sample comprising: a biological sample, which is collected from a patient, said sample stabilized by a treatment, and wherein said sample is transported with a datalogger collecting a time and temperature; based on the given analyte, the type of biological sample, and preferably at least one of the time, temperature, and the treatment type, selecting a stability model that has been trained to predict a correction factor for the analyte when measured from the specific sample; obtaining the correction factor, wherein the correction factor is a predicted relative percent difference based upon at least the time and temperature degradation of the sample; applying the correction factor to a degraded value obtained from analyzing the biological sample to obtain a corrected value for the given analyte.

Description

METHODS OF IMPROVING QUANTIFIED VALUES FOR MEASURED ANALYTES IN A BIOLOGICAL SAMPLECross-Reference to Related Applications

[0001] This application claims the benefit of Australian Provisional Patent Application No. 2024901941 filed on June 26, 2024, the contents of which are incorporated herein by reference in their entirety.Field of the Invention

[0002] The present embodiments related to biological sample testing, and specifically to those biological samples tested via remote testing, wherein spoilage is a common problem. However, it will be appreciated that the invention is not limited to this particular field of use.Background of the Invention

[0003] Any discussion of the background art throughout the specification should in no way be considered as an admission that such background art is prior art, nor that such background art is widely known or forms part of the common general knowledge in the field in Australia or worldwide as at the priority date of the present application. All references, including any patents or patent applications, cited in this specification are hereby incorporated by reference, which means that it should be read and considered by the reader as part of this text.

[0004] Collection of biological samples at a laboratory setting is a common procedure in healthcare. Such sample collection yields easily replicable methods to stabilize and test biological samples proximate to the time they are given. However, a major problem remains that many patients do not live adjacent to or even near a hospital or laboratory setting to give a sample. This has led to high rates of patient noncompliance for routine biological sample collection.

[0005] Current biological sample protocols state that a biological sample must be analyzed within a predefined time period after collection to avoid any significant deviance of the degraded value from the true value at time zero (To), that is unaffected by sample degradation. In the case where a sample is stored for a duration that exceeds the maximum recommended delay, the sample must be recollected. While there exists some known variance in the duration of the maximum recommended delay dependent on the test analyte, sample type and sample treatment, many biological samples are simply determined to need recollection if they are collected outside of a tight time window, usually of no more than a few hours.

[0006] In certain instances, treatment, additive and stabilizers can be added to a biological sample. Thus, methods of sample treatment with chemical agents to alter the degradation profile and prolong the maximum recommended delay in some instances. Of course, another known method is sample preprocessing to prolong the maximum recommended delay; these include fractionating blood at the point of collection using a centrifuge or microfluidics devices. Furthermore, in some instances, a sample is converted to a more stable state to prolong the maximum recommended delay; these include dry blood storage by spotting on Guthrie cards for transport.

[0007] Transit of samples is also known in the art. Certain methods of controlling the sample transit condition to prolong the maximum recommended delay exist, such as cold chain management or thermal resistant packaging for transport. However, these methods are expensive and cumbersome for all parties and thus do not have high usage rates. Therefore, if a sample is intended for cold shipping, but arrives at room temperature, qualitative methods of rejecting samples for analysis based upon sample degradation or transit conditions can provide sample quality assessment; these include methods of determining if a sample has exceeded a predefined condition for the purpose of sample rejection.

[0008] Therefore, there is a pressing need for low cost systems and methods of controlling biological sample degradation during standard shipping methods, and to provide a means for correcting measured degraded biological sample analyte values obtained in the test laboratory of a degraded biological sample to more closely approximate the true value of the analytes. This method needs to be adaptable to the diverse requirements of each sample type, and preexisting collection and stabilization methods. Applicant has unexpectedly found that creation of a data training set, and use of a trained model therefrom, can improve the performance of quantification of a biological sample by magnitudes of improvement over prior methodologies, thus allowing for many more analytes to be tested that were heretofore unable to be tested due to degradation issues beyond even a few hours of transit. These and other methods are detailed in the embodiments herein.Summary of the Invention

[0009] It is an object of the present invention to overcome or ameliorate at least one or more of the disadvantages of the prior art, or to provide a useful alternative.

[0010] Applicant, as detailed herein, provides a solution greatly increasing the number of analytes that can be quantified through creation of a data training set and a model trained therefromto allow for quantitative adjustment of test results of analytes from remotely collected degraded biological samples, thereby extending the sample quality control threshold, and reducing the requirement for sample recollection. In some embodiments, models are developed for making analyte corrections, and wherein the models include a plurality of models, systems of models, competing models, and / or ensembles of models that work together to provide a predicted analyte correction value for a given sample and a given analyte.

[0011] Disclosed herein are details of the creation of data sets and models for determining values of a desired analyte from a given biological sample, particularly towards degraded biological samples, which are exposed to dynamic time and temperature variables, but which are captured directly or by a proxy, and wherein the dynamic variables can be fed into the models to predict a correction value for a given analyte based upon those variables, and / or donor variables, and / or degradation variables.

[0012] According to a first aspect of the invention, there is provided a computer-implemented method for correcting an analyte measurement taken from a degraded biological sample, the method comprising:(a) obtaining a first set of rules that define stability model selection based on analyte type, sample type, and / or sample treatment;(b) obtaining a data set corresponding to the degraded biological sample, the data set having data indicative of an analyte type, a sample type, a sample treatment, if any, and a transit time, wherein a value indicative of transit time was uploaded from a sensor accompanying a package in which the degraded biological sample was received and / or via a proxy;(c) applying the first set of rules to the data set, and in response thereto, selecting a stability model from a plurality of stability models, the selected stability model specifically trained for the analyte type, the sample type indicated in the data set, and the sample treatment, if any indicated in the data set;(d) generating a correction factor by evaluating the value of the transit time against the selected stability model; and(e) providing the correction factor for applying to a degraded analyte measurement taken from the degraded biological sample, wherein the degraded analyte measurement corresponds to the analyte indicted in the data set.

[0013] In a further embodiment, the method wherein the first set of rules has temperature parameters encoded as a binary variable.

[0014] In a further embodiment, the method further comprising producing a corrected value by applying the correction factor to the analyte measurement.

[0015] In a further embodiment, the method wherein the first set of rules are based on a decision tree or an encoding of a regression equation; and / or wherein the first set of rules are based on a decision tree a and a second set of rules are based on binary encoding of a regression equation.

[0016] In a further embodiment, the method wherein the first set of rules includes a selection criteria relating to transit time and / or transit temperature.

[0017] In a further embodiment, the method wherein the set of rules includes wherein transit time is selected from the group consisting of: greater than 12 hours, greater than 24 hours, greater than 36 hours, greater than 48 hours, greater than 72 hours, between 24 hours and 36 hours, between 24 hours and 48 hours, between 24 hours and 60 hours, between 24 hours and 72 hours, between 36 hours and 48 hours, and between 48 hours and 72 hours.

[0018] In a further embodiment, the method wherein a temperature criteria is selected from the group consisting of: mean sample transit temperature, maximum sample transit temperature, sample days at 23°C to 25°C, sample days at below 13°C, minimum sample transit temperature, sample days at above 35 °C, sample days at 13 °C to 23 °C, and cumulative sample temperature, and combinations thereof.

[0019] In a further embodiment, the method wherein the first set of rules includes one or more donor attributes, and / or one or more degradation attributes.

[0020] In a further embodiment, the method wherein the donor attributes relate to donor demographics, donor health, or both.

[0021] In a further embodiment, the method wherein the data set includes data indicative of one or more of a transit time, a transit temperature, donor demographics, donor health, and sample degradation.

[0022] In a further embodiment, the method wherein the sensor is a timer, a temperature sensor, or both.

[0023] In a further embodiment, the method wherein the selected stability model was trained using an environmental attribute with of weight of between: 0% to about less than 70%, less thanabout 30% to about 100%, greater than or equal to about 30% to about 100%, greater than or equal to about 60% to about 100%, or 100%.

[0024] In a further embodiment, the method wherein the selected stability model was trained using a weight factor of: correction factor= y + (32 — 100) x environmental + (0 — 38) x donordemo + (0 — 24) x donorhealth + (0 — 7) x degradation wherein indicates a value for ay-intercept.

[0025] In a further embodiment, the method wherein the sample treatment is a physical treatment or a chemical treatment.

[0026] In a further embodiment, the method wherein the chemical treatment is ethylenediaminetetraacetic acid (EDTA), lithium heparin, hydrochloric acid (HC1), sodium dodecyl sulfate (SDS) / TRIS (tris(hydroxymethyl)aminomethane) / EDTA, a protease inhibitor, sodium citrate, RPMI (Roswell Park Memorial Institute Medium), HEPES (4-(2-hy droxy ethyl)- 1 -piperazineethanesulfonic acid),FICOLL®70 (pPoly(sucrose-co-epichlorhydrin)), and combinations thereof.

[0027] According to a further aspect of the invention, there is provided a computer program element comprising computer program code means to make a computer execute a procedure for correcting an analyte measurement taken from a degraded biological sample according to the method of the first aspect.

[0028] According to a further aspect of the invention, there is provided a computer readable medium, having a program recorded thereon, where the program is configured to make a computer execute a procedure to for correcting an analyte measurement taken from a degraded biological sample according to the method of the first aspect.

[0029] According to a second aspect of the invention, there is provided a computer-implemented method for improving accuracy and precision of analyte measurements in a degraded biological sample, the method comprising:(a) uploading, from a sensor included with a package in which the degraded biological sample was received, at least one of a transit time or a transit temperature and / or via a proxy;(b) identifying, from a database storing data about the degraded biological sample, a given analyte and a type of biological sample, wherein the given analyte corresponds to the analyte measurement to be corrected and the type of biological sample is selected from one of a body tissue sample or a body fluid sample;(c) based on the given analyte and the type of biological sample, selecting a stability model that has been trained to predict a correction factor for the given analyte when measured in the identified type of biological sample, wherein training utilizes a specifically designed machine learning or artificial intelligence model;(d) inputting the transit time and / or the transit temperature into the selected stability model and in response thereto, obtaining the correction factor, wherein the correction factor is a predicted relative percent difference between a true value and a test value;(e) dividing an uncorrected value of the given analyte by the obtained correction factor, wherein the uncorrected value is determined by measuring the given analyte in the degraded biological sample; and(f) in response to dividing, obtaining a corrected value for the given analyte, wherein the corrected value corresponds to an analyte measurement that would have occurred if the given analyte was measured before the degraded biological sample expired.

[0030] In a further embodiment, the method wherein the stability model is selected based upon the analyte, the type of biological sample, and / or presence or absence of a treatment, and / or at least one transit time, and / or at least one transit temperature.

[0031] In a further embodiment, the method wherein for a given analyte, the selected stability model will predict a different correction factor based on the type of biological sample.

[0032] In a further embodiment, the method wherein the degraded biological sample is determined to be expired by:(i) a time that exceeds a time in which accuracy, precision, or both for a particular analyte has been affected by a delay in sample processing; and / or(ii) wherein an existing time and / or total allowable error threshold for the particular analyte is established by an established authority, a particular laboratory, or both.

[0033] According to a further aspect of the invention, there is provided a computer program element comprising computer program code means to make a computer execute a procedure for improving accuracy and precision of analyte measurements in a degraded biological sample according to the method of the second aspect.

[0034] According to a further aspect of the invention, there is provided a computer readable medium, having a program recorded thereon, where the program is configured to make a computer execute a procedure for improving accuracy and precision of analyte measurements in a degraded biological sample according to the method of the second aspect.

[0035] According to a third aspect of the present invention, there is provided a computer-implemented method for correcting an analyte measurement taken from a degraded biological sample, the method comprising:(a) obtaining a first set of rules that define stability model selection based on analyte type, sample type, and / or sample treatment;(b) obtaining a data set corresponding to the degraded biological sample, the data set having data indicative of an analyte type, a sample type, a sample treatment, if any, a transit time, and a transit temperature, wherein a value indicative of transit time and transit temperature is uploaded from a sensor accompanying a package in which the degraded biological sample is received and / or via a proxy;(c) applying the first set of rules to the data set, and in response thereto, selecting a stability model from a plurality of stability models, the selected stability model specifically trained for the analyte type, the sample type indicated in the data set, the sample treatment, and at least one of the transit time, transit temperature, or both;(d) generating a correction factor by evaluating the value of the transit time and transit temperature against the selected stability model; and(e) providing the correction factor for applying to a degraded analyte measurement taken from the degraded biological sample, wherein the degraded analyte measurement corresponds to the analyte indicted in the data set.

[0036] According to a further aspect of the invention, there is provided a computer program element comprising computer program code means to make a computer execute a procedure for correcting an analyte measurement taken from a degraded biological sample according to the method of the third aspect.

[0037] According to a further aspect of the invention, there is provided a computer readable medium, having a program recorded thereon, where the program is configured to make a computer execute a procedure for correcting an analyte measurement taken from a degraded biological sample according to the method of the third aspect.

[0038] According to a fourth aspect of the present invention, there is provided a computer-implemented method for improving accuracy and precision of analyte measurements in a degraded biological sample, the method comprising:(a) uploading, from a sensor included with a package in which the degraded biological sample was received, at least one of a transit time or a transit temperature, and / or via a proxy;(b) identifying, from a database storing data about the degraded biological sample, a given analyte and a type of biological sample, wherein the given analyte corresponds to the analyte measurement to be corrected and the type of biological sample is selected from one of a body tissue sample or a body fluid sample;(c) based on the given analyte and the type of biological sample, selecting a stability model that has been trained to predict a correction factor for the given analyte when measured in the identified type of biological sample, wherein training utilizes a specifically designed machine learning or artificial intelligence model that selects a best fit model based upon: correction factor = y + (32 — 100) x environmental + (0 — 38) x donordemo +(0 — 24) x donorhealth + (0 — 7) x degradation wherein indicates a value for ay-intercept;(d) inputting the transit time and / or the transit temperature into the selected stability model and in response thereto, obtaining the correction factor, wherein the correction factor is a predicted relative percent difference between a true value and a test value;(e) dividing an uncorrected value of the given analyte by the obtained correction factor, wherein the uncorrected value is determined by measuring the given analyte in the degraded biological sample; and(f) in response to dividing, obtaining a corrected value for the given analyte, wherein the corrected value corresponds to an analyte measurement that would have occurred if the given analyte was measured before the degraded biological sample expired.

[0039] According to a further aspect of the invention, there is provided a computer program element comprising computer program code means to make a computer execute a procedure for improving accuracy and precision of analyte measurements in a degraded biological sample according to the method of the fourth aspect.

[0040] According to a further aspect of the invention, there is provided a computer readable medium, having a program recorded thereon, where the program is configured to make a computerexecute a procedure for improving accuracy and precision of analyte measurements in a degraded biological sample according to the method of the fourth aspect.

[0041] According to a fifth aspect of the present invention, there is provided a method for preparing a biological sample for measuring an analyte after the sample is degraded by time, the method comprising:(a) obtaining a biological sample from a patient, the biological sample within a vessel comprising a sample additive;(b) providing an environmental sensor, suitable for capturing a transit time elapsed and a transit temperature at regular intervals from the time of capture of the biological sample to an end point;(c) providing, to a database, data relating to type of captured biological sample, type of additive, and an environmental data from the environmental sensor;(d) selecting, based upon the environmental data, the type of captured biological sample, and type of additive, a model for quantifying a degradation constant;(e) analyzing the sample to obtain a degraded value for the analyte of interest; and(f) based on the selected model, calculating a correction factor to be applied to the degraded value of the desired analyte.

[0042] In a further embodiment, the method further comprising wherein the database further comprises a donor attribute.

[0043] In a further embodiment, the method wherein the selected model was trained using a weight factor of: correction factor= y + (32 — 100) x environmental + (0 — 38) x donordemo+ (0 — 24) x donorhealth + (0 — 7) x degradation wherein indicates a value for ay-intercept.

[0044] In a further embodiment, the method wherein the selected model includes wherein transit time is selected from the group consisting of: greater than 12 hours, greater than 24 hours, greater than 36 hours, greater than 48 hours, greater than 72 hours, between 24 hours and 36 hours, between 24 hours and 48 hours, between 24 hours and 60 hours, between 24 hours and 72 hours, between 36 hours and 48 hours, and between 48 hours and 72 hours.

[0045] In a further embodiment, the method wherein the step of selecting a model includes a temperature criteria, wherein the temperature criteria is selected from the group consisting of: mean sample transit temperature, maximum sample transit temperature, sample days at 23°C to 25°C, sample days at below 13°C, minimum sample transit temperature, sample days at above 35°C, sample days at 13°C to 23°C, cumulative sample temperature, and combinations thereof.

[0046] In a further embodiment, the method wherein the sample additive is a chemical treatment, and / or wherein the sample is prepared with a physical treatment.

[0047] In a further embodiment, the method wherein the chemical treatment is ethylenediaminetetraacetic acid (EDTA), lithium heparin, hydrochloric acid (HC1), sodium dodecyl sulfate (SDS) / TRIS (tris(hydroxymethyl)aminomethane) / EDTA, a protease inhibitor, sodium citrate, RPMI (Roswell Park Memorial Institute Medium), HEPES (4-(2-hy droxy ethyl)- 1 -piperazineethanesulfonic acid),FICOLL®70 (poly(sucrose-co-epichlorhydrin)), and combinations thereof.

[0048] According to a sixth aspect of the present invention, there is provided a method for preparing a biological sample for measuring an analyte after the sample is degraded by time, the method comprising:(a) obtaining from a patient, the biological sample within a vessel, and performing on said sample a treatment;(b) providing an environmental sensor, suitable for capturing total time elapsed and temperature at regular intervals from the time of capture of the biological sample to an end point;(c) providing data to a database, the data relating to type of captured biological sample, type of treatment, and an environmental data from the environmental sensor;(d) based upon the temperature and time data, the type of captured biological sample, and type of additive, selecting a model for quantifying a degradation constant; and(e) preserving the captured biological sample at the end point wherein the degradation constant derived can be utilized to provide a correction of an analyte from the captured biological sample.

[0049] According to a seventh aspect of the present invention, there is provided a method of preparing a degraded sample for quantification of a desired analyte, the method comprising:(a) obtaining a biological sample from a patient, the biological sample within a vessel comprising a sample additive;(b) providing an environmental sensor, suitable for capturing total time elapsed and temperature at regular intervals from time of capture of the biological sample to an end point;(c) providing data to a database, the data relating to type of captured biological sample, type of additive, and environmental data from the environmental sensor;(d) based upon the temperature and time data, the type of captured biological sample, and type of additive, selecting a model for quantifying a degradation constant; and(e) capturing the desired portion of the sample for subsequent analysis wherein the constant derived can be utilized to provide a correction of an analyte from the captured biological sample.

[0050] According to an eighth aspect of the present invention, there is provided a method of preparing a degraded portion of a sample capable of being analyzed for a desired analyte wherein said sample would otherwise be discarded due to sample error, the method comprising:(a) obtaining a request for the sample and the desired analyte from said sample, and storing the same within a database;(b) capturing the sample within a specimen container, said specimen container comprising an additive, and contemporaneously beginning a data logger to measure a transit time and a transit temperature;(c) subjecting the sample to measure elapsed transit time and transit temperature conditions, said elapsed transit time and transit temperature conditions sufficient to degrade the sample to otherwise disqualify the sample from being usable for sampling of a desired analyte;(d) upon receipt of the sample within a facility for analyzing the sample, terminating the data logger and obtaining an environmental data comprising the elapsed transit time from capturing the sample to the termination of the data logger, and a temperature data during the elapsed transit time;(e) inputting sample type, the desired analyte, the additive, total elapsed transit time, and the temperature data into a model to define a correction factor;(f) processing the sample to obtain a desired portion of the sample for calculating of a degraded value of the desired analyte; and(g) calculating a corrected value of the analyte by applying the correction factor to the degraded value.

[0051] In a further embodiment, the method wherein the temperature is obtained at time intervals during the elapsed transit time.

[0052] In a further embodiment, the method wherein the set of rules includes wherein transit time is selected from the group consisting of: greater than 12 hours, greater than 24 hours, greater than 36 hours, greater than 48 hours, greater than 72 hours, between 24 hours and 36 hours, between 24 hours and 48 hours, between 24 hours and 60 hours, between 24 hours and 72 hours, between 36 hours and 48 hours, and between 48 hours and 72 hours.

[0053] In a further embodiment, the method wherein temperature criteria is selected from the group consisting of: mean sample transit temperature, maximum sample transit temperature, sample days at 23°C to 25°C, sample days at below 13°C, minimum sample transit temperature, sample days at above 35 °C, sample days at 13 °C to 23 °C, cumulative sample temperature, and combinations thereof.

[0054] In a further embodiment, the method wherein the selected model was trained using a weight factor of: correction value= y + (32 — 100) x environmental + (0 — 38) x donordemo+ (0 — 24) x donorhealth + (0 — 7) x degradation wherein indicates a value for ay-intercept.

[0055] In a further embodiment, the method wherein the additive is a chemical treatment or comprises a physical treatment.

[0056] In a further embodiment, the method wherein the chemical treatment is selected from the group consisting of: ethylenediaminetetraacetic acid (EDTA), lithium heparin, hydrochloric acid (HC1), sodium dodecyl sulfate (SDS) / TRIS (tris(hydroxymethyl)aminomethane) / EDTA, a protease inhibitor, sodium citrate, RPMI (Roswell Park Memorial Institute Medium), HEPES (4-(2-hy droxy ethyl)- 1 -piperazineethanesulfonic acid),FICOLL®70 (poly(sucrose-co-epichlorhydrin)), and combinations thereof.

[0057] According to a ninth aspect of the present invention, there is provided a method of correcting test results of a degraded sample having experienced a delay in time between collection and analysis of the degraded sample, the method comprising:(a) receiving within a collection container a patient biological sample received in a packaged state, said collection container comprising an additive, and wherein said sample packaging comprising:(i) one or more environmental sensors and a data logger or proxies thereof configured to measure and store environmental attribute data to which the patient biological sample was exposed during transit; and(ii) patient attribute data with respect to attributes of a patient;(b) providing a data reader for downloading the environmental attribute data from the data logger or use of historical, weather, and / or shipment tracking information as a proxy for environmental attribute data and associating the environmental attribute data and patient attribute data with the patient biological sample;(c) providing biological sample test hardware for measuring one or more analyte degraded value from a degraded patient biological sample;(d) providing a database for storing data regarding the biological patient sample comprising:(i) environmental attributes, sample type, and / or sample additive; and / or(ii) degradation attributes and / or donor health attributes;(e) providing a processor configured to receive the patient attribute data from the database and, selecting a machine learning or artificial intelligence (Al) model based upon the patient attribute data, wherein the selection is based upon a set of rules;(f) generating a correction factor based upon the selected model, wherein input to determine the correction factor is the environmental attributes, the sample type, and the sample additive;(g) generating a degraded value of said analyte from said patient biological sample; and(h) applying the correction factor to the degraded value to yield a corrected value.

[0058] According to a further aspect of the invention, there is provided a computer program element comprising computer program code means to make a computer execute a procedure for correcting test results of a degraded sample having experienced a delay in time between collection and analysis of the degraded sample according to the method of the ninth aspect.

[0059] According to a further aspect of the invention, there is provided a computer readable medium, having a program recorded thereon, where the program is configured to make a computer execute a procedure for correcting test results of a degraded sample having experienced a delay in time between collection and analysis of the degraded sample according to the method of the ninth aspect.

[0060] According to a tenth aspect of the present invention, there is provided a method of analyte correction from a biological sample having a level of degradation due to time and temperature exposure between collection and processing, the method comprising:(a) receiving a biological sample into a sample container, and applying a treatment to said biological sample;(b) activating a data logger upon providing the biological sample;(c) exposing the sample container to an environmental attribute of time and temperature;(d) capturing the environmental attributes from the data logger, uploading the same into a database associated with the biological sample;(e) processing the biological sample, and obtaining a degraded value for at least one analyte;(f) generating a correction factor by inputting the environmental attributes, the sample type, the analyte, and the treatment type into a model trained to determine a correction factor; and(g) applying the correction factor to the degraded value to obtain a corrected analyte value.

[0061] According to a further aspect of the invention, there is provided a computer program element comprising computer program code means to make a computer execute a procedure for analyte correction from a biological sample having a level of degradation due to time and temperature exposure between collection and processing according to the method of the tenth aspect.

[0062] According to a further aspect of the invention, there is provided a computer readable medium, having a program recorded thereon, where the program is configured to make a computer execute a procedure for analyte correction from a biological sample having a level of degradation due to time and temperature exposure between collection and processing according to the method of the tenth aspect.

[0063] In a further embodiment, the method further comprising providing degradation attribute hardware for determining one or more degradation attributes of the patient biological sample.

[0064] In a further embodiment, the method wherein the collection container comprises a unique ID, wherein the unique ID is stored with the database, and wherein the environmental data and the patient attribute data are associated with the unique ID within the database.

[0065] In a further embodiment, the method wherein the machine learning or Al model is trained by generating a first sample from a donor, the first sample having a true value of a first analyte, generating at least one test sample from the donor, said test sample undergoing a known environmental attribute, determining a test value for the first analyte, and training the machine learning or Al model on a difference between the true value of the analyte and the test value of the analyte based upon the known environmental attribute.

[0066] In a further embodiment, the method further comprising wherein the model is trained by inclusion of the degradation attributes and / or donor attributes.

[0067] In a further embodiment, the method wherein the biological sample is plasma, precentrifuged plasma, serum, dried blood spot, urine, saliva, and reproductive fluids.

[0068] In a further embodiment, the method further comprising performing one or more degradation analyses, processing the sample when the degradation analysis is within a predetermined range, and rejecting the sample if the degradation analysis is outside of the predetermined range.

[0069] In a further embodiment, the method further comprising wherein the selected model is biased toward the environmental attribute of the sample.

[0070] In a further embodiment, the method wherein the donor attribute is selected from the group consisting of: age, sex, ethnicity, BMI, fasting status, time of day, medication, medical conditions, and combinations thereof.

[0071] In a further embodiment, the method wherein the environmental attribute is time, temperature, humidity, pressure, geolocation, acceleration, light, or combinations thereof.

[0072] In a further embodiment, the method wherein the degradation attribute is selected from the group consisting of: DNA, white blood cells, hemoglobin, sodium, chloride, potassium, a color, one or more of a proxy of entropy of a biological sample, blood cell lysis, presence or absence of DNA, an analyte different from an analyte of interest, evaporation, and combinations thereof.

[0073] In a further embodiment, the method wherein the model is determined by finding an optimal value for a parameter in order to minimize an error or loss function by selecting from a gradient descent model, close-form solution, regularization, Bayesian inference estimates, and / or cross-validation.

[0074] In a further embodiment, the method wherein the environmental attribute is a dynamic environmental condition and wherein the dynamic environmental condition can be obtained by a data logger or by a proxy, said proxy corresponding to an ambient temperature at a location of the sample.

[0075] In a further embodiment, the method wherein donor information is obtained from an electronic medical record.

[0076] In a further embodiment, the method wherein the degradation attributes from the biological sample are provided by the attributes within the biological sample itself.

[0077] In a further embodiment, the method wherein weight is applied to a variable within the model wherein the weight is based upon the known environmental attribute associated with a sample.

[0078] In a further embodiment, the method wherein the sample additive is selected based upon lack of interference with the test for the desired analyte.

[0079] According to an eleventh aspect of the present invention, there is provided a method of providing corrective modification of preserved degraded biological samples, the method comprising:(a) obtaining a biological sample within a collection vessel, said collection vessel comprising an additive therein;(b) providing a data logger comprising an environmental sensor within packaging for transport of the biological sample;(c) capturing an environmental attribute perceived by the environmental sensor and storing the environmental attribute within the data logger;(d) upon receipt of the biological sample at a processing facility, obtaining the environmental attribute from the data logger;(e) performing an analysis of a desired analyte from the biological sample to obtain a degraded value of the desired analyte;(f) selecting a model for obtaining a correction factor, said model selected from input of analyte type, sample type, sample additive, and at least one environmental attribute;(g) inputting the at least one environmental attribute into the selected model and receiving a correction coefficient; and(h) applying the correction coefficient to the degraded value to obtain a corrected value of the desired analyte.

[0080] According to a further aspect of the invention, there is provided a computer program element comprising computer program code means to make a computer execute a procedure for providing corrective modification of preserved degraded biological samples according to the method of the eleventh aspect.

[0081] According to a further aspect of the invention, there is provided a computer readable medium, having a program recorded thereon, where the program is configured to make a computer execute a procedure for providing corrective modification of preserved degraded biological samples according to the method of the eleventh aspect.

[0082] In a further embodiment, the method wherein the biological sample is a blood sample, a preprocessed blood sample, urine, or saliva.

[0083] In a further embodiment, the method wherein the sample type is defined as the sample type in transit.

[0084] Throughout this specification, unless the context requires otherwise, the words “comprise”, “comprises” and “comprising” will be understood to imply the inclusion of a stated step or element or group of steps or elements but not the exclusion of any other step or element or group of steps or elements. Any one of the terms “including” or “which includes” or “that includes” as used herein is also an open term that also means including at least the elements / features that follow the term, but not excluding others. Thus, “including” is synonymous with and means “comprising”.

[0085] In the claims, as well as in the summary above and the description below, all transitional phrases such as “comprising”, “including”, “carrying”, “having”, “containing”, “involving”, “holding”, “composed of’, and the like are to be understood to be open-ended, i.e., to mean “including but not limited to”. Only the transitional phrases “consisting of’ and “consisting essentially of’ alone shall be closed or semi-closed transitional phrases, respectively.

[0086] Those skilled in the art will appreciate that the invention described herein is susceptible to variations and modifications other than those specifically described. The invention includes all such variation and modifications. The invention also includes all of the steps, features, formulations, and compounds referred to or indicated in the specification, individually or collectively and any and all combinations of any two or more of the steps or features.

[0087] Even though particular combinations of features are recited in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of possible implementations. In fact, many of these features may be combined in ways not specifically recited in the claims and / or disclosed in the specification. Although each dependent claim listed below may directly depend on only one claim, the disclosure of possible implementations includes each dependent claim in combination with every other claim in the claim set.Brief Description of the Figures

[0088] It should be noted in the following description that like or the same reference numerals in different embodiments denote the same or similar features.

[0089] Notwithstanding any other forms which may fall within the scope of the present invention, a preferred embodiment / preferred embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings in which:

[0090] FIG. 1 shows a flowchart diagram related to the creation of a stability model training data set using biological samples incorporating patient health conditions and subjected to environmental and conditions experienced during transit.

[0091] FIGS. 2A, 2B, and 2C show that a sample stability model derived from a specific sample type is inaccurate against different sample types when performing the same test. FIG. 2A and 2B depicts the discordant change over time in urea test results in plasma, precentrifuged plasma, serum, precentrifuged serum dried blood spot, urine and saliva. FIG. 2C contrasts the % accuracy improvement for urea test results when information about the sample types is included (Specific) compared to excluded (NonSpecific), where the Serum model was utilized for each solution, noting that Serum is identical for the specific and nonspecific results.

[0092] FIGS. 3 A and 3B show that a sample stability model derived from a blood sample treated with lithium heparin is inaccurate against blood samples treated with different clot activators and anticoagulants when performing the same test. FIG. 3 A depicts the discordant change over time in creatinine test results in blood treated with lithium heparin, EDTA and SST. Time at temperature refers to the mean temperature x days in transit. FIG. 3B contrasts the % accuracy improvement for creatinine test results when information about the sample treatment is included (Specific) compared to excluded (NonSpecific), as a ratio between the degraded value / true value.

[0093] FIGS. 4 A and 4B show that a sample stability model derived from an untreated urine sample is inaccurate against urine samples treated with preservatives when performing the same test. FIG. 4A depicts the discordant change over time in urea test results in untreated urine and urine treated with HCI. FIG. 4B depicts the % accuracy improvement for urea test results when information about the sample preservative is included (Specific) compared to excluded (NonSpecific).

[0094] FIGS. 5A and 5B show that a sample stability model derived from an untreated saliva sample is inaccurate against saliva samples treated with preservatives when performing the same test. FIG. 5A depicts the discordant change over time in urea test results in untreated urine and urine treated with SDS / TRIS / EDTA. FIG. 5B depicts the % accuracy improvement for urea test results when information about the sample preservative is included (Specific) compared to excluded (NonSpecific).

[0095] FIGS. 6A and 6B show that a sample stability model improves test accuracy and / or extends the maximum recommended delay for sample analysis when used in conjunction with enzymatic stabilization techniques. FIG. 6 A depicts testing for MCP1, using either EDTA or EDTA + a protease inhibitor. FIG. 6B depicts that when using the protease inhibitor, and selecting a generic model, vast differences are found in the results that would alter results of any test for the MCP1 analyte.

[0096] FIGS. 7A and 7B show that different stability materials using whole blood, dramatically alter the degradation profile of Basophil (BA) count. FIG. 7A depicts the relative difference for four different solutions, anti-coagulants (sodium citrate, EDTA), supplemental media (RMPI) and physical stabilizers (ficol70). FIG. 7B depicts these four solutions, when compared for uncorrected, nonspecific models, and specific model solutions. Notably, the specific model solutions significantly improve the prediction, instead of merely using the generic solution calibrated for EDTA.

[0097] FIGS. 8A-8H show that a sample stability model improves test agreement to the required total allowable error, accuracy (%basis), precision (Std dev) and / or extends the maximum recommended delay for sample analysis when sampleType is specified as a rule for model selection and prediction of correction factor. FIGS. 8 A and 8B depict data for improvement with correction for urea for different solutions of blood plasma. FIGS. 8C and 8D showing solutions for urea serum. FIGS. 8E and 8F address dried blood spots and urine samples. FIGS. 8G and 8H address saliva samples.

[0098] FIGS. 9 A and 9B show that a sample stability model improves test agreement to the required total allowable error, accuracy (%bias), precision (Std dev) and / or extends the maximum recommended delay for sample analysis when sampleTreatment is specified as a rule for model selection and prediction of correction factor. For the Calcium test analyte, selection of model using treatments with EDTA or Lithium heparin improves performance relative to a generic to the sample treatment type. Notably, by selecting for the sample treatment type, massive gains in accuracy are depicted in FIG. 9B.

[0099] FIGS. 10A and 10B used MCP1 as the analyte and selecting for sample treatment type, similar to that in FIG. 9 and again FIG. 10B yields massive improvements in accuracy of the testing results when selecting models in this manner.

[0100] FIGS. 11A and 11B show another example by testing HGB as the analyte and yielding significantly improved results when modeling for the sample additive.

[0101] FIGS. 12A and 12B depict glucose testing from urine, when including sample type specific independent variables, such as nitrite, pH, and / or specific gravity.

[0102] FIG. 13 depicts a summary of selection by, uncorrected, with environmental factors, when including sample treatment as a model selection factor, and when using sample type as a model selection factor. By using specific models, the difference yields substantial changes in predictive outcomes.

[0103] FIG. 14 depicts a flow diagram of determining a corrected value of an analyte from a degraded sample.

[0104] FIG. 15 shows a computing device on which the various embodiments described herein may be implemented in accordance with an embodiment of the present invention.

[0105] FIG. 16 shows a client server implementation of a test correction platform for correction of test data from a client device by a client accessible server device providing a software as a service application.Definitions

[0106] The following definitions are provided as general definitions and should in no way limit the scope of the present invention to those terms alone, but are put forth for a better understanding of the following description.

[0107] Throughout this specification, unless the context requires otherwise, the words “comprise”, “comprises” and “comprising” will be understood to imply the inclusion of a stated step or element or group of steps or elements but not the exclusion of any other step or element or group of steps or elements.

[0108] Any one of the terms “including” or “which includes” or “that includes” as used herein is also an open term that also means including at least the elements / features that follow the term, but not excluding others. Thus, “including” is synonymous with and means “comprising”.

[0109] In the claims, as well as in the summary above and the description below, all transitional phrases such as “comprising”, “including”, “carrying”, “having”, “containing”, “involving”, “holding”, “composed of’, and the like are to be understood to be open-ended, i.e., to mean “including but not limited to”. Only the transitional phrases “consisting of’ and “consisting essentially of’ alone shall be closed or semi-closed transitional phrases, respectively.

[0110] Reference to positional descriptions and spatially relative terms), such as “inner”, “outer”, “beneath”, “below”, “lower”, “above”, “upper” and the like, are to be taken in context of the embodiments depicted in the figures, and are not to be taken as limiting the invention to the literal interpretation of the term but rather as would be understood by the skilled addressee.

[0111] Although the terms “first”, “second”, “third”, etc. may be used herein to describe various elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms may be only used to distinguish one element, component, region, layer or section from another region, layer or section. Terms such as “first”, “second”, and other numerical terms when used herein do not imply a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the example embodiments.

[0112] It will be understood that when an element is referred to as being “on”, “engaged”, “connected” or “coupled” to another element / layer, it may be directly on, engaged, connected or coupled to the other element / layer or intervening elements / layers may be present. Other words used to describe the relationship between elements / layers should be interpreted in a like fashion (e.g., “between”, “adjacent”). As used herein the term “and / or” includes any and all combinations of one or more of the associated listed items.

[0113] The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. The use of the singular includes the plural unless specifically stated otherwise. As used herein, the singular forms “a”, “an” and “the” may be intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0114] Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, preferred methods and materials are described. It will be appreciated that the methods, apparatus and systems described herein may be implemented in a variety of ways and for a variety of purposes. The description here is by way of example only.

[0115] Also, data structures may be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.Detailed Description of the Invention

[0116] Those skilled in the art will appreciate that the invention described herein is susceptible to variations and modifications other than those specifically described. The invention includes all such variation and modifications. The embodiments herein comprise one or more of the steps features, formulations, and compounds referred to or indicated in the specification, individually or collectively and any and all combinations of the steps or features. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, preferred methods and materials are described. It will be appreciated that the methods, apparatus, and systems described herein may be implemented in a variety of ways and for a variety of purposes. The description here is by way of example only.

[0117] Modifications and variations such as would be apparent to the skilled addressee are considered to fall within the scope of the present invention. The present invention is not to be limited in scope by any of the specific embodiments described herein. These embodiments are intended for the purpose of exemplification only. Functionally equivalent products, formulations and methods are clearly within the scope of the invention as described herein. It will be understoodby those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

[0118] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the invention belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. For the purposes of the present invention, the following terms are defined below.

[0119] The term “test value” or “degraded value” refers to the raw output measurement from an analyzer for an analyte in a biological sample that is uncorrected and typically from a sample in which transport or an extended time between the collection time and the analysis time has affected the accuracy or precision of that result. We use the term test value typically when building data sets, while degraded value, preferably refers to a patient sample that is degraded by transit before being analyzed.

[0120] The term “true value” refers to a measurement of an analyte in a biological sample shortly after sample collection, or with minimal delay, and is considered to be the correct value of the analyte measurement with respect to best practice measurement methods.

[0121] The term “corrected value” refers to a test value which has been corrected according to a correction factor or equation derived from the machine learning model disclosed herein to obtain a measurement value as close as possible to the true value. Such corrected values typically mean the degraded value of an analyte divided by the correction factor.

[0122] The term “sample type” refers to a biospecimen such as blood, urine, saliva, etc., that are typically collected for the purpose of analysis including testings for health management.

[0123] The terms “sample treatment”, “sample additive” or “sample preservation” include those such as EDTA, lithium heparin, sodium citrate, media, protease inhibitors or other chemical or physical agents used in sample collection and transport to prevent clotting, preserve sample integrity, inhibit enzymatic activity, inhibits growth of microorganisms or other processes that affect the rate of sample degradation and / or testing accuracy.

[0124] The term “analyte” is a catch-all term that refers to the quantification of a measurand in a biological sample.

[0125] The term “proxy” refers to any covariate used as a substitute for an independent variable based on an association and / or correlation with the independent variable discussed herein. By way of example, quantification of plasma color change by light absorbance is a proxy for plasma hemoglobin concentration.

[0126] The articles “a” and “an” are used herein to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. By way of example, “an element” refers to one element or more than one element.

[0127] The term “about” is used herein to refer to quantities that vary by as much as 10% to a reference quantity. The use of the word “about” to qualify a number is merely an express indication that the number is not to be construed as a precise value.

[0128] The term “model” means a mathematical representation of a process that is trained on data to make predictions or decisions.

[0129] The phrase “and / or” as used herein in the specification and in the claims should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and / or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and / or” clause, whether related or unrelated to those elements specifically identified.

[0130] As used herein in the specification and in the claims, the phrase “at least one” in reference to a list of one or more elements should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified.

[0131] The various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and / or programming or scripting tools, machine learning or Al libraries,and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine. In this respect, various inventive concepts may be embodied as a computer readable storage medium of any form or other non-transitory medium or tangible computer storage medium encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the invention discussed above. The computer readable medium or media can be transportable, or accessed remotely, such as via a cloud server, or Web site, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of the present invention as discussed above.

[0132] The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer, multiple separate computers, or other processor to implement various aspects of embodiments as discussed above. Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments. Such programs or software can be run on a suitable computer or computers as is known to those of ordinary skill in the art. In certain embodiments, data may be directly obtained, by connecting a device to a computer to download data from the device, or may be acquired by use of electronic data transfer.

[0133] Also, various inventive concepts may be embodied as one or more methods, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments. For the purpose of this specification, where method steps are described in sequence, the sequence does not necessarily mean that the steps are to be carried out in chronological order in that sequence, unless there is no other logical manner of interpreting the sequence.

[0134] Measurement of analytes (also referred to as tests) from a biological sample, is a routine medical procedure that allows for a medical practitioner to evaluate a patient for certain issues. Analytes here are broadly referring to a measurand from the biological sample. In many cases, biological samples are collected at a hospital or outpatient office and the samples are immediatelyanalyzed or are manipulated in certain manners, such as separating the plasma from the hematocrit, and / or freezing a sample after such separation, or preserving the sample in other known methodologies that prevents degradation of a sample. Even under such care, sampling error rates are approximated at between 0.1% and 6.5%. However, the largest issue is patient noncompliance, meaning that the test is not even taken, which is estimated at between 6.8% and 62%. Indeed, a lack of access to routine biological testing impacts around 43% of the world’s population that are classified as living in a rural or remote area. This limited access to health services leads patients to delay or avoid essential medical intervention and participation and adherence in clinical trials, while also driving life expectancy gaps across age, sex, and socioeconomic statuses. Therefore, remote testing is likely to improve patient health and compliance with testing, as the burden for such testing will be reduced. However, current protocols make remote sample collection all but impossible for virtually all desired analytes, thus preventing its widespread use.

[0135] The present embodiments address the significant and long felt need to be able to quantify analytes measured in degraded biological samples where degradation alters the amount of analytes in some way (e.g., more or less of the analyte) typically due to the passage of time between sample collection and processing. Current methods for addressing analyte degradation due to latency in the time between providing the sample and processing the sample or another factor affecting analyte degradation are limited to tossing out the sample and recollecting for analysis within a certain time or other parameter. Each type of biological sample (e.g., blood, plasma, serum, urine, saliva) presents a unique challenge, with varying susceptibilities to degradation and exposure to potential contaminants that accelerate degradation causing erroneous test results. Shipping of biological samples requires care based on degradation processes. For example, blood samples naturally degrade once extracted from the body in a process known as blood lysis (haemolysis) whereby red blood cells rupture and their contents (cytoplasm) leak out into the surrounding fluid (e.g., blood plasma). Urine samples are particularly susceptible to microbial contamination as a result of improper collection that can dramatically impact pH and specific gravity that alter the rates of sample degradation. Similarly, the degradation of saliva samples can be impacted by microbial content, but also the last meal relative to collection and even time of collection through circadian rhythms. There remains a significant need for the ability to quantify analytes in biological samples that have degraded to a point (aka “expired”) where measurements are not considered to be accurate, precise, and / or reliable. This is especially true for those biological samples that are self-collected at home or collected at another location that is remote from the lab in which the testing was done. In turn, there is also a significant need for ways to enable self-collection of biological samples that can be shipped at a relatively low-cost to a laboratory sothat analytes present in the biological sample can be accurately quantified even if the biological sample is degraded. By preparing samples to be analyzed post degradation, Applicant has dramatically improved the option for remote collection of biological samples.

[0136] There are a variety of methods used to collect and physically or chemically stabilize a sample in transit. Certain blood tests require samples to be treated with specific coagulants and anticoagulants to minimize interference with the test chemistry and maximize accuracy. Similarly, urine and saliva samples are routinely treated with preservatives to minimize the effects of test interference, microbial growth and contamination. Even collection and transport devices and methods that physically stabilize, remove common contaminants or control environmental conditions of a sample have been devised. Each of these treatments and / or methods impact the rate and sensitivity of a sample to degradation. An adaptable solution to this issue needs to be addressed for at-home collection of biological sampling to account for differences in sample collection, treatment and stabilization.

[0137] Laboratories that are utilized to evaluate a given analyte have certain regulatory and accreditation bodies that have enacted strict standards. These standards seek to mitigate the risk of false positive or false negative results, by limiting error of a desired measured analyte from a given sample. Thus, for many remotely collected samples, the latency between collection and evaluation of the sample is well beyond acceptable standards, thus resulting in the sample being rejected. To address this challenge, a number of hardware solutions have been devised including:(i) point-of-care testing (POCT);(ii) preprocessing of the sample;(iii) chemical stabilization of sample;(iv) physical stabilization of the sample; and(v) environmental control of the sample.

[0138] The present embodiments serve a critical purpose in the modern healthcare economy by enabling patients to remotely collect or self-collect a biological sample with minimal equipment, training or instruction, and return the sample by mail for testing. The primary challenge associated with remote sample collection is preventing or controlling sample changes (e.g., analyte reduction or accumulation) that occur during transit thereby causing erroneous test results. Such endeavors will promote equitable access to healthcare, particularly in rural and remote areas and for patients that require frequent care.

[0139] POCT is the system / method of having a miniaturized / simplified equipment located at a clinic where patients visit, essentially bringing the laboratory to the patient. Using blood as an example, a sample can be collected from the patient during an in-person visit and immediately analyzed in order to discuss the subsequent test results in a single visit. These methods are still expensive to set up and maintain with high staff turnover in rural areas, and have a limited set of available tests. Indeed, their cost and transit difficulties make such solutions unusable in most situations.

[0140] Preprocessing of a sample requires the patient to purify their sample into a state that is more stable in transit by removing potential contaminants that accelerate the degradation process. Using blood as an example, a patient may be required to fractionate plasma from the hematocrit by centrifugation or use of a microfluidics device. These methods are expensive, have high rates of user error and are not suitable for all sample types or tests. Thus, because of high error rates, such solutions are generally unsuitable for use.

[0141] Chemical stabilization of a sample involves the addition of a chemical / biological agent that inhibits contaminants that accelerate the degradation process. Using urine for example, concentration hydrochloric acid is often added to prevent the growth of microorganisms. However, these methods are not suitable for all sample types and the chemical can have unknown effects on test result accuracy and precision that are typically limited to 2-3 days.

[0142] Physical stabilization of a sample involves the application of a sample onto a medium that prolongs the stability of a sample. Using blood as an example, blood can be dried onto paper (Guthrie card) that preserves the integrity of DNA for genetic analysis. These methods are not suitable for all sample types and the sample extraction from the physical medium can have unknown effects on test result accuracy and precision that are typically limited to 2-3 days.

[0143] Environmental control involves a method that aims to minimize the impact of the environment of the sample during transit. Such methods include but are not limited to the use of a medical courier to reduce transit time or the inclusion of temperature resistant or moderating packaging / vessel / containers for the sample during transit. These methods are expensive and are not suitable for all sample types or tests.

[0144] There are no published software-based solutions to challenges of remote sample collection; however, methods have been developed that estimate sample limits for quality control purposes and estimate shelf-life of reagents stored for prolonged periods at fixed temperatures. These methods, however, are qualitative, not quantitative, and are not suitable for dynamicprocesses akin to the diurnal and seasonal temperature fluctuation to which a biological sample may be subjected during transit.

[0145] Here we describe a combination hardware and software-based solution that accounts for sample type and / or treatment / additives / preservation methods and can be used independently or in conjunction with existing hardware solutions to provide accurate and precise analyte measurements in biological samples that have been degraded such as by an environmental condition experienced during transit. The model solution allows for a degraded biological sample to be quantified and used for its intended purpose significantly beyond the typical time window after collection, which enables an expansion of remote collection and subsequent transport of various types of biological samples.

[0146] Applicant devised a strategy to train a plurality of stability models using a data-specific training sets, to assist in correcting measurement values of a desired analyte when a given biological sample having some level of degradation. The specific aim is to increase the time between providing the sample, usually into a specimen container, which is prepared in a known manner as detailed herein, and the processing of the sample, usually at a laboratory. The first step in such a process is to create a data training set of sufficient breadth to enable the training of several models to assist in predicting the shift in analytes from a given biological sample based upon certain factors such as biological sample type, biological sample treatment, and / or environmental factors, such as time and / or temperature. Further models can be trained based upon the inclusion of certain donor information (e.g., sex and / or age) and / or degradation information (e.g., indicative of sample-specific degradation). In some instances, a simple model is sufficient, while in others, a combination of attributes greatly reduces the error rate of a corrected measurement value as compared to a true measurement value of a particular analyte. As such, Applicant has developed a plurality of stability models, wherein at least one of the stability models in the plurality is trained to predict a correction factor based at least on a particular type of biological sample, a particular analyte to be measured, and an environmental factor, such as time and / or temperature for which the biological sample was in transit.

[0147] FIG. 1 shows a method (100) for building the training data set for creation of a given stability model. The kinetics of environmental parameters were determined for sample transport to the laboratory by post using a data logger fixed to a postage satchel. The minimum and maximum transit time was between less than one and eight days with a mean of 1.5 days. Replicates of donor samples were subjected to varying degrees of each environmental attribute (105) in vitro, akin to those experienced by real-world samples. Sample aliquots were exposed tostatic and dynamic temperature fluctuations, repeated over multiday cycles, with and without agitation to simulate anticipated transit conditions. Dynamic temperatures, consisting of a sine wave temperature ramp between the minimum and maximum temperatures experienced over the day / night cycle at the peak of summer and the lows of winter, were included to simulate the effect of sample transport in an uncontrolled environment. Conversely, static temperatures were also utilized to replicate samples transported under common controlled conditions including, ice at a temperature of 4°C, cold packs at a temperature of 15°C, and ambient at a temperature of 22°C. Modifying the time, temperature and agitation attributes experimentally were intended to capture the permutations of environmental conditions (attributes) (105) a biological sample could experience during transit.

[0148] Referring to FIG. 1, method (100) is used to build a training data set for creation of stability models. In this particular model formation, donors (101) provided a biological sample (102) of a given type (e.g., urine, blood, saliva, etc.), each of which was divided (104) into a plurality of aliquots (for example, about fifty to one hundred aliquots per donor) for testing and simulation of environmental attributes. Donor and health attributes (103) such as age, sex, body mass index (BMI), medication(s) and / or medical condition(s), are a characteristic of the individual providing a sample and which were collected at the time of sample collection. Data on donor and health attributes can be collected before, during, and / or after the time of collection and sent alongside the biological samples (102).

[0149] Method (113) is the sample characteristics, i.e., sample type, sample treatment, etc. for rule-based model selection.

[0150] A first subset (104a) of aliquots of each sample were subjected (106) to simulated postage conditions including static and / or dynamic temperature exposure and agitation. As used herein, an environmental attribute (105) is an external force applied to the sample while in transit, including but not limited to time, temperature, vibration, pressure, light, etc. Data on environmental attributes can be captured during transit by accompanying each sample with sensors and / or data loggers included with the postage packaging, or can be obtained by a proxy, as provided in some embodiments. Data from the environmental sensor is also used in the rule-based model selection based on length of transit time.

[0151] One or more data loggers were placed in close proximity to the biological samples and accompanied the samples during transit to the test facility to monitor the environmental conditions experienced by the biological sample during transit. Here, “environmental conditions” means oneof time and / or temperature that the biological sample is subjected to since collection. The one or more data loggers includes one or more of:(i) a temperature sensor for detection of temperature fluctuations of the sample during transit; and(ii) an accelerometer for detection of impact / vibrations or kinetic shocks experienced by the package and the sample during transit since kinetic agitation of the sample can accelerate the lysis rate of the blood cells.

[0152] Additional sensor equipment that may be used to accompany the test samples during transit include: a light sensitivity sensor to monitor any potential exposure of the sample to light, and / or a geolocation sensor to track the sample during transit for extrapolating environmental conditions from weather data such as local temperatures experienced by the package. Such sensors may be configured within a single device or multiple devices.

[0153] Degradation attribute (107) is an internal sample metric, for example if the biological sample is blood, it might include metrics such as evaporation, cell lysis, plasma hemoglobin, plasma DNA content, or a proxy for degradation as described herein, which indicates to what extent a sample has degraded during transit. Other biological samples will have other degradation metrics based on the sample type, such as pH, specific gravity and nitrites for urine. Data on degradation attributes (107) are enumerated from the sample upon receipt in the laboratory. Alternatively, models that predict degradation from environmental conditions can be used in place of direct quantification of degradation attributes from the sample.

[0154] Environmental attributes (105) are known to affect how biological samples change during transit. Here, it was investigated whether sample type, sample treatment, and / or environmental attributes (105) (for example, agitation, time, temperature, light exposure, and / or evaporation) were significant covariates of analyte change to establish one or more stability model’s functions. It was also investigated whether degradation attributes (107) (for example, cell lysis, plasma hemoglobin, plasma DNA content, and / or degradation prediction) and / or and donor and health attributes (103) (for example, sex, age, ethnicity, and BMI, medical condition[s], and / or medication[s])were also significant covariates of analyte change to establish one or more stability model’s functions.

[0155] A measurement (108) of a plurality of analyte characteristics is then made for the degraded samples and the samples are then subjected to plasma extraction (110). Notably, samples may be in transit in one form, and then an analyte measured from a portion of the same, such aswhole blood in transit, and then plasma, after being separated by the lab, which is used to measure a desired analyte. A second subset (104b) was not subjected to the degradation from environmental attributes (105) or degradation attributes (107) but rather passed directly to the sample preparation step (serum extraction, using blood as an example) (110). Analyte measurements of the direct samples (104b) are considered to be the true value (109) of the analyte characteristic for each analyte measured. Analyte measurements of the degraded sample subset (104a) are considered to be the test value (111) of the analyte characteristic for each analyte measured. The test values (111) for each analyte measured are compared (112) to the analyte measurement from the true values (109) to determine the relative difference between the true and test values to construct a regression line capable of predicting a correction factor in order to create a training set (140) to be able to train (114) a stability model (150) to predict the relative change of the analyte measurement with respect to the sample type, sample treatment, environmental (105), donor attributes (103) and / or degradation (107) attributes.

[0156] Thus, a data training set, being created, allows for a model to then be trained to determine shift in a desired analyte from a given biological sample, with increasing accuracy. Moreover, a capture of Sample attributes will inform rule-based model selection. Indeed, the increased accuracy allows for significant extensions of time in the sample processing latency that was heretofore impossible. Such significant latency extension allows for a completely new class of testing, namely remote self-collection (or other collection) testing that can be provided without special handling requirements, such as overnight, temperature controlled, frozen samples, separating mechanisms, or specialized devices to aid in separating or stabilizing the biological sample.Sample Type-Specific Effects

[0157] Clinical testing is performed on an extensive list of sample types, including but not limited to, blood, plasma, serum, urine and saliva. Each sample type presents a unique challenge when transported in an uncontrolled environment, with varying susceptibilities to degradation and exposure to potential contaminants that accelerate degradation causing erroneous test results. As mentioned, shipping of blood samples in particular require care as blood cells naturally degrade once extracted from the body in a process known as blood lysis (haemolysis) whereby red blood cells rupture and their contents (cytoplasm) leak out into the surrounding fluid (e.g., blood plasma). Urine samples are particularly susceptible to microbial contamination as a result of improper collection that can dramatically impact pH and specific gravity that alter the rates of sample degradation. Similarly, the degradation of saliva samples can be impacted by microbial content,but also the patient’s last meal relative to collection and even time of collection through circadian rhythms. There remains a significant need for home biological sampling and for low cost shipping methods of self-collected biological samples which accounts for differences in sample types.

[0158] When there is an extended time delay between collection and analysis of a biological sample, like that for remote sample collection during transit by mail, the degraded value enumerated by the analyzer from the degraded sample begins to deviate from the True Value from an undegraded sample at time zero (To). This relative change in relation to the true value poses a significant clinical risk due to the increasing likelihood of a misdiagnosis. Unexpectedly, the degree of change between the true and degraded value can differ substantially between sample types, even for the same test analyte. FIGS. 2A and 2B show that there is almost no consistency of the relative change in the degraded value in six different common sample types in plasma, precentrifuged plasma, serum, precentrifuged serum, dried blood spot, urine, and saliva, despite this being for the same analyte in urea. Notably, the above refer to the sample type, during analysis, while plasma and serum are actually transported as whole blood, before being processed by the laboratory into the serum or plasma for analysis. These changes are in fact so different that if we model the relative change in plasma to form a stability model and use this to correct test results from sample types other than plasma, we see highly inaccurate test results, in some circumstances worse than uncorrected test results (FIG. 2C). Conversely, training a stability model trained using data from the specific sample type, we see an enormous improvement in test accuracy, with an average % test accuracy of 122.6%, compared to (16.3% for a sample type nonspecific model, a 280.6% improvement. Therefore, a sample stability model must be flexible to and account for sample type to accurately correct test results from at-home collected samples.Sample Treatment-Specific Effects — Anticoagulants

[0159] Biological samples are treated with many different physical and chemical agents, primarily to maintain a physical or biochemical attribute of the sample for testing purposes. For example, blood is routinely treated with an anticoagulant during collection to ensure the blood is free-flowing without clotting in order to perform full blood counts of blood cells. Alternatively, a blood sample is routinely treated with a clot activator to ensure the uniform and rapid clotting of blood, to aid sample processing and to remove excess proteins that interfere with the measurement of some test analytes. These sample treatments come in many different types, chosen to ensure the accuracy of the required test. However, each of these treatments affects the rate of sample degradation differently. FIG. 3A shows the discrepancy in relative change in the creatinine test results as a consequence of using different anticoagulants (Lithium heparin and EDTA). DespiteCreatinine not being involved in the coagulation process, and comparable healthy ranges between the sample type, the difference in the rate and magnitude of the degradation kinetics is significant and unexpected. Using a stability model trained on samples treated with Lithium heparin to correct test results of samples treated with EDTA we see highly inaccurate test results and worse than uncorrected test results in all circumstances (FIG. 3B). Conversely, training a stability model using data from sample treatment, we see an enormous improvement in test accuracy by using a specific solution, instead of a nonspecific or uncorrected solution. Using the Lithium Heparin model to test samples treated with Lithium or EDTA. Of course, the Lithium Heparin model will then be equivalent for the specific and nonspecific results. However, when comparing the accuracy for EDTA, the changes are significant, with the specific model being improved by (165%. Therefore, a sample stability model must be flexible to and account for sample treatment to accurately correct test results from at-home collected samples. Selection of a model based on the additive thus is an important consideration in obtaining optimized corrected values of a degraded biological sample.Sample Treatment-Specific Effects — Chemical Stabilization and Preservation Methods

[0160] Chemical agents are also used to treat biological samples to inhibit potential contaminants that accelerate the degradation process. For example, urine and saliva are routinely treated with acids (hydrochloric acid [HC1]) and surfactants (sodium dodecyl sulfate [SDS]) as part of standard clinical practice to prevent the growth of microbes that can quickly render a sample useless for clinical interpretation. FIGS. 4A and 4B show the discrepancy in relative change for urea in urine as a consequence of using different preservatives (HC1 and SDS) in comparison to no preservatives. Using a stability model trained on samples with no preservatives to correct test results of samples treated with preservatives, we see highly inaccurate test results and in some cases worse than uncorrected test results for both urine (FIG. 4A) and saliva (FIGS. 5A and 5B). In FIG. 4B, training a stability model using data on sample preservation, we see an enormous improvement in test accuracy, with an average % test accuracy of 59.9.3%, compared to -54.9% for a sample preservation nonspecific model. And in FIG. 5B, similar results are depicted for urea within saliva samples. Therefore, a sample stability model must be flexible to and account for sample preservation to accurately correct test results from at-home collected samples among different sample types.

[0161] The existing models for sample collection focus on two primary goals, speed or preservation. When we focus on speed, a sample collected and then analyzed within an hour or less, typically has very little shift in desired analytes to be measured. However, when the latencybetween the collection time and analyzing is more than a few hours, more than 12 hours, more than 24 hours, more than 36 or 48 hours, many samples become unstable with regard to analytes to measure and thus cannot be reliably used. Accordingly, the next strategy is frequently to stabilize the material. However, even such stabilization strategies remain lacking, and are not effective for all biological samples or for all analytes in a given sample. The present embodiments prepare samples to be analyzed after they are degraded, thus extending the duration of latency between donating the sample and the analysis of the sample and dramatically reducing the need to discard samples.

[0162] The addition of preservatives to a sample prior to transport alone substantially improves the test stability by comparison to a sample transported with no preservatives (FIGS. 4A, 4B, 5A, and 5B). However, application of a stability model to correct test results, even for a relatively stable sample treated with preservatives, can further improve test accuracy performance by up to 186.1% (FIG. 6B). Moreover, this increase in test accuracy enables a sample type like plasma to maintain a diagnostic quality of test performance up to 8 days, compared to only 2 days for uncorrected.

[0163] FIGS. 7 A and 7B further detail certain stabilizers, including sodium citrate, RPMI, EDTA, and a combination of PRMI, HEPES and 10% FICO70. These data are depicted in FIGS. 7A and 7B, showing that when using the specific model for a given solution, the specific solution dramatically improves the accuracy of the corrected values.Physical Stabilization and Purification Methods

[0164] Physical stabilization of a sample involves the use of a medium that prolongs the stability of a sample, purifying a sample to remove potential contaminants that accelerate the degradation process and / or use of environmental control that aims to minimize the impact of the environment during sample transit.

[0165] Purifying a whole blood sample into a more stable physical form, such as plasma or serum, removed of contaminants (hematocrit), can also be achieved with use of a portable centrifuges or microfluidics devices and are being trialed in the use of at-home sample collection. Microfluidic devices consist of intricate networks of microchannels, often etched or fabricated on a chip-like substrate (typically made from materials like glass or polymers). These channels are designed at a scale of micrometers, allowing precise control over the flow of blood components. Blood components can be separated based on their size using microfluidic channels. Larger components such as cells (red blood cells, white blood cells) and platelets can be separated fromsmaller components like plasma proteins and metabolites. Differences in density can also be exploited. For example, plasma can be separated from cells by allowing them to settle or by using gradients of density in microfluidic chambers. Microfluidic devices may incorporate filters or membranes with specific pore sizes to selectively allow certain components to pass through while retaining others. Electric fields can be applied within the microchannels to manipulate charged particles or cells, enabling their separation based on electrophoretic mobility or dielectrophoresis. We can replicate the effect of a microfluidics device or a portable centrifuge in the at-home setting by fractionating a whole blood sample into plasma before a sample is transported. Plasma extracted immediately after collection and prior to transport (term, precentrifuged serum or precentrifuged plasma) substantially improves the test stability. This preprocessing is more stable than a sample transported as whole blood and extracted upon receipt by the laboratory. However, application of a stability model to correct test results, even for a relatively stable sample in precentrifuged plasma, can further improve test accuracy performance by up to 237.4% (FIG. 8A and 8B). Moreover, this increase in test accuracy enables a sample type like precentrifuged plasma to maintain a diagnostic quality of test performance up to 8 days, compared to only 2 days for uncorrected.

[0166] Serum solutions show a similar improvement to the plasma, in FIGS. 8C and 8D. As the exposure to contaminants that affect test accuracy are vastly different during transport of whole blood versus transport of pre-processed serum / plasma, inclusion of sample type, additional gains are made in the accuracy of values to a true value.

[0167] Dried blood spots (DBS) are used to physically preserve a sample in transit and are used for application including at-home sample collection. DBS are created by applying a small amount of blood onto a specialized card or paper, allowing it to dry completely. The dried blood spot can then be stored for an extended period of time, making it a convenient and efficient way to collect and transport blood samples for various types of testing, including diagnostics, research, and monitoring. This method stabilizes the sample by:(i) immobilization that reduces enzymatic and chemical degradation, preserving the integrity of the sample over time;(ii) protection from exposure to light and oxygen, which can accelerate degradation of certain analytes in liquid blood samples; and(iii) minimization of contamination as they are less likely to leak or spill during handling and transport.

[0168] We can replicate the effect of a DBS in the at-home setting blotting a whole blood sample onto filter paper prior to transport. Drying and immobilization of blood by DBS prior to transport substantially improves the test stability by comparison to a plasma extracted from whole blood upon receipt by the laboratory. However, application of a stability model to correct test results, even for a relatively stable sample in DBS, can further improve test accuracy performance as depicted in FIGS. 8E and 8F).

[0169] Urine samples also benefit from knowledge of the sample type, as compared to a generic model, with modest improvements seen in FIGS. 8E and 8F.

[0170] Further samples, such as those testing for calcium, are further improved by knowledge of the type of treatment, regardless of the treatment type. FIGS. 9A and 9B depict models that are utilized when EDTA or Lithium Heparin are used in treating the blood sample, with or without consideration of the specific treatment. Notably, as depicted in FIG. 9B, the results are nearly 600 and 900% improved with regard to accuracy of the given analyte test.

[0171] FIGS. 10A and 10B consider MCP1 analyte, when considering EDTA or no EDTA in the sample. Notably, the changes are over 5,000% increase in their accuracy. FIGS. 11A and 11B use different additives in the sample consideration, but again yield dramatic improvements in accuracy when considering the additive in the selected model.

[0172] FIGS. 12A and 12B consider certain additional variables such as nitrite, pH, and / or specific gravity within the given samples. Notably, as previously detailed, urine has certain susceptibility to degradation based upon the risk of growth of bacteria that is different from blood or saliva. Thus, different factors may be considered in selecting models based upon urine as the sample type in transit, which thus impacts the downstream ability to correct the desired analyte, here, glucose, from a degraded sample.Environmental Control Methods

[0173] Environmental control of sample transport is also commonplace for at-home collected samples. This method aims to minimize the impact of the environment of the sample during transit. Insulated packaging is one of the simplest methods to control temperature during shipping. It typically involves using materials such as expanded polystyrene (Styrofoam), polyurethane foam, or vacuum-insulated panels to provide thermal insulation. These materials help to maintain the temperature inside the package by reducing heat transfer with the external environment. Cold packs or gel packs are commonly used in conjunction with insulated packaging. These packs areprecooled and placed inside the shipping container alongside the blood samples. They absorb heat from the environment, helping to keep the samples cool during transit. Specialized thermal shipping containers are designed to maintain a specific temperature range for an extended period. These containers often incorporate phase-change materials (PCMs) or dry ice to regulate temperature. PCMs absorb or release heat as they change phase (e.g., solid to liquid), providing consistent cooling within the container. Temperature data loggers are devices that monitor and record temperature variations during shipping. They are placed inside the shipping container or package with the blood samples. Data loggers can provide a detailed temperature history, allowing recipients to verify if the samples have been kept within the required temperature range throughout transit. Some advanced shipping systems use active temperature control devices, such as electronic coolers or heaters. These devices actively regulate the internal temperature of the shipping container. They are often powered by batteries and can maintain precise temperature settings, which is critical for transporting temperature-sensitive blood samples. We can replicate the effect of temperature moderators by incubating a biological sample at a fixed temperature during transport. Maintaining a sample in an optimal temperature window during transport substantially improves the test stability compared to samples transported in an uncontrolled environment. However, application of a stability model to correct test results, even for a relatively stable sample stored under a constant optimal temperature, can further improve test accuracy performance by up to 409.8% (data not shown). Moreover, this increase in test accuracy enables certain sample types to maintain a diagnostic quality of test performance up to 8 days, compared to only 0 days for uncorrected for many analytes from different biological samples.Point-Of-Care Testing Methods

[0174] POCT minimizes the need for sample transport over long distances to centralized laboratories, as samples can be analyzed on equipment kept by small clinics and laboratories found locally. POCT allows for immediate testing at or near the site of patient care, providing results within minutes to hours. This immediacy reduces the risk of sample degradation during transport, which can affect the accuracy of test results. Preanalytical errors, such as improper sample handling, labeling errors, or delays in processing, are minimized with POCT because testing occurs directly where the sample is collected. This reduces the likelihood of errors that can compromise sample integrity and result accuracy. Quick availability of test results through POCT enables healthcare providers to make timely clinical decisions. This is critical in emergency situations, acute care settings, or when managing conditions that require immediate treatment adjustments. POCT facilitates more efficient patient management by enabling rapid diagnosis and treatmentinitiation. For example, in infectious disease management, early identification through POCT can lead to timely isolation measures and appropriate antibiotic therapy. POCT is particularly beneficial in remote or underserved areas where access to centralized laboratories may be limited or where transportation logistics are challenging. Testing at the point of care reduces reliance on transportation infrastructure and can improve healthcare delivery in resource-limited settings. POCT results can be quickly communicated to healthcare providers, including specialists via telemedicine platforms, facilitating remote consultation and management. This integration supports collaborative care and reduces the need for physical sample transport. POCT can enhance patient satisfaction by providing faster results and reducing the need for multiple visits or prolonged waiting times associated with centralized laboratory testing. We can replicate POCT by analyzing samples using a POCT device commonly used in clinics and small laboratories (FIG. 7). Application of a stability model to correct test results obtained from a POCT device improved test accuracy performance by up to 229.3% compared to uncorrected test results. Moreover, this increase in test accuracy enables certain sample types to maintain a diagnostic quality of test performance up to 4.7 days, compared to only about 0 days for uncorrected.

[0175] In sum, the data shows the power of selecting models based upon the sample type and the sample treatment. Thus, when samples are prepared with degradation in mind, and models are created based upon quantifying a desired analyte from that degraded sample.Implementation of Invention

[0176] The collection of a biological sample under the embodiments herein is intended to allow for quantification of an analyte from a degraded sample. However, when a biological sample is not taken within a laboratory setting, and processed contemporaneously, degradation will occur. This leads to the value of the analyte diverging from the true value, whether that is up or down. However, as evidenced by the data in the present figures, the value can be dramatically altered by the inclusion of certain additives. For many samples, the given additive may be prescribed by rules or standards for taking of the given analyte, so that the additive does not adversely react with the biological sample to confuse the sampling process. However, in some instances, different additives may be utilized for the same sample, and for the same desired analyte to be tested, yet will dramatically alter the degraded value of the sample. Therefore, even after a few hours, absent information related to the preparation of a sample, a degraded sample cannot meet the standards set to be analyzed and thus must be discarded.

[0177] Thus, preferred embodiments of the present disclosures contemplate methods and systems for preparing a biological sample for measuring of a desired analyte after the sample is degraded. Indeed, we are aware that when a patient is capturing a biological sample remote from a testing laboratory that the sample will have latency between its capture and its testing. Thus, we acknowledge that the sample, when it is analyzed, will inherently be degraded as compared to if it was tested contemporaneously with its collection. Thus, in certain embodiments, the methods herein relate to preparation of the biological sample to be analyzed in its subsequent degraded state. By specifically acknowledging that the sample will be in transit, that it will undergo some degradation in transit, as time, temperature, as well as the selected additive impact the degradation, we can prepare said sample to be optimized for its quantification for a desired analyte in the subsequent degraded state.

[0178] We have noted that prior models are ineffective for their ability to quantify a metabolite from a degraded sample, instead these prior solutions merely identify if the sample can be analyzed for a given analyte, such that the quantified result is within acceptable limits of error. These prior models, however, provide no solutions as to how to prepare the sample itself for actual quantification of the sample after it has been degraded. Specifically, the methods herein allow for a dramatic increase in the latency between collection and the analysis of the biological sample for a given analyte, as compared to any prior solution.

[0179] Preparation of a given biological sample is critical for its subsequent analysis for a given analyte. As detailed in FIGS. 3A, 3B, 4A, 4B, 5A, and 5B, several simple examples are depicted as they related to blood, urine, and saliva samples with each using different preservatives. In each of the figures, it is noted that selection of both of the sample types and also of the additive into the sample, the corrected results are dramatically different. Thus, preparation of each sample to be tested is not merely to order a test and provide a sample, but also to identify the sample type, and further to identify the additive to be included within the sample for each of a given analyte to be measured from the given sample, so as to pick an appropriate model for correction of the degraded value of the given analyte. Additional figures, such as those of FIGS. 6A-12B further support the conclusions about the variability of sample type, when using a different additive and then selecting a model for correcting for degradation under those specific conditions that degrade the given sample.

[0180] Thus, the methodology for sample preparation can be evaluated in view of the flow diagram of FIG. 14. The preparation steps are primarily in step 1402), in which a practitioner orders a given sample to be provided by the patient. Thus, data of the sample to be transportedincludes at least the test type, corresponding to the desired analyte to be tested, the type of sample, such as blood, urine, saliva that is transported, the treatment (such as an additive, preservative, or other mechanical processing). These features are essential to preparation of the sample to be quantified in a subsequent degraded state. Additional information that may be used may include donor information and certain degradation factors.

[0181] Once the sample is collected, the preparation is not yet complete, because it is then essential to capture, directly or by proxy, certain environmental conditions that impact the degradation of the sample. Primarily, these are the time of transit and the temperature of the transit, as is discussed herein in the various embodiments. The sample is then transported and data collected during transportation. Upon receipt of the transported sample, the collection of data from included hardware, or captured by proxy, becomes the complete data set necessary to process the sample upon receipt at a laboratory, where the sample will then be in its degraded state and ready for analysis based on its now known degraded profile because of the preparation and models detailed in the present embodiments.

[0182] Continuing in FIG. 14, a stability model can then be selected, wherein the data 1402) can be input into the selected models (1406) or 1414) and the model executed to capture a correction factor 1410). The degraded sample is then processed to obtain a degraded value, which is then applied to the correction factor (1418) to yield the corrected value (1420). Thus, when a sample is desired, a collection kit and required tests can be ordered independently by the patient or by a general practitioner (“GP”) or another health care professional, after an in-person, telehealth consultation or provided as part of a screening program and delivered to the patient’s home by registered mail or courier. The collection kit may be a listed medical device that contains all of the consumables and instructions necessary for a patient, caregiver, phlebotomist, or the like to collect a biological sample. The sample is labeled with information such as the patient’s name, date of birth, time of sample collection, and / or any other information as needed / desired. This can be provided directly, or with a code that accesses the relevant health records or data of the patient. Such information can then be uploaded into a database and / or added within the relevant health record to allow for access to the information relevant to the given test. The sample is packaged and sent to a partnered pathology laboratory such as by mail. In certain embodiments, the return package contains an active sensor that records environmental conditions during the sample transit. For example, a sensor / data logger can record time and / or corresponding temperature at regular intervals. Intervals can be every minute, every 5 minutes, 10 minutes, 20 minutes, etc. over the course of less than one day to a week or more.

[0183] Upon receipt of the sample by the laboratory, the sensor / data logger data is securely uploaded to the cloud automatically using a geofence and radio-frequency identification (RFID) or near-field communication (NFC) or by manual downloading of the data from the sensor / data logger. If the return package does not utilize an active sensor, the environmental conditions are obtained by proxy, by capturing the environmental temperatures manually or via an API or via another accessible database. In this way, the conditions of the sample are known based on the dynamic nature of the environmental conditions to which the sample is subjected. Further, if not already uploaded to the cloud, donor information / attributes may be input and associated with the received biological sample. Thus, data associated with a sample to be analyzed relates to one or more of: sample type (e.g., blood, urine, saliva, etc.), test type (e.g., one or more analytes to be measured), environmental attributes (e.g., transit time, temperature data), donor demographics (e.g., sex, age, ethnicity), donor health (e.g., BMI, medical conditions, medications taking), and sample degradation attributes (e.g., indicators of how much a sample has degraded since collection, if applicable, and provided after receipt of the sample at the laboratory). The laboratory may analyze the biological sample directly or further process the biological sample for analysis on standard (or other) pathology analyzers to obtain measurement values for one or more analytes. The laboratory requests correction of the value obtained by analysis (i.e., “degraded values”) as a stability model routine or by API or using the software integrated with the laboratory equipment or laboratory information management system. Applicant takes the degraded value(s) and provides the correction factor(s) and / or the corrected value(s) to the laboratory such as via a stability model routine or the laboratory’s information management system using standardized transfer for clinical and administrative health data and onwards to the referring doctor for a follow up consultation.

[0184] FIG. 14 illustrates a process (1400) that may be utilized to provide one or more correction factors and / or corrected values to a requesting laboratory, physician, or the like. In practice, some or all of the steps may be utilized by a particular embodiment of the process (1400). An overall objective of the process (1400) is to enable use of the degraded biological sample even though it may otherwise be considered to be “expired” and would typically be discarded. As such, process (1400), or a portion thereof, is typically employed when the biological sample has been degraded, and, but for process (1400), the sample would not be accepted for analyte measurement. Some or all of the process (1400) may also be employed in cases where a sample is still acceptable, but has additional error attributed to minor degradation.

[0185] In the past, samples may have degraded to a point in which they have “expired”, meaning that laboratories would not use them for analyte analysis. Expiration dates for various types of samples / analytes are not necessarily uniform and may be set by a regulation or other authorities such as the College of American Pathologists (CAP) and / or Clinical Laboratory Improvements Amendments (CLIA) in the US. Even so, each laboratory may also have its own set of criteria for sample type / analyte expiration, especially if a test is developed by the lab. Thus, criteria for when a particular sample has heretofore become unusable can differ from country to country, lab to lab, and / or test to test. Nevertheless, biological sample degradation is a fact, and as is shown herein degradation can vary by sample type (e.g., urine, saliva, blood, etc.) and by sample treatment (e.g., preservative, stabilizer, buffer, inhibitor, etc.). Environmental factors such as transport time and / or temperatures and / or donor attributes such as demographics and / or health may each affect biological sample degradation differently. As such, using processes that were the same as or similar to the process (100) of FIG. 1, Applicant developed a plurality of stability models where each model was developed to provide a correction factor to correct an analyte that was measured in a degraded / expired biological sample. Because these models were trained according to different datasets, the best results will be obtained utilizing a stability model that has been trained to “best fit” the sample type and / or sample treatment. For example, as is shown in FIG. 2A a model trained to correct a specific analyte (e.g., urea) can also be trained to provide a correction factor that is specific to the sample type (e.g., plasma, serum, urine, etc.). Stability models that are trained according to at least a particular sample type typically predict a correction factor with increased accuracy, as is shown in FIG. 2C. As each stability model is trained to predict a correction factor according to a specific set of conditions, process (1400) can begin with analysis of the data associated with a given biological sample.

[0186] Data associated with a biological sample (1402) may include at least one or more test types such as for one or more analytes in the biological sample. Data associated with the biological sample may also include the type of sample that is being processed such as whole blood, DBS, plasma, serum, urine, and saliva, without limitation. At a minimum, data will also include environmental factors, which include at least a transit time and temperature, which may have been obtained via the activation, deactivation or reading of the sensor / datalogger. Transit time may also be inferred by other information such as time of sample collection determined by the patient minus the time of sample receipt by the laboratory or time stamp logs from logistics providers scanning the sample packaging during collection, transit and delivery. Additional data associated with the biological sample may include data relating to transit environmental conditions, donor demographics and / or health, and sample degradation indications. In many cases a biologicalsample may be treated with a chemical / biological treatment to increase sample stability during transit. Thus, data relating to a sample may also include the type of treatment. Herein, stability models have been developed for samples having different treatment types. For example, whole blood samples may be treated with EDTA (ethylenediaminetetraacetic acid, a preservative / anticoagulant), EDTA plus one or more enzyme inhibitors, lithium heparin (an anticoagulant), or the like, and then processed in the laboratory into the plasma for analysis. Other data may also be needed for a particular analyte test / stability model and included with the data set (1402) associated with the sample.

[0187] Using data from the data set (1402), the process (1400) may determine which stability model is the best suited to give the most accurate and precise correction factor outcome. Applicant has devised at least two alternative approaches to model data, hence selecting a stability model according to the different approaches. However, Applicant appreciates that blending components of the two approaches will also yield equivalent results and as such model selection rules could be modified accordingly. A set of “decision-tree” based rules may be used to select a stability model that has been trained according to one approach (i.e., “Approach A”), which is shown on the left side of FIG. 14, and “variable encoding” based rules, in which the rules are independent variables in the model, may be used to select a stability model that has been trained according to an alternative approach (i.e., “Approach B”), which is shown on the right side of FIG. 14. An illustrative set of decision-tree-based rules is given in Table 1.Table 1:Illustrative Rules for Stability Model Selection (“Approach A ”)

[0188] A simplified example of stability model selection via a computer implemented method includes obtaining a first set of rules that define stability model selection based on at least analyte / test type and sample type. The rules may be obtained automatically from local or remotestorage such as upon computer and / or application start up. The first set of rules, such as the examples in Table 1, may also define stability model selection based on sample treatment. The stability model selection process (1404) may use data in the data set (1402) relating to test / analyte type, sample type, and sample treatment, if any, to select a particular stability model that has been trained for the indicated parameters. For example, if the selection process (1404) applies the rules of Table 1 to the data set (1402) provided in Table 2, the decisions regarding stability model selection may look something like the output shown in Table 3.Table 2:Illustrative Data Set to be Analyzed by Rules of Table 1

[0189] Per this exemplary set of rules and data, the stability model selected for execution is one that has been trained to predict a correction factor for cholesterol using data from a degraded serum sample treated with SST. It should be understood that if a treatment type had not been specified, and a stability model had been trained to predict a correction factor for a cholesterol test analyzed in a degraded serum sample in which a treatment type was not specified or lacking, then the selection process would have selected the stability model trained for predicting Cholesterol correction in a serum sample with unspecified / no treatment. As we have shown in at least FIGS. 3 A-7B, the accuracy of correction factor prediction is increased when both sample type and sample treatment are modeled.Table 3:Illustrative Results of Applying the Rules of Table 1 to the data of Table 2

[0190] The process (1400), having selected an appropriate stability model (1406), may then execute the selected stability model (1408) utilizing at least the time data in the data set (1402) (Table 2). For example, referring to the model in FIG. 2A for serum and using cholesterol as the test / analyte to be corrected instead of urea, the training data set would correspond to relative difference / change between test values and true values in view of time. Using the stability model, a correction factor (i.e., relative difference) can be predicted. Whilst a regression equation is provided as an illustrative example, models are not only linear models, A simplified regression may result in an equation such as correction factor = 1 + 0.179><time. Plugging in the data of Table 2, our predicted correction factor (1410) is 1.1 = 1 + 0.179x0.56. In this equation time is the only variable as the training dataset was specific to the type of analyte and the type of sample. If a stability model had been trained for a specific test, sample type and treatment type, then the only variable in the stability model would be time. However, if all input data remained the same, i.e., Analyte = Cholesterol and Time = 0.56, but the sample type (plasma) and treatment (EDTA) were changed, the aforementioned decision-tree rules would select a different stability model, fit to Plasma / EDTA samples, and the correction factor would likely change. The importance of this rule-based system is exemplified in various foregoing figures where Applicant has shown that model training according to analyte, sample, and / or sample treatment per variables such as time, temperature, etc. results in models that are more accurate and precise compared to models that have not been trained to be specific sample type and / or treatment. In fact, as is shown in FIG. 13, when sample treatment and sample type are both utilized in model training the accuracy of the stability model greatly improves as compared to using only the environmental factors. See also FIGS. 2B, 3B, 4B, 5B, 6B, and 7B.

[0191] An alternative way to achieve a similar outcome in which data relating to the biological sample may be utilized to select an appropriate stability model is via encoding binary variables. In this case, sample rules may be the same as or similar to those of Table 4.Table 4:Illustrative Rules for Stability Model Selection Wherein Available Sample Types are Serum and Plasma and the Analyte is Cholesterol (“Approach B”)

[0192] According to this type of stability modeling an exemplary equation to predict a correction factor may be:Correction factor= 0.856 + 0.178 x time + 0.317 x plasma + 0.254 x serumH — 0.01 x cholesterol where the y-intercept and coefficients are determined during model training. Plugging the sample related data (Table 2) into the rules (Table 4), the resultant outcome / stability model selection for this example is:1.1 = 0.856 + 0.178 x 0.56 + 0.317 X 0 + 0.254 X 1 + -0.01 x 1

[0193] Note that if a binary variable is encoded with “0” the effect on the output (correction factor) is nullified. Only when the binary variable is encoded with “1” does the correction factor change. In the above example, the sample type is “Serum” and encoded with “1”, where the 0.254* 1 affects the correction factor. However, the sample type is not “Plasma” and encoded with “0”, therefore, the effect of the plasma variable, 0.317*0, is nullified. Additionally, in this simplified example binary encoding was demonstrated with respect to sample type; but binary encoding may be utilized to encode multiple variables and their associated subsets. For example, where a model is trained for different treatment types, each type of treatment may be specified in a model equation where binary encoding (i.e., “0” or a “1”) would indicate the presence (“1”) or the absence (“0”) of the particular treatment type.

[0194] Thus, regardless of how the stability models are trained (e.g., tree-based or variable-based) or if components of Approach A and Approach B are blended, the predicted relative difference / correction factor (1410) should be the same, substantially the same, or equivalent.

[0195] The type of rule (i.e., decision-tree, variable encoding, a blend) and how they are written can be indicated for a number of different criteria, functions, and mathematical expression. With a blended rule, a first set of rules may include decision-tree based, such as for a specific analyte and sample type. A second set of rules may be of the binary / variable encoding type for sample treatment, where each type of treatment being considered (e.g., plasma treated with EDTA, EDTA plus one or more enzyme inhibitors, and lithium heparin) is encoded as a variable having a value of either “0” or “1”. Thus, rules can be based on a decision tree type approach, a variable / binary encoding based approach, or a combination of the two. Further, the equation utilized for predicting a correction factor will include the number and type of variables appropriate for the selectedstability model. Table 5 illustrates several criteria that may be utilized in stability model selection. In practice, the criteria may be utilized to select a particularly trained model, encode a variable with a binary value, or a combination of the two. Thus, even though the criteria and functions shown in Table 5 are written according to one approach, they can be converted to the other type of approach, or a blended approach.Table 5:Examples of Additional Rules that may be Utilized for Stability Model Selection as Either Part of a First Set of Rules or a Second Set of Rules

[0196] The types of rules shown in Table 5 can be part of a first set of rules or a second set of rules. For example, if part of a first set of rules, the particular criteria and function can be added to the rule set of Table 1 as an additional or alternative criteria. If part of a second rule set, a given criteria and corresponding function could be utilized to narrow an initially selected stability model where a correction factor could be provided according to an initially selected stability model and / or a more specific stability model. Narrowing of a stability model may be via a decision tree for a specific model type, a decision tree for an initial stability model followed by binary encoded variables, or by various degrees of variables with binary encoding. It should be appreciated that prediction equations will typically be trained according to one or more independent variables, such as time, temperature, other environmental parameters (e.g., agitation, pH, light exposure, impact, etc.), donor attribute(s), health attribute(s), sample degradation attribute(s), etc. The exact number, type, and encoding of independent variables depending on what approach was used to train the model (i.e., “A”, “B”, both).

[0197] Thus, the outcome of stability model selection is a stability model that is best suited for the data relating to the biological sample being utilized. Where there is a minimal amount of data such as test / analyte type, sample type, and transit time, the best stability model for predicting a correction factor for this data set is selected, regardless of the type of approach that was used to create the model. Where a data set includes a complete complement of data (e.g., test / analyte type, sample type, sample treatment, transit time, transit temperature, donor demographics, donor health, and sample degradation), the selection process will determine which stability model is best suited to predict an outcome based on the data set and / or how to encode the variables in an equation used to predict the correction factor. Where the data set is between these two examples, again, the process has the ability to determine which model and / or equations are best suited to fit the data depending upon the approach taken for training one or more stability models.

[0198] In some embodiments, attributes such as environmental, donor demographics, donor health, and / or degradation indicators, encoded as independent variables, are assigned a coefficient or “weight” during training that best minimizes model error. As one nonlimiting example, when training a model to predict a correction factor for a glucose test, independent variables (e.g., environment, donor demographic, donor health and donor degradation) are assigned a weight:Glucose = y + 65 x environmental + 20 x donordemo + 10 x donorhealth + 5 x degradation

[0199] In this example, the model has been trained for glucose correction in a particular sample type and / or treatment using environmental, donor demographics, donor health, and sample degradation attributes as variables and each variable was assigned a weight within a predetermined range of weights. Weights in these examples are proportional, adding up to 100, indicating the percent contribution of each attribute to the correction factor of Glucose, rather than the nominal coefficient. This is because the nominal coefficient is dependent on any unit, transformation or scaling of the model inputs, which cannot be standardized It would be understood that when using modeling approach “B” or a blend of modeling approach “A” and “B” independent variables may include “sample type” and / or “sample treatment” such as was explained above with respect to binary variable encoding. Further, it would also be understood that a particular variable only be included in the equation when the inclusion of a variable in the model results in a meaningful improvement in model error. Taking the glucose formula above as an example, if any one or more of “environmental”, “donordemo”, “donorhealth” or “degradation” were included as part of model training, but only important independent variables (e.g., environmental, donor demographic, donor health) are selected and assigned a weight taken from a range of weights, and insignificant variables (e.g., degradation) are unselected, assigned nor weight, and removed from the model. Then these variables could be excluded from the equation and the weight associated attributes used for training would also change. For example, if inclusion of degradation attributes had insignificant effect on model error during model training, degradation attributes would be omitted and the resulting equation may be as follows:Glucose = y + 70 x environmental + 20 x donordemo + 10 x donorhealth

[0200] In this example, the proportional weight of the environmental variable increased by five, while the weights of donordemo and donorhealth remained the same. Meaning that the environmental variable contributes 5% more to the determination of the correction factor for Glucose. This model represents a simpler model, with fewer variables than the previous example of a more complex model, with more variables, but both may give a comparable correction factor for Glucose. As is evident from the foregoing examples, proportional weights for a particular independent variable can change depending upon the number and type of attributes in models with varying complexity, which is why ranges of proportional weights have been determined. It should also be mentioned that when utilizing binary encoding, even if the variable is written into the equation, any weight given could be nullified by having a value of “0” as was described above.

[0201] Since environmental, donor demographics, donor health, and / or sample degradation can affect each different sample type and / or treatment differently, we determined a specific range ofproportional weights that indicate the contribution of environmental, demographic, health, and / or degradation attributes for each of the most common sample types and / or treatments utilized when testing is delayed, such as due to being transported. These sample types and / or treatments include: serum and pre centrifuged serum (PC serum) each collected in serum separator tubes (SST) that usually include a clotting factor and / or gel barrier; whole blood treated with EDTA and them processed into plasma for analysis, EDTA and one or more enzyme inhibitors, or lithium heparin, pre centrifuged plasma (PC plasma) treated with lithium heparin, untreated urine, urine treated with hydrochloric acid (HC1), saliva treated with either phosphate buffered saline (PBS), a variant thereof (e.g., PBST), or a combination of detergent and / or buffer with EDTA (e.g., SDS / Tris / EDTA); and a DBS wherein the blood was applied to a filter paper / Guthrie card. Since some tests can be performed on multiple different types of samples (e.g., urine, saliva, serum, plasma, etc.) whereas other types of tests are sample specific (e.g., blood only) a weight range was determined for each analyte in each sample type in which it can be tested. This was necessary as the various attributes can be weighted differently for different sample types and / or different treatment types. To try and simplify the vast amount of information that Applicant developed, weight ranges can be categorized according to the weight of environmental attributes, which is shown in Table 6.Table 6:Categories According to the Weight of Environmental Attributes

[0202] To better understand these weighting categories, a test for urea may serve as an example. Table 7 shows various sample types, sample treatments, category of environmental weight range and specific ranges for environmental, donor demographics, donor health, and / or sample degradation attributes. A urea test may be indicative of liver and / or kidney function. Many medications (prescription and over the counter) can affect the liver and / or kidneys and / as such patients taking these medications may be closely monitored to assess for organ damage and / or medication adjustment. Since many of these patients need to provide samples on a frequent basis, and they live in locations where immediate sample analysis is not possible, being able to provide samples from a remote location is highly valuable in terms of easy monitoring, but also with patient compliance and overall better health care. As discussed herein, being able to predict a correction factor specific to test / analyte type, sample type, and / or sample treatment has been shown to increase the accuracy and precision of the prediction outcome when using a degraded biological sample. Greater accuracy and precision with our modeling increases the public’s confidence in utilizing what would otherwise be an unacceptable biological sample. Part of the accuracy and precision of our modeling comes from the number of specific models that have been designed to consider a plurality of different attributes and also in part due to our categorization of environmental weighting.

[0203] As is shown in Table 7, we have designed models for sample types such as serum (whole blood in transit), pre centrifuged serum, plasma (whole blood in transit), pre centrifuged plasma, urine, saliva, and, although not shown DBS. With the exception of serum and plasma that are transported as whole blood during transit and processed into serum and plasma prior to analysis, these sample type names refer to the sample state during transit. “Pre-centrifuged serum” differs from serum in that pre-centrifuged serum is separated into serum prior to transport, whereas serum is transported as whole blood and processed to serum upon arrival at the laboratory and before analysis. As is also shown in Table 7, different sample types may have been subject to differenttreatments. Referring to environmental weighting categories, it is easy to see that the weight of environmental attributes can range from very low as with PC serum treated via SST to high / very high for urine samples, and very high for saliva samples. With this categorization of environmental weighting, it is easy to assess the relative importance of environmental attributes to correction factor prediction and where environmental attributes are not as important it is also understood that other attributes play a more significant role in predicting a correction factor. Using the ranges of Table 7, a correction factor for urea to be measured in a degraded plasma sample treated with EDTA would have weights assigned from the ranges as indicated in the following formula:Urea = y + (32 — 100) x environmental + (0 — 38) x donordemo + (0— 24) x donorhealth + (0 — 7) x degradation

[0204] The above equation represents models of decreasing complexity that can obtain a correction factor to improve test result accuracy and can be shown as:Urea = y + 32 x environmental + 38 x donordemo + 23 x donorhealth + 7 x degradationUrea = y + 3 x environmental + 38 x donordemo + 24 x donorhealth + 6 x degradationUrea = y + 66 x environmental + 22 x donordemo + 12 x donorhealthUrea = y + 100 x environmental

[0205] Where the proportional weights for environmental (32-100), donor demographic (0-38), donor health (0-24) and degradation (0-7) for models with varying complexity that can achieve a comparable performance improvement in testing accuracy and / or precision.

[0206] And a correction factor for urea to be measured in a degraded plasma sample treated with lithium heparin would have proportional weights assigned from the ranges as indicated in the following formula:Urea = y + (22 — 78) x environmental + (0 — 34) x donordemo + (0— 22) x donorhealth + (20 — 23) x degradationTable 7:Weighting Ranges for Environmental (Env.), Donor Demographics (Demo), Donor Health (Health) and Sample Degradation (Degrad. ) Attributes and Environmental Attribute Category

[0207] Thus, the proportional weighting of each of the models is significantly impacted by the type of the sample as well as the treatment of the sample, and once training is complete, the weights for a given model do not change. Using these two factors, in addition to the environmental factors, allows for dramatic improvements to modeling, as depicted in the various figures herein, that allows for degraded samples to be utilized for analysis. Indeed, these models predict and are created with the specific purpose that the samples will be degraded by the various factors, specifically as to the environmental factors and the treatment type, and that by preparing the samples with the relevant data, collecting the environmental factors during transit, and thenselecting a model based upon these factors, we can dramatically improve the quality and accuracy of quantifying the value of a given analyte from such degraded samples.Table 8:

[0208] In certain instances, the environmental factors are received by the laboratory and include, for example, the time elapsed since the collection of the degraded sample. Models can be definedto use the time component of the environmental factors as a component of model selection. Thus, if the sample is received at a total time of 52 hours, it would be appropriate to select a model that is trained in a manner that minimizes error for samples received between 48 and 60 hours, which would yield an improved correction factor, as compared to selecting a model based on time between 0 and 72 hours. Thus, when selecting a model, time, being a variable, we can select models based upon defined windows, such as 0-12 hours, 0-24 hours, 12-24 hours, 12-36 hours, 24-36 hours, 36-48 hours, 48-60 hours, 48-72 hours, etc. When such narrow windows are not available, the models may choose different time windows, such as: less than 24 hours, between 24 and 72 hours, and greater than 72 hours.

[0209] In Table 9, information on the length of the sample transport is used in the rule-based system for model selection. As an example, three models are created, one that performs best (low %bias) for samples received within 24 hours, greater than three days or even performance between 0-3 days. The performance was then determined for samples received <24 hours, 1-3 days, and >3 days. Using the rule-based system for Albumin analyte, we can see that the models that were fit to perform well at >3 days, having the most accuracy test result for samples at >3 days and least accuracy at <24 hours. Conversely, models fit to perform well at <24 hours have the most accurate results on sample delivery in <24 hours and the worst results at >3 days. The intermediate model of 1-3 days have consistent performance over the entire transit duration, but less than specific models which exemplifies the inclusion in the rule-based selection. Therefore, use of transit time in selecting the model can further improve the performance of a given model for a specific degraded sample.Table 9:Testing Accuracy (%Bias) for Albumin When Using Duration of Transit Time in Model Selection

[0210] All of the foregoing for creation of models, performing calculations on the data, and implementing the various rules and / or best fits are preferably optimized by using databases to holdthe data and then performing the calculations necessary to create the models and then using the rules and obtained data to select a model to perform the methods herein.

[0211] The system and methodologies utilized herein are conceived to be able to be processed by a computer in a local setting or, most preferably via a cloud setting. The instructions for implementing the disclosed technologies in hardware, software, or a combination of hardware and software, that is commonly used by persons of ordinary skill in the art to which this disclosure pertains.

[0212] When the solutions are utilized with databases or automatic collection of data, communication is provided directly or through at least one network to other data devices, using, for example, satellite, cellular, Wi-Fi, or Bluetooth technology, and can then appropriately upload data into a database, such as an electronic medical record pertaining to a patient. Data needed for performing some of the analysis can then be obtained from the database or electronic medical record, including information related to the desired test analyte, the desired sample type, etc.

[0213] The correction data received from client is performed using a correction coefficient based on a machine learning model trained on a dataset of test samples to provide a database of models / equation that take environmental, donor, sample type, sample treatment, and degradation attributes as inputs and which output the correction coefficient with respect to a plurality of testable analytes of the biological sample. In certain embodiments, the methods and systems can be implemented within a SaaS system (an example of which is depicted in FIG.16). In use of the SaaS system, the biological testing laboratory clients receives biological samples and associated data with respect to one or more degradation attributes experienced by the received biological samples; conducts testing of the samples of one or more analytes to record a test value for the analyte; uploads sample degraded value data and to the software application so that a correction calculation could be made. The software application would apply the relevant model to the uploaded degraded value and provide back a “corrected” value along with the original value, the correction applied, and the version of the model that was applied to the result based upon selecting the model from the given inputs.ExamplesExample 1Use of a Data Logger with a Sample

[0214] As detailed herein, the methods improve upon preventing rej ection of a biological sample, as the methods track the environmental conditions experienced by the patient sample. A patient, providing a patient sample can activate the data logger upon providing the sample and placing the patient sample and packaging, containing the data logger in the mail. Alternatively, the data logger can be started automatically or remotely, or by another mechanism. Upon receipt by the laboratory which will process the sample, the data logger can be stopped when the processing of the sample is performed, and a download of the time and temperatures from the data loggers can be subsequently uploaded into storage / database related to the given patient sample. This download can be manual, automatic, wirelessly, etc. The model is selected based upon the donor data, environmental conditions, sample treatment, and analyte of interest, and can be run by obtaining the information from the database to provide a correction factor. Specifically, a model may be selected based upon the transit time information of the environmental conditions, wherein, models selected for a narrower time window provided improved outputs as compared to more generic models. Then, after the sample is tested for the given analyte to provide a degraded value, applying the correction value to the degraded Value provides for a Corrected Value of the given analyte. Therefore, using a trained model based on and including such environmental conditions, donor data, sample treatment, and analyte data, applicant can:(i) greatly reduce the number of sample recollections required for remotely collected samples;(ii) actually adjust the degraded Value to generate a Corrected Value for a desired analyte; and(iii) singularly enable such remote biological sample collection for numerous analytes of interest by extending the sample processing latency period.Example 2Estimating Temperature for a Donor Sample

[0215] In some embodiments, the methods do not require the use of a data logger and instead rely upon databases to obtain ambient temperature information for a given location. Thus, a donor sample is obtained and posted in the mail at Location A on day one and is received for processingat a laboratory in Location B on day three. Upon receipt of the donor sample at the laboratory in Location B, information stored within the database, or on the donor sample or with donor sample packaging (e.g., a data logger), can provide an estimate of the temperature, max and min temperature and average at Location A on day one, and on Location B on day three, and estimates of the location’s temperature at all point in between, or an average of Location A and Location B, as a simple example. Using the proxy data for temperature can then be utilized with the time as the environmental attributes as detailed in the example above to process the sample and determine a correction factor via the selected model.Example 3

[0216] Similarly, a urine sample may show a pH spike, or the presence of certain blood markers within the urine that may cause degradation of the sample. Such degradation elements are necessary to identify, review and determine whether the model can sufficiently overcome such degradation aspects in a replicable manner using the models. The data uploaded to the system identifies information related to the given test type, which is a urine sample, a sample treatment is provided, such as HC1, and data from the data logger generates environmental data. The combination of data then selects a model to create a correction factor. Upon analysis of the urine sample for the desired analyte, the correction factor is applied to generate the corrected value.Example 4Reducing Rejection Rate of a Biological Sample

[0217] The preferred methods herein are directed toward reducing the number of rejected remotely collected biological samples. The primary goal of this is to enable the remote collection of biological samples, without the need for expensive and complex devices to capture the blood samples or to seek to stabilize them from the remote location. Therefore, by creation of a data training set, and training a model from said data training set, what was once a near 100% rejection of biological samples being held for more than twelve hours, allows for virtually all samples to be analyzed and to quantify analytes in such samples at two, three, four, or more days by performing an analysis of the sample to obtain a degraded Value, and then using the known data from the sample, i.e., its environmental attributes, donor attributes, degradation attributes, sample type, and sample treatment, or combinations thereof, to select the best model and use the same data to then generate a correction factor. Finally, by applying the correction factor to the degraded Value, a Corrected Value is generated that provides for a dramatic reduction in the error rate of the analyte calculation.Example 5

[0218] A biological sample is collected and a stabilizing or preserving component (additive) is added to the sample, before it is transported. The sample is prepared with the additive, and models are trained specifically with the sample type and the additive and further based upon the environmental factors. Based upon the entirety of the data, such as a time and temperature from the environmental factors, and the sample type and additive, a model is selected, and a correction factor is quantified. By applying the correction factor a quantified degraded value of the sample, a corrected value can be obtained.Implementation Example — Hardware Overview

[0219] According to one embodiment, the techniques and methods described herein are implemented by at least one computing device. The techniques may be implemented in whole or in part using a combination of at least one server computer and / or other computing devices that are coupled using a network, such as a packet data network. The computing devices may be hard-wired to perform the techniques, or may include digital electronic devices such as at least one application-specific integrated circuit (ASIC) or field programmable gate array (FPGA) that is persistently programmed to perform the techniques, or may include at least one general purpose hardware processor programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such computing devices may also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the described techniques. The computing devices may be server computers, workstations, personal computers, portable computer systems, handheld devices, mobile computing devices, wearable devices, body mounted or implantable devices, smartphones, smart appliances, internetworking devices, autonomous or semi -autonomous devices such as robots or unmanned ground or aerial vehicles, any other electronic device that incorporates hard-wired and / or program logic to implement the described techniques, one or more virtual computing machines or instances in a data centre, and / or a network of server computers and / or personal computers.

[0220] FIG. 31 is a block diagram that illustrates an example computer system with which an embodiment of the methods described herein may be implemented, for example, methods (100) and (1400) of FIGS. 1 and 14 respectively, or methods as disclosed in the above description including: generating a training set for a machine learning model; creating a machine learning model based on the difference between test values and true values of analyte measurements of a degraded biological sample; or correcting test analyte values using a machine learning module.

[0221] In the example implementation of FIG 31, a computer system (1500) and instructions for implementing the disclosed technologies in hardware, software, or a combination of hardware and software, are represented schematically, for example as boxes and circles, at the same level of detail that is commonly used by persons of ordinary skill in the art to which this disclosure pertains for communicating about computer architecture and computer systems implementations.

[0222] Computer system (1500) includes an input / output (I / O) subsystem (1502) which may include a bus and / or other communication mechanism(s) for communicating information and / or instructions between the components of the computer system (1500) over electronic signal paths. The I / O subsystem (1502) may include an I / O controller, a memory controller and at least one I / O port. The electronic signal paths are represented schematically in the drawings, for example as lines, unidirectional arrows, or bidirectional arrows.

[0223] At least one hardware processor (1504) is coupled to I / O subsystem (1502) for processing information and instructions. Hardware processor (1504) may include, for example, a general-purpose microprocessor or microcontroller and / or a special-purpose microprocessor such as an embedded system or a graphics processing unit (GPU) or a digital signal processor or ARM processor. Processor (1504) may comprise an integrated arithmetic logic unit (ALU) or may be coupled to a separate ALU.

[0224] Computer system (1500) includes one or more units of memory (1506), such as a main memory, which is coupled to I / O subsystem (1502) for electronically digitally storing data and instructions to be executed by processor (1504). Memory (1506) may include volatile memory such as various forms of random-access memory (RAM) or other dynamic storage device. Memory (1506) also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor (1504). Such instructions, when stored in non-transitory computer-readable storage media accessible to processor (1504), can render computer system (1500) into a special-purpose machine that is customized to perform the operations specified in the instructions.

[0225] Computer system (1500) further includes non-volatile memory such as read only memory (ROM) (1508) or other static storage device coupled to I / O subsystem (1502) for storing information and instructions for processor (1504). The ROM (1508) may include various forms of programmable ROM (PROM) such as erasable PROM (EPROM) or electrically erasable PROM (EEPROM). A unit of persistent storage (1510) may include various forms of non-volatile RAM (NVRAM), such as FLASH memory, or solid-state storage, magnetic disk or optical disk such asCD-ROM or DVD-ROM, and may be coupled to I / O subsystem (1502) for storing information and instructions. Storage (1510) is an example of a non-transitory computer-readable medium that may be used to store instructions and data which when executed by the processor (1504) cause performing computer-implemented methods to execute the techniques herein.

[0226] The instructions in memory (1506), ROM (1508) or storage (1510) may comprise one or more sets of instructions that are organized as modules, methods, objects, functions, routines, or calls. The instructions may be organized as one or more computer programs, operating system services, or application programs including mobile apps. The instructions may comprise an operating system and / or system software; one or more libraries to support multimedia, programming or other functions; data protocol instructions or stacks to implement TCP / IP, HTTP or other communication protocols; file format processing instructions to parse or render files coded using HTML, XML, JPEG, MPEG or PNG; user interface instructions to render or interpret commands for a graphical user interface (GUI), command-line interface or text user interface; application software such as an office suite, internet access applications, design and manufacturing applications, graphics applications, audio applications, software engineering applications, educational applications, games or miscellaneous applications. The instructions may implement a web server, web application server or web client. The instructions may be organized as a presentation layer, application layer and data storage layer such as a relational database system using structured query language (SQL) or no SQL, an object store, a graph database, a flat file system or other data storage.

[0227] Computer system (1500) may be coupled via EO subsystem (1502) to at least one output device (1512). In one embodiment, output device (1512) is a digital computer display. Examples of a display that may be used in various embodiments include a touch screen display or a light-emitting diode (LED) display or a liquid crystal display (LCD) or an e-paper display. Computer system (1500) may include other type(s) of output devices (1512), alternatively or in addition to a display device. Examples of other output devices (1512) include printers, ticket printers, plotters, projectors, sound cards or video cards, speakers, buzzers or piezoelectric devices or other audible devices, lamps or LED or LCD indicators, haptic devices, actuators or servos.

[0228] At least one input device (1514) is coupled to EO subsystem (1502) for communicating signals, data, command selections or gestures to processor (1504). Examples of input devices (1514) include touch screens, microphones, still and video digital cameras, alphanumeric and other keys, keypads, keyboards, graphics tablets, image scanners, joysticks, clocks, switches, buttons, dials, slides, and / or various types of sensors such as force sensors, motion sensors, heat sensors,accelerometers, gyroscopes, and inertial measurement unit (IMU) sensors and / or various types of transceivers such as wireless, such as cellular or Wi-Fi, radio frequency (RF) or infrared (IR) transceivers and Global Positioning System (GPS) transceivers.

[0229] Another type of input device is a control device (1516), which may perform cursor control or other automated control functions such as navigation in a graphical interface on a display screen, alternatively or in addition to input functions. Control device (1516) may be a touchpad, a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor (1504) and for controlling cursor movement on display (1512). The input device may have at least two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. Another type of input device is a wired, wireless, or optical control device such as a joystick, wand, console, steering wheel, pedal, gearshift mechanism or other type of control device. An input device (1514) may include a combination of multiple different input devices, such as a video camera and a depth sensor.

[0230] In another embodiment, computer system (1500) may comprise an internet of things (loT) device in which one or more of the output device (1512), input device (1514), and control device (1516) are omitted. Or, in such an embodiment, the input device (1514) may comprise one or more cameras, motion detectors, thermometers, microphones, seismic detectors, other sensors or detectors, measurement devices or encoders and the output device (1512) may comprise a special-purpose display such as a single-line LED or LCD display, one or more indicators, a display panel, a meter, a valve, a solenoid, an actuator or a servo.

[0231] When computer system (1500) is a mobile computing device, input device (1514) may comprise a global positioning system (GPS) receiver coupled to a GPS module that is capable of triangulating to a plurality of GPS satellites, determining and generating geo-location or position data such as latitude-longitude values for a geophysical location of the computer system (1500). Output device (1512) may include hardware, software, firmware and interfaces for generating position reporting packets, notifications, pulse or heartbeat signals, or other recurring data transmissions that specify a position of the computer system (1500), alone or in combination with other application-specific data, directed toward host (1524) or server (1530).

[0232] Computer system (1500) may implement the techniques described herein using customized hard-wired logic, at least one ASIC or FPGA, firmware and / or program instructions or logic which when loaded and used or executed in combination with the computer system causes or programs the computer system to operate as a special-purpose machine. According to oneembodiment, the techniques herein are performed by computer system (1500) in response to processor (1504) executing at least one sequence of at least one instruction contained in main memory (1506). Such instructions may be read into main memory (1506) from another storage medium, such as storage (1510). Execution of the sequences of instructions contained in main memory (1506) causes processor (1504) to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.

[0233] The term “storage media” as used herein refers to any non-transitory media that store data and / or instructions that cause a machine to operate in a specific fashion. Such storage media may comprise non-volatile media and / or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage (1510). Volatile media includes dynamic memory, such as memory (1506). Common forms of storage media include, for example, a hard disk, solid state drive, flash drive, magnetic data storage medium, any optical or physical data storage medium, memory chip, or the like.

[0234] Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise a bus of VO subsystem (1502). Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.

[0235] Various forms of media may be involved in carrying at least one sequence of at least one instruction to processor (1504) for execution. For example, the instructions may initially be carried on a magnetic disk or solid-state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a communication link such as a fiber optic or coaxial cable or telephone line using a modem. A modem or router local to computer system (1500) can receive the data on the communication link and convert the data to a format that can be read by computer system (1500). For instance, a receiver such as a radio frequency antenna or an infrared detector can receive the data carried in a wireless or optical signal and appropriate circuitry can provide the data to VO subsystem (1502) such as place the data on a bus. VO subsystem (1502) carries the data to memory (1506), from which processor (1504) retrieves and executes the instructions. The instructions received by memory (1506) may optionally be stored on storage (1510) either before or after execution by processor (1504).

[0236] Computer system (1500) also includes a communication interface (1518) coupled to bus (1502). Communication interface (1518) provides a two-way data communication coupling to network link(s) (1520) that are directly or indirectly connected to at least one communication networks, such as a network (1522) or a public or private cloud on the Internet. For example, communication interface (1518) may be an Ethernet networking interface, integrated-services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of communications line, for example an Ethernet cable or a metal cable of any kind or a fiber-optic line or a telephone line. Network (1522) broadly represents a local area network (LAN), wide-area network (WAN), campus network, internetwork or any combination thereof. Communication interface (1518) may comprise a LAN card to provide a data communication connection to a compatible LAN, or a cellular radiotelephone interface that is wired to send or receive cellular data according to cellular radiotelephone wireless networking standards, or a satellite radio interface that is wired to send or receive digital data according to satellite wireless networking standards. In any such implementation, communication interface (1518) sends and receives electrical, electromagnetic or optical signals over signal paths that carry digital data streams representing various types of information.

[0237] Network link (1520) typically provides electrical, electromagnetic, or optical data communication directly or through at least one network to other data devices, using, for example, satellite, cellular, Wi-Fi, or BLUETOOTH technology. For example, network link (1520) may provide a connection through a network (1522) to a host computer (1524).

[0238] Furthermore, network link (1520) may provide a connection through network (1522) or to other computing devices via internetworking devices and / or computers that are operated by an Internet Service Provider (ISP) (1526). ISP (1526) provides data communication services through a world-wide packet data communication network represented as internet (1528). A server computer (1530) may be coupled to internet (1528). Server (1530) broadly represents any computer, data center, virtual machine or virtual computing instance with or without a hypervisor, or computer executing a containerized program system such as DOCKER or KUBERNETES or the like. Server (1530) may represent an electronic digital service that is implemented using more than one computer or instance and that is accessed and used by transmitting web services requests, uniform resource locator (URL) strings with parameters in HTTP payloads, API calls, app services calls, or other service calls. Computer system (1500) and server (1530) may form elements of a distributed computing system that includes other computers, a processing cluster, server farm orother organization of computers that cooperate to perform tasks or execute applications or services. Server (1530) may comprise one or more sets of instructions that are organized as modules, methods, objects, functions, routines, or calls. The instructions may be organized as one or more computer programs, operating system services, or application programs including mobile apps. The instructions may comprise an operating system and / or system software; one or more libraries to support multimedia, programming or other functions; data protocol instructions or stacks to implement TCP / IP, HTTP or other communication protocols; file format processing instructions to parse or render files coded using HTML, XML, JPEG, MPEG or PNG; user interface instructions to render or interpret commands for a graphical user interface (GUI), command-line interface or text user interface; application software such as an office suite, internet access applications, design and manufacturing applications, graphics applications, audio applications, software engineering applications, educational applications, games or miscellaneous applications. Server (1530) may comprise a web application server that hosts a presentation layer, application layer and data storage layer such as a relational database system using structured query language (SQL) or no SQL, an object store, a graph database, a flat file system or other data storage.

[0239] Computer system (1500) can send messages and receive data and instructions, including program code, through the network(s), network link (1520) and communication interface (1518). In the Internet example, a server (1530) might transmit a requested code for an application program through Internet (1528), ISP (1526), local network (1522) and communication interface (1518). The received code may be executed by processor (1504) as it is received, and / or stored in storage (1510), or other non-volatile storage for later execution.

[0240] The execution of instructions as described in this section may implement a process in the form of an instance of a computer program that is being executed, and consisting of program code and its current activity. Depending on the operating system (OS), a process may be made up of multiple threads of execution that execute instructions concurrently. In this context, a computer program is a passive collection of instructions, while a process may be the actual execution of those instructions. Several processes may be associated with the same program; for example, opening up several instances of the same program often means more than one process is being executed. Multitasking may be implemented to allow multiple processes to share processor (1504). While each processor (1504) or core of the processor executes a single task at a time, computer system (1500) may be programmed to implement multitasking to allow each processor to switch between tasks that are being executed without having to wait for each task to finish. In an embodiment, switches may be performed when tasks perform input / output operations, when atask indicates that it can be switched, or on hardware interrupts. Time-sharing may be implemented to allow fast response for interactive user applications by rapidly performing context switches to provide the appearance of concurrent execution of multiple processes simultaneously. In an embodiment, for security and reliability, an operating system may prevent direct communication between independent processes, providing strictly mediated and controlled inter-process communication functionality.

[0241] The term “cloud computing” is generally used herein to describe a computing model which enables on-demand access to a shared pool of computing resources, such as computer networks, servers, software applications, and services, and which allows for rapid provisioning and release of resources with minimal management effort or service provider interaction.

[0242] A cloud computing environment (sometimes referred to as a cloud environment, or a cloud) can be implemented in a variety of different ways to best suit different requirements. For example, in a public cloud environment, the underlying computing infrastructure is owned by an organization that makes its cloud services available to other organizations or to the general public. In contrast, a private cloud environment is generally intended solely for use by, or within, a single organization. A community cloud is intended to be shared by several organizations within a community; while a hybrid cloud comprises two or more types of cloud (e.g., private, community, or public) that are bound together by data and application portability.

[0243] Generally, a cloud computing model enables some of those responsibilities which previously may have been provided by an organization’s own information technology department, to instead be delivered as service layers within a cloud environment, for use by consumers (either within or external to the organization, according to the cloud’s public / private nature). Depending on the particular implementation, the precise definition of components or features provided by or within each cloud service layer can vary, but common examples include: Software as a Service (SaaS), in which consumers use software applications that are running upon a cloud infrastructure, while a SaaS provider manages or controls the underlying cloud infrastructure and applications. Platform as a Service (PaaS), in which consumers can use software programming languages and development tools supported by a PaaS provider to develop, deploy, and otherwise control their own applications, while the PaaS provider manages or controls other aspects of the cloud environment (i.e., everything below the run-time execution environment). Infrastructure as a Service (laaS), in which consumers can deploy and run arbitrary software applications, and / or provision processing, storage, networks, and other fundamental computing resources, while an laaS provider manages or controls the underlying physical cloud infrastructure (i.e., everythingbelow the operating system layer). Database as a Service (DBaaS) in which consumers use a database server or Database Management System that is running upon a cloud infrastructure, while a DBaaS provider manages or controls the underlying cloud infrastructure, applications, and servers, including one or more database servers.

[0244] In a particular arrangement of a SaaS application comprising a cloud-based architecture (1600), an example of which is provided in FIG. 16, SaaS system (1600) comprises a client-accessible server application system (1610) for providing the SaaS application, the server (1610) comprising:• a user interface (1611) providing responsive dashboards for data presentation and user interaction by third-party client devices (1620);• arithmetic logic unit or processor(s) (1613) for performing computer program code instructions. The processor(s) 2013) may be a reduced instruction set computer (RISC) or complex instruction set computer (CISC) processor or the like;• memory (1615) for storage of computer code instructions, for example as disclosed in any one of the aspects or arrangements of the correction models discussed above;• database (1617) for storage of the stability consensus functions and models as discussed above for correction of analyte measurements of degraded biological samples and cloud storage of aggregated stability consensus model data including correction coefficients for one or more analytes;• server application programming interface(s) (API(s)) (1617) encapsulating portal functionality, using open-standards to facilitate third-party vendors in building platform-compatible apps and connectors built on open-standard API's to facilitate connection to third-party client devices (1620);• wherein user interface (1611) is configured to provide corrected output data comprising the corrected analyte test result of the biological sample to the third party client requestor (1620).

[0245] Processor(s) (1613), memory (1615) and database (1617) preferably are configured to provide computer software code instructions and data for implementation of:• a data validation engine which is configured to receive test values and corresponding degradation attribute data from a third-party client such as a measurement laboratory;• optionally, a quality control module configured to screen the received sample measurements and supplied degradation attribute information supplied by client to determine if sample degradation is in line with expectations, and to reject significantly degraded samples prior to proceeding to analyte measurements; and• correction module configured for correcting test values of a degraded biological sample having undergone changes on the basis of environmental factors experienced by the sample.

[0246] Client devices (1620) of a client requestor, for example a biological test laboratory, comprise:• Processor(s) (1621) for execution of software code instructions stored in memory (1623) for interacting with application module(s) (1625) and for interacting with server (1610) via client API(s) (1629) over a network (1610), such as, for example, the internet or similar communications network.• Application module(s) (1625) configured to receive biological sample test data and corresponding degradation attributes via input module(s) (1627).

[0247] The correction data received from client device(s) (1620) is performed using a correction coefficient based on a machine learning model trained on a dataset of degraded samples to provide a database of models / equation, that take environmental, donor and degradation attributes as inputs and which output the correction coefficient with respect to a plurality of testable analytes of the biological sample in accordance with one or more of the methods or procedures disclosed herein. The correction module of server (1610) is configured to receive analyte test results of a degraded biological sample, apply the correction factor applicable to an analyte to provide a corrected test result.

[0248] The SaaS system (1600) is designed for scalability and extensibility to allow customization to accommodate the needs of individual healthcare organizations. Examples of such customization include, among other uses as would be appreciated by the skilled addressee:(i) Customer A needs a solution that automatically links the output of the CDS to the input of the Predictive Analytics (PA). A module with a pre-stored set of queries to be automatically run on the PA system for each of a particular set of patients;(ii) Customer B wishes to automatically aggregate clinical data from certain patients as part of a planned research study;(iii) Customer C plans to deploy biometric monitoring for select patients and is interested in setting the CDS diagnostic report for auto-run to generate reports on these patients when certain events are registered in real time from the sensors.

[0249] In use, of the SaaS system, the biological testing laboratory clients receives biological samples and associated data with respect to one or more degradation attributes experienced by the received biological samples; conducts testing of the samples of one or more analytes to record a Test Value for the analyte; uploads sample Test Value data and to the software application so that a correction calculation could be made. The software application would apply the relevant biomarker model to the uploaded biomarker result and provide back a “corrected” value along with the original value, the correction applied, and the version of the model that was applied to the result.Embodiments

[0250] Reference throughout this specification to “one embodiment”, “an embodiment”, “one arrangement” or “an arrangement” means that a particular feature, structure or characteristic described in connection with the embodiment / arrangement is included in at least one embodiment / arrangement of the present invention. Thus, appearances of the phrases “in one embodiment / arrangement” or “in an embodiment / arrangement” in various places throughout this specification are not necessarily all referring to the same embodiment / arrangement, but may. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to one of ordinary skill in the art from this disclosure, in one or more embodiments / arrangements .

[0251] Similarly, it should be appreciated that in the above description of example embodiments / arrangements of the invention, various features of the invention are sometimes grouped together in a single embodiment / arrangement, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of thevarious inventive aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment / arrangement. Thus, the following claims are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment / arrangement of this invention.

[0252] Furthermore, while some embodiments / arrangements described herein include some but not other features included in other embodiments / arrangements, combinations of features of different embodiments / arrangements are meant to be within the scope of the invention, and form different embodiments / arrangements, as would be understood by those in the art. For example, in the following claims, any of the claimed embodiments / arrangements can be used in any combination.

[0253] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

[0254] In describing the preferred embodiment of the invention illustrated in the drawings, specific terminology will be resorted to for the sake of clarity. However, the invention is not intended to be limited to the specific terms so selected, and it is to be understood that each specific term includes all technical equivalents which operate in a similar manner to accomplish a similar technical purpose. Terms such as “forward”, “rearward”, “radially”, “peripherally”, “upwardly”, “downwardly”, and the like are used as words of convenience to provide reference points and are not to be construed as limiting terms.Conclusion

[0255] In conclusion, remote sample collection is vital to addressing the increasing health disparity for patients living in rural and remote communities that cannot easily access common pathology services like biological sample testing. However, strict quality control measures implemented by medical pathology laboratories reject most samples collected in an at-home setting due to the inherent time delay and degradation that occurs during sample transit that would, without a solution, cause issues with accuracy of test results. Prior art solutions have proven to be prohibitively expensive, enough so that remote sample collection is not commonplace in clinical practice. The invention described herein, is a cost-effective, software-based solution that:(i) expands the catalog to include labile tests not previously available in the at-home setting by extending the sample processing latency period;(ii) improves testing precision and accuracy for nonlabile analytes and sample transported in controlled and uncontrolled environments; and(iii) creates a new quality control standard for samples collected at-home leading to lower sample recollection rates by factoring in each sample transit condition into the assessment.

[0256] Specifically, the embodiment detailed herein will revolutionize how healthcare can be delivered at-home and on the patient’s terms to quantify analytes that were heretofore unable to be measured from remotely collected biological samples.

Claims

CLAIMS:

1. A computer-implemented method for correcting an analyte measurement taken from a degraded biological sample, the method comprising:(a) obtaining a first set of rules that define stability model selection based on analyte type, sample type, and / or sample treatment;(b) obtaining a data set corresponding to the degraded biological sample, the data set having data indicative of an analyte type, a sample type, a sample treatment, if any, and a transit time, wherein a value indicative of transit time was uploaded from a sensor accompanying a package in which the degraded biological sample was received and / or via a proxy;(c) applying the first set of rules to the data set, and in response thereto, selecting a stability model from a plurality of stability models, the selected stability model specifically trained for the analyte type, the sample type indicated in the data set, and the sample treatment, if any indicated in the data set;(d) generating a correction factor by evaluating the value of the transit time against the selected stability model; and(e) providing the correction factor for applying to a degraded analyte measurement taken from the degraded biological sample, wherein the degraded analyte measurement corresponds to the analyte indicted in the data set.

2. The method of Claim 1, wherein the first set of rules has temperature parameters encoded as a binary variable.

3. The method of either Claim 1 or Claims 2, further comprising producing a corrected value by applying the correction factor to the analyte measurement.

4. The method of any one of Claims 1 to 3: wherein the first set of rules are based on a decision tree or an encoding of a regression equation; and / or wherein the first set of rules are based on a decision tree a and a second set of rules are based on binary encoding of a regression equation.

5. The method of any one of Claims 1 to 4, wherein the first set of rules includes a selection criteria relating to transit time and / or transit temperature.

6. The method of Claim 5, wherein the set of rules includes wherein transit time is selected from the group consisting of:greater than 12 hours; greater than 24 hours; greater than 36 hours; greater than 48 hours; greater than 72 hours; between 24 hours and 36 hours; between 24 hours and 48 hours; between 24 hours and 60 hours; between 24 hours and 72 hours; between 36 hours and 48 hours; and between 48 hours and 72 hours.

7. The method of any one of Claims 1 to 6, wherein a temperature criteria is selected from the group consisting of: mean sample transit temperature; maximum sample transit temperature; sample days at 23 °C to 25°C; sample days at below 13°C; minimum sample transit temperature; sample days at above 35 °C; sample days at 13°C to 23°C; and cumulative sample temperature, and combinations thereof.

8. The method of any one of Claims 1 to 7, wherein the first set of rules includes: one or more donor attributes; and / or one or more degradation attributes.

9. The method of any one of Claim 8, wherein the donor attributes relate to donor demographics, donor health, or both.

10. The method of any one of Claims 1 to 9, wherein the data set includes: data indicative of one or more of a transit time; a transit temperature; donor demographics; donor health; and sample degradation.

11. The method of any one of Claims 1 to 10, wherein the sensor is a timer, a temperature sensor, or both.

12. The method of any one of Claims 1 to 11, wherein the selected stability model was trained using an environmental attribute with of weight of between:0% to about less than 70%; less than about 30% to about 100%; greater than or equal to about 30% to about 100%; greater than or equal to about 60% to about 100%; or 100%.

13. The method of any one of Claims 1 to 12, wherein the selected stability model was trained using a weight factor of: correction factor= y + (32 — 100) x environmental + (0 — 38) x donordemo+ (0 — 24) x donorhealth + (0 — 7) x degradation wherein indicates a value for a -intercept.

14. The method of any one of Claims 1 to 13, wherein the sample treatment is a physical treatment or a chemical treatment.

15. The method of Claims 14 wherein the chemical treatment is: ethylenediaminetetraacetic acid (EDTA); lithium heparin; hydrochloric acid (HC1); sodium dodecyl sulfate (SDS) / TRIS (tris(hydroxymethyl)aminomethane) / EDTA; a protease inhibitor; sodium citrate;RPMI (Roswell Park Memorial Institute Medium);HEPES (4-(2 -hydroxy ethyl)- 1 -piperazineethanesulfonic acid);FICOLL®70 (pPoly(sucrose-co-epichlorhydrin)); and combinations thereof.

16. A computer-implemented method for improving accuracy and precision of analyte measurements in a degraded biological sample, the method comprising:(a) uploading, from a sensor included with a package in which the degraded biological sample was received, at least one of a transit time or a transit temperature and / or via a proxy;(b) identifying, from a database storing data about the degraded biological sample, a given analyte and a type of biological sample, wherein the given analyte corresponds to the analyte measurement to be corrected and the type of biological sample is selected from one of a body tissue sample or a body fluid sample;(c) based on the given analyte and the type of biological sample, selecting a stability model that has been trained to predict a correction factor for the given analyte when measured in the identified type of biological sample, wherein training utilizes a specifically designed machine learning or artificial intelligence model;(d) inputting the transit time and / or the transit temperature into the selected stability model and in response thereto, obtaining the correction factor, wherein the correction factor is a predicted relative percent difference between a true value and a test value;(e) dividing an uncorrected value of the given analyte by the obtained correction factor, wherein the uncorrected value is determined by measuring the given analyte in the degraded biological sample; and(f) in response to dividing, obtaining a corrected value for the given analyte, wherein the corrected value corresponds to an analyte measurement that would have occurred if the given analyte was measured before the degraded biological sample expired.

17. The method of Claim 16, wherein: the stability model is selected based upon the analyte; the type of biological sample; and / or presence or absence of a treatment; and / or at least one transit time; and / or at least one transit temperature.

18. The method of either Claim 16 or Claim 17, wherein for a given analyte, the selected stability model will predict a different correction factor based on the type of biological sample.

19. The method of any one of Claims 16 to 18, wherein the degraded biological sample is determined to be expired by:(i) a time that exceeds a time in which accuracy, precision, or both for a particular analyte has been affected by a delay in sample processing; and / or(ii) wherein an existing time and / or total allowable error threshold for the particular analyte is established by an established authority, a particular laboratory, or both.

20. A computer-implemented method for correcting an analyte measurement taken from a degraded biological sample, the method comprising:(a) obtaining a first set of rules that define stability model selection based on analyte type, sample type, and / or sample treatment;(b) obtaining a data set corresponding to the degraded biological sample, the data set having data indicative of an analyte type, a sample type, a sample treatment, if any, a transit time, and a transit temperature, wherein a value indicative of transit time and transit temperature is uploaded from a sensor accompanying a package in which the degraded biological sample is received and / or via a proxy;(c) applying the first set of rules to the data set, and in response thereto, selecting a stability model from a plurality of stability models, the selected stability model specifically trained for the analyte type, the sample type indicated in the data set, the sample treatment, and at least one of the transit time, transit temperature, or both;(d) generating a correction factor by evaluating the value of the transit time and transit temperature against the selected stability model; and(e) providing the correction factor for applying to a degraded analyte measurement taken from the degraded biological sample, wherein the degraded analyte measurement corresponds to the analyte indicted in the data set.

21. A computer-implemented method for improving accuracy and precision of analyte measurements in a degraded biological sample, the method comprising:(a) uploading, from a sensor included with a package in which the degraded biological sample was received, at least one of a transit time or a transit temperature, and / or via a proxy;(b) identifying, from a database storing data about the degraded biological sample, a given analyte and a type of biological sample, wherein the given analyte corresponds to the analyte measurement to be corrected and the type of biological sample is selected from one of a body tissue sample or a body fluid sample;(c) based on the given analyte and the type of biological sample, selecting a stability model that has been trained to predict a correction factor for the given analyte when measured in the identified type of biological sample, wherein training utilizes a specifically designed machine learning or artificial intelligence model that selects a best fit model based upon:y + (32 — 100) x environmental + (0 — 38) x donordemo + (0 — 24) x donorhealth + (0 — 7) x degradation wherein indicates a value for ay-intercept;(d) inputting the transit time and / or the transit temperature into the selected stability model and in response thereto, obtaining the correction factor, wherein the correction factor is a predicted relative percent difference between a true value and a test value;(e) dividing an uncorrected value of the given analyte by the obtained correction factor, wherein the uncorrected value is determined by measuring the given analyte in the degraded biological sample; and(f) in response to dividing, obtaining a corrected value for the given analyte, wherein the corrected value corresponds to an analyte measurement that would have occurred if the given analyte was measured before the degraded biological sample expired.

22. A method for preparing a biological sample for measuring an analyte after the sample is degraded by time, the method comprising:(a) obtaining a biological sample from a patient, the biological sample within a vessel comprising a sample additive;(b) providing an environmental sensor, suitable for capturing a transit time elapsed and a transit temperature at regular intervals from the time of capture of the biological sample to an end point;(c) providing, to a database, data relating to type of captured biological sample, type of additive, and an environmental data from the environmental sensor;(d) selecting, based upon the environmental data, the type of captured biological sample, and type of additive, a model for quantifying a degradation constant;(e) analyzing the sample to obtain a degraded value for the analyte of interest; and(f) based on the selected model, calculating a correction factor to be applied to the degraded value of the desired analyte.

23. The method of Claim 22, further comprising wherein the database further comprises a donor attribute.

24. The method of either Claim 22 or Claim 23, wherein the selected model was trained using a weight factor of: correction factor= y + (32 — 100) x environmental + (0 — 38) x donordemo+ (0 — 24) x donorhealth + (0 — 7) x degradationwherein indicates a value for a -intercept.

25. The method of any one of Claims 22 to 24, wherein the selected model includes wherein transit time is selected from the group consisting of: greater than 12 hours; greater than 24 hours; greater than 36 hours; greater than 48 hours; greater than 72 hours; between 24 hours and 36 hours; between 24 hours and 48 hours; between 24 hours and 60 hours; between 24 hours and 72 hours; between 36 hours and 48 hours; and between 48 hours and 72 hours.

26. The method of any one of Claims 22 to 25, wherein the step of selecting a model includes a temperature criteria, wherein the temperature criteria is selected from the group consisting of: mean sample transit temperature; maximum sample transit temperature; sample days at 23 °C to 25°C; sample days at below 13°C; minimum sample transit temperature; sample days at above 35 °C; sample days at 13°C to 23°C; cumulative sample temperature; and combinations thereof.

27. The method of any one of Claims 22 to 26: wherein the sample additive is a chemical treatment; and / or wherein the sample is prepared with a physical treatment.

28. The method of any one of Claims 22 to 27, wherein the chemical treatment is: ethylenediaminetetraacetic acid (EDTA); lithium heparin; hydrochloric acid (HC1);sodium dodecyl sulfate (SDS) / TRIS (tris(hydroxymethyl)aminomethane) / EDTA; a protease inhibitor; sodium citrate;RPMI (Roswell Park Memorial Institute Medium);HEPES (4-(2 -hydroxy ethyl)- 1 -piperazineethanesulfonic acid);FICOLL®70 (poly(sucrose-co-epichlorhydrin)); and combinations thereof.

29. A method for preparing a biological sample for measuring an analyte after the sample is degraded by time, the method comprising:(a) obtaining from a patient, the biological sample within a vessel, and performing on said sample a treatment;(b) providing an environmental sensor, suitable for capturing total time elapsed and temperature at regular intervals from the time of capture of the biological sample to an end point;(c) providing data to a database, the data relating to type of captured biological sample, type of treatment, and an environmental data from the environmental sensor;(d) based upon the temperature and time data, the type of captured biological sample, and type of additive, selecting a model for quantifying a degradation constant; and(e) preserving the captured biological sample at the end point wherein the degradation constant derived can be utilized to provide a correction of an analyte from the captured biological sample.

30. A method of preparing a degraded sample for quantification of a desired analyte, the method comprising:(a) obtaining a biological sample from a patient, the biological sample within a vessel comprising a sample additive;(b) providing an environmental sensor, suitable for capturing total time elapsed and temperature at regular intervals from time of capture of the biological sample to an end point;(c) providing data to a database, the data relating to type of captured biological sample, type of additive, and environmental data from the environmental sensor;(d) based upon the temperature and time data, the type of captured biological sample, and type of additive, selecting a model for quantifying a degradation constant; and(e) capturing the desired portion of the sample for subsequent analysis wherein the constant derived can be utilized to provide a correction of an analyte from the captured biological sample.

31. A method of preparing a degraded portion of a sample capable of being analyzed for a desired analyte wherein said sample would otherwise be discarded due to sample error, the method comprising:(a) obtaining a request for the sample and the desired analyte from said sample, and storing the same within a database;(b) capturing the sample within a specimen container, said specimen container comprising an additive, and contemporaneously beginning a data logger to measure a transit time and a transit temperature;(c) subjecting the sample to measure elapsed transit time and transit temperature conditions, said elapsed transit time and transit temperature conditions sufficient to degrade the sample to otherwise disqualify the sample from being usable for sampling of a desired analyte;(d) upon receipt of the sample within a facility for analyzing the sample, terminating the data logger and obtaining an environmental data comprising the elapsed transit time from capturing the sample to the termination of the data logger, and a temperature data during the elapsed transit time;(e) inputting sample type, the desired analyte, the additive, total elapsed transit time, and the temperature data into a model to define a correction factor;(f) processing the sample to obtain a desired portion of the sample for calculating of a degraded value of the desired analyte; and(g) calculating a corrected value of the analyte by applying the correction factor to the degraded value.

32. The method of Claims 31, wherein the temperature is obtained at time intervals during the elapsed transit time.

33. The method of either Claim 31 or Claim 32, wherein the set of rules includes transit times selected from the group consisting of: greater than 12 hours; greater than 24 hours; greater than 36 hours; greater than 48 hours; greater than 72 hours; between 24 hours and 36 hours; between 24 hours and 48 hours; between 24 hours and 60 hours;between 24 hours and 72 hours; between 36 hours and 48 hours; and between 48 hours and 72 hours.

34. The method of any one of Claims 31 to 33, wherein temperature criteria is selected from the group consisting of: mean sample transit temperature; maximum sample transit temperature; sample days at 23 °C to 25°C; sample days at below 13°C; minimum sample transit temperature; sample days at above 35 °C; sample days at 13°C to 23°C; cumulative sample temperature; and combinations thereof.

35. The method of any one of Claims 31 to 34, wherein the selected model was trained using a weight factor of: correction factor= y + (32 — 100) x environmental + (0 — 38) x donordemo+ (0 — 24) x donorhealth + (0 — 7) x degradation wherein indicates a value for a -intercept.

36. The method of any one of Claims 31 to 35, wherein the additive is a chemical treatment or comprises a physical treatment.

37. The method of any one of Claims 31 to 36, wherein the chemical treatment is selected from the group consisting of: ethylenediaminetetraacetic acid (EDTA); lithium heparin; hydrochloric acid (HC1); sodium dodecyl sulfate (SDS) / TRIS (tris(hydroxymethyl)aminomethane) / EDTA; a protease inhibitor; sodium citrate;RPMI (Roswell Park Memorial Institute Medium);HEPES (4-(2 -hydroxy ethyl)- 1 -piperazineethanesulfonic acid);FICOLL®70 (poly(sucrose-co-epichlorhydrin)); and combinations thereof.

38. A method of correcting test results of a degraded sample having experienced a delay in time between collection and analysis of the degraded sample, the method comprising:(a) receiving within a collection container a patient biological sample received in a packaged state, said collection container comprising an additive, and wherein said sample packaging comprising:(i) one or more environmental sensors and a data logger or proxies thereof configured to measure and store environmental attribute data to which the patient biological sample was exposed during transit; and(ii) patient attribute data with respect to attributes of a patient;(b) providing a data reader for downloading the environmental attribute data from the data logger or use of historical, weather, and / or shipment tracking information as a proxy for environmental attribute data and associating the environmental attribute data and patient attribute data with the patient biological sample;(c) providing biological sample test hardware for measuring one or more analyte degraded value from a degraded patient biological sample;(d) providing a database for storing data regarding the biological patient sample comprising:(i) environmental attributes, sample type, and / or sample additive; and / or(ii) degradation attributes and / or donor health attributes;(e) providing a processor configured to receive the patient attribute data from the database and, selecting a machine learning or artificial intelligence (Al) model based upon the patient attribute data, wherein the selection is based upon a set of rules;(f) generate a correction factor based upon the selected model, wherein input to determine the correction factor is the environmental attributes, the sample type, and the sample additive;(g) generating a degraded value of said analyte from said patient biological sample; and(h) applying the correction factor to the degraded value to yield a corrected value.

39. A method of analyte correction from a biological sample having a level of degradation due to time and temperature exposure between collection and processing, the method comprising:(a) receiving a biological sample into a sample container, and applying a treatment to said biological sample;(b) activating a data logger upon providing the biological sample;(c) exposing the sample container to an environmental attribute of time and temperature;(d) capturing the environmental attributes from the data logger, uploading the same into a database associated with the biological sample;(e) processing the biological sample, and obtaining a degraded value for at least one analyte;(f) generating a correction factor by inputting the environmental attributes, the sample type, the analyte, and the treatment type into a model trained to determine a correction factor; and(g) applying the correction factor to the degraded value to obtain a corrected analyte value.

40. The method of either Claims 38 or Claim 39, further comprising providing degradation attribute hardware for determining one or more degradation attributes of the patient biological sample.

41. The method of any one of Claims 38 to 40: wherein the collection container comprises a unique ID; wherein the unique ID is stored with the database; and wherein the environmental data and the patient attribute data are associated with the unique ID within the database.

42. The method of any one of Claims 38 to 41, wherein: the machine learning or Al model is trained by generating a first sample from a donor, the first sample having a true value of a first analyte; generating at least one test sample from the donor, said test sample undergoing a known environmental attribute; determining a test value for the first analyte; and training the machine learning or Al model on a difference between the true value of the analyte and the test value of the analyte based upon the known environmental attribute.

43. The method of any one of Claims 38 to 42, further comprising wherein the model is trained by inclusion of the degradation attributes and / or donor attributes.

44. The method of any one of Claims 38 to 43, wherein the biological sample is: plasma; precentrifuged plasma; serum; dried blood spot; urine; saliva; and reproductive fluids.

45. The method of any one of Claims 38 to 44, further comprising: performing one or more degradation analyses; processing the sample when the degradation analysis is within a predetermined range; and rejecting the sample if the degradation analysis is outside of the predetermined range.

46. The method of any one of Claims 38 to 45, further comprising wherein the selected model is biased toward the environmental attribute of the sample.

47. The method of any one of Claims 38 to 46. wherein the donor attribute is selected from the group consisting of: age; sex; ethnicity;BMI; fasting status; time of day; medication; medical conditions; and combinations thereof.

48. The method of any one of Claims 38 to 47, wherein the environmental attribute is: time; temperature; humidity; pressure; geolocation;acceleration; light; or combinations thereof.

49. The method of any one of Claims 38 to 48, wherein the degradation attribute is selected from the group consisting of:DNA; white blood cells; hemoglobin; sodium; chloride; potassium; a color; one or more of a proxy of entropy of a biological sample; blood cell lysis; presence or absence of DNA; an analyte different from an analyte of interest; evaporation; and combinations thereof.

50. The method of any one of Claims 38 to 49, wherein the model is determined by finding an optimal value for a parameter in order to minimize an error or loss function by selecting from: a gradient descent model; close-form solution; regularization;Bayesian inference estimates; and / or cross-validation.

51. The method of any one of Claims 38 to 50: wherein the environmental attribute is a dynamic environmental condition; and wherein the dynamic environmental condition can be obtained by a data logger or by a proxy, said proxy corresponding to an ambient temperature at a location of the sample.

52. The method of any one of Claims 38 to 51, wherein donor information is obtained from an electronic medical record.

53. The method of any one of Claims 38 to 52, wherein the degradation attributes from the biological sample are provided by the attributes within the biological sample itself.

54. The method of any one of Claims 38 to 53, wherein weight is applied to a variable within the model wherein the weight is based upon the known environmental attribute associated with a sample.

55. The method of any one of Claims 38 to 54, wherein the sample additive is selected based upon lack of interference with the test for the desired analyte.

56. A method of providing corrective modification of preserved degraded biological samples, the method comprising:(a) obtaining a biological sample within a collection vessel, said collection vessel comprising an additive therein;(b) providing a data logger comprising an environmental sensor within packaging for transport of the biological sample;(c) capturing an environmental attribute perceived by the environmental sensor and storing the environmental attribute within the data logger;(d) upon receipt of the biological sample at a processing facility, obtaining the environmental attribute from the data logger;(e) performing an analysis of a desired analyte from the biological sample to obtain a degraded value of the desired analyte;(f) selecting a model for obtaining a correction factor, said model selected from input of analyte type, sample type, sample additive, and at least one environmental attribute;(g) inputting the at least one environmental attribute into the selected model and receiving a correction coefficient; and(h) applying the correction coefficient to the degraded value to obtain a corrected value of the desired analyte.

57. The method of Claim 56, wherein the biological sample is: a blood sample; a preprocessed blood sample; urine; or saliva.

58. The method of Claim 56, wherein the sample type is defined as the sample type in transit.

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