Systems and methods for customized automated clinical diagnostic crossover testing - Patents.com

Abstract

A system and method for performing customized automated crossover testing for clinical diagnostic analyzers is disclosed. In a disclosed embodiment, the optimal number of samples to be performed on a new QC material is determined based on historical information between the analytical instrument and a peer group of similar instruments. The determined optimal number of samples is used to perform the crossover test and report the results.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application Serial No. 63 / 368,993, filed July 21, 2022, the disclosure of which is incorporated herein by reference in its entirety.

[0002] The present invention relates generally to clinical diagnostic analyzers, and more particularly to systems and methods for performing customized automated crossover tests in such analyzers. [Background technology]

[0003] Clinical diagnostic laboratories use a variety of quality control schemes to ensure that clinical diagnostic processes and clinical diagnostic analyzers used to analyze patient samples and other test specimens provide accurate diagnostic results. One common quality control scheme involves testing quality control (QC) materials with known properties using the same analyzers and processes used to test patient samples. Performing such quality control testing with materials with known properties ensures that the clinical diagnostic analyzers used to perform the tests provide accurate results or provide results within predetermined ranges or specifications, and similarly ensures that the reagents and processes used with the analyzers provide expected results. Summary of the Invention [Problem to be solved by the invention]

[0004] Although quality control testing using control materials with known properties is generally useful, statistical control issues can arise when control materials must be replenished. Because control materials have expiration dates and QC testing using control materials consumes them, laboratories must periodically acquire and use new lots of control materials. This requires a material transition (crossover) and the initiation of use of new lots of QC materials. Crossing over to new QC materials is a significant undertaking for laboratories because they must ensure the reliability and accuracy of the new control materials before relying on them for further testing. Even if a new lot of QC control material has similar properties to the previous lot, even slight variations between lots can affect the accuracy of the test, especially until a sufficient number of tests can be performed with the new QC material. Therefore, laboratories must conduct crossover tests to verify the parameters of the new material before the desired accuracy of the test can be assured. Such crossover tests must be performed each time the control material is changed. This is because control materials with insert ranges, i.e., even assayed control materials, are intended to provide a quick determination of whether a laboratory is in control and are not intended to be used for performance monitoring.

[0005] A typical crossover study involves determining the statistical behavior of a new lot of QC control material, i.e., estimating the arithmetic mean and standard deviation (SD) of the new material. To obtain the mean and SD measurements, a common approach in crossover studies was to evaluate samples over time to collect data for the new control material until sufficient data had been collected, and then calculate the mean and SD from the collected data. Once calculated, the newly calculated mean and SD were then used and assigned for future control tests using the new quality control material.

[0006] One generally accepted method for conducting this initial assessment is described in "Statistical Quality Control for Quantitative Measurement Procedures: Principles and Definitions; Approved Guideline - Third Edition," which requires a minimum of 20 measurements of control material on separate days for each control level. Therefore, this generally accepted method requires the collection of at least 20 data points per control level over a 20-day period. Thus, for example, a trilevel control containing 30 analytes would require 90 tests, with one data point collected for each individual test. The collected data are then used to estimate the mean and standard deviation for new lots of material. In addition to the time required, such testing is quite expensive for laboratories, costing over $200 for each molecular data point collected. Furthermore, such testing is labor-intensive. Because no standardized system exists for conducting such crossover studies, most laboratories typically manually process the collected data using spreadsheets and manually enter the data to calculate the mean and standard deviation for new control materials.

[0007] Even with the time, expense, and inefficiency of conducting crossover tests according to commonly recommended procedures, the results of those tests may not have the accuracy desired or required by the testing laboratory. For example, 20 data points are sufficient to determine the mean for a new material, but collecting such a number of data points is unnecessary and therefore inefficient, since the mean can be determined using only 10 data points. Thus, commonly recommended crossover testing methods incur unnecessary testing and expense in determining the mean. Furthermore, 20 data points are insufficient to determine the SD with the desired level of accuracy; typically, 80 data points are required. Thus, commonly recommended methods typically result in estimated SDs with high error, and using more data points can incur unnecessary time and expense.

[0008] Recognizing the above limitations, the industry is new =(MEAN new *CV old ) / 100, where CV old =SD old *100 / MEAN old An alternative method for determining the SD of a new control material has been proposed, based on using only 10 data points, by incorporating the mean and SD of the old material using the formula: However, although this alternative determination method requires fewer data points and therefore less time, the results using this method still involve potential inaccuracies in the calculation of the mean (see, for example, C24Statistical Quality Control for Quantitative Measurement Procedures: Principles and Definitions, 4 th See Edition).

[0009] Although 10 data point processing can provide an improvement over the standard 20 data point processing, saving time, effort, and QC materials, it is still a "one size fits all" compromise in that there are times when even 10 data points do not need to be examined, and times when more than 10 data points will produce optimal results.

[0010] Therefore, it is apparent that there remains a need in the art for improved systems and methods for performing automated crossover testing that account for test method variability, test method implementation, analytical concentration of QC materials, and other systemic variability in the process, and that increase the accuracy of crossover testing and reduce the time and cost required compared to commonly known methods. [Means for solving the problem]

[0011] Systems and methods for performing automated clinical diagnostic crossover testing are described in PCT Application No. PCT / US20 / 66424, and systems and methods for virtual crossover testing for clinical diagnostic systems are described in PCT Application No. PCT / US20 / 66563, the entire disclosures of which are incorporated herein by reference.

[0012] The present invention relates to a system and method for performing customized, automated crossover testing on clinical diagnostic analyzers. In an exemplary embodiment, the system and method of the present invention minimizes the time, effort, and expense required to perform crossover testing on new batches of QC material by using one or more clinical diagnostic analyzers to determine a customized, optimal number of data points / samples for new quality control material to be tested in the crossover testing based on historical data from the instrument(s) and its peer group(s).

[0013] In one aspect, a clinical diagnostic analyzer for performing customized automated crossover testing includes a processor, memory, measurement hardware, and an input panel / display. The analyzer prompts a user to load QC specimens and begin testing and analysis to determine an optimized number of QC samples to test for new or crossover QC material.

[0014] In another aspect, an automated method for calculating the optimal number of data points / samples to use in a crossover study includes estimating the historical relationship between a clinical diagnostic analyzer and its peer group, estimating a new control mean using the historical relationship and new control peer data, estimating a confidence coefficient for the new control mean, simulating or calculating the variation of the new control mean with sample size, and selecting a sample size that meets desired performance criteria.

[0015] Reference to the remaining portions of the specification, including the drawings and claims, will reveal other features and advantages of the present invention. Further features and advantages of the present invention, as well as the structure and operation of various embodiments of the present invention, are described in detail below with reference to the accompanying drawings and claims. In the drawings, like reference numbers indicate identical or functionally similar elements. [Brief explanation of the drawings]

[0016] The invention will be described in more detail in the following detailed description of the invention, taken in conjunction with the accompanying drawings, which form a part hereof.

[0017] [Figure 1] FIG. 1 shows a block diagram of a clinical diagnostic analyzer system having multiple clinical diagnostic analyzers in communication with a server over a network, according to an exemplary embodiment of the present invention.

[0018] [Figure 2]FIG. 2 shows a block diagram of a single clinical diagnostic analyzer of the system of FIG.

[0019] [Figure 3A] FIG. 3A is a diagram of a first exemplary prompt screen presented by the clinical diagnostic analyzer of FIG.

[0020] [Figure 3B] FIG. 3B is a diagram of a second exemplary prompt screen presented by the clinical diagnostic analyzer of FIG.

[0021] [Figure 3C] 3C is a diagram of a third exemplary prompt screen presented by the clinical diagnostic analyzer of FIG. 2.

[0022] [Figure 3D] 3D is a diagram of a fourth exemplary prompt screen presented by the clinical diagnostic analyzer of FIG. 2.

[0023] [Figure 4] FIG. 4 is a flow diagram of an exemplary method for performing a customized automated crossover test.

[0024] [Figure 5] FIG. 5 is a flow diagram of an exemplary method for calculating an optimal value for a customized sample number for use when performing a customized automated crossover test according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0025] Systems and methods for performing customized automated crossover testing in a clinical diagnostic analyzer or fleet of clinical diagnostic analyzers are described herein in accordance with exemplary embodiments of the present invention. While the present invention will be described in detail below with reference to illustrative and alternative embodiments, it will be understood that the present invention is not limited to the specific configurations shown and described in these embodiments. Rather, those skilled in the art will recognize that a variety of configurations may be implemented in accordance with the present invention.

[0026] Referring initially to Figure 1, a clinical diagnostic system in accordance with an exemplary embodiment of the present invention is generally designated by the numeral 100. System 100 generally includes a plurality of clinical diagnostic analyzers 110a, 110b, 110c, 110n and a server 112 in communication with a database 114. The plurality of clinical diagnostic analyzers 110a, 110b, 110c, 110n: Communication with network 116 facilitates the transmission of commands, information, and data between each clinical diagnostic analyzer 110a, 110b, 110c, 110n and server 112, and between each clinical diagnostic analyzer 110a, 110b, 110c, 110n and other diagnostic analyzers, or any combination of clinical diagnostic analyzers and / or servers. Herein, clinical diagnostic analyzers are sometimes referred to as analyzers or meters.

[0027] Network 116 may be a local area network (LAN), a wide area network (WAN), an ad hoc network, or any other network configuration known in the art, or a combination thereof. For example, in the exemplary embodiment shown in Figure 1, network 116 may include a LAN that enables communication between clinical diagnostic analyzers 110a, 110b, 110c, 110n, such as in a single laboratory environment having multiple clinical diagnostic analyzers. It may also include a WAN, such as the Internet or other wide area network, that enables communication between the LAN and server 112 and / or between the clinical diagnostic analyzers and the server.

[0028] It should be understood that the configuration shown in FIG. 1 is exemplary and not limiting, and that the inventions described herein may be practiced in a single clinical diagnostic analyzer, in a group of clinical diagnostic analyzers co-located in a single laboratory or facility, and in a group of geographically dispersed clinical diagnostic analyzers.

[0029] For example, multiple systems 100, each including one or more clinical diagnostic analyzers and a server, may be located at a single laboratory, or at multiple laboratories distributed across a facility or the world, all of which may communicate over a WAN. It is further understood that the present invention may be embodied in a single clinical diagnostic analyzer, or in a collection of clinical diagnostic analyzers that communicate with each other over a LAN or WAN without going through a server(s). These and other variations and embodiments will be apparent to those skilled in the art.

[0030] Server 112 preferably includes processor 118, memory 120, and logic and control circuitry 122, all of which are in communication with each other. Server 112 may be any server, server system, computer, or computer system known in the art and is preferably configured to communicate instructions and data between server 112 and a network and / or any devices connected to the network, and to store and retrieve data and information from database 114. Processor 118 may be any microprocessor, controller, or multiple such devices known in the art. Processor 118 preferably runs a server operating system, such as a Linux-based, Windows-based, or other server operating system known in the art. Preferably, processor 118 is configured in conjunction with the operating system to control the operation of server 112, thereby enabling the server to communicate with database 114 and network 116 and / or devices connected to the network, such as clinical diagnostic analyzers 110a, 110b, 110c, and 110n. In some embodiments, the server may control the operation of the clinical diagnostic analyzers. For example, it may authorize operation of the analytical devices for a particular period of time, collect data from the analytical devices for recording in the database 114, transfer data to the analytical devices for viewing and / or analysis, collect test data from the analytical devices, and provide data, instructions, or prompts to the analytical devices individually or in groups.

[0031] Memory 120, which may be volatile or non-volatile, is used to store data and information related to the operation of the server, as well as data for transmission to and from the server. For example, the memory may store a server operating system executed by processor 118. It may also store data related to clinical diagnostic analyzers 110a, 110b, 110c, 110n, which communicate with server 112 via network 116. In some embodiments, memory 120 on the server may supplement or replace database 114.

[0032] Database 114 is preferably used to record control information related to the operation of server 112 and the operation and control of clinical diagnostic analyzers 110a, 110b, 110c, 110n, and may also be used to record data related to the processing of samples by the clinical diagnostic analyzers. For example, the database can include instructions or programming executed by a processor on the clinical diagnostic analyzer or executed on the server, or can store data related to the samples themselves, such as the number of samples processed, frequency of testing, results of analyses performed on the analyzer, and tracking information, lot number, sample size, sample weight, percentage of sample remaining, etc. Preferably, database 114 includes non-volatile storage, such as a hard drive, solid-state memory, or a combination thereof.

[0033] The logic and control circuitry 122 provides interface circuitry that allows the processor and memory to communicate and provides other operational functions for the server, such as facilitating data communication to and from the network 116 .

[0034] Referring to Figure 2, there is shown a detailed diagram of a single clinical diagnostic analyzer 110a of the system of Figure 1. Clinical diagnostic analyzer 110a preferably includes a processor 124, a memory device 126, measurement hardware 128, and an input panel / display 130.

[0035] The processor 124 may be any controller, microcontroller, or microprocessor known in the art. The processor 124 communicates with a memory device 126 that stores instructions executed by the processor to control and communicate with the measurement hardware 128 and the input panel / display 130, thereby causing the clinical diagnostic analyzer to perform desired steps. Desired steps may include instructing the measurement hardware to load a test specimen or run a test on a loaded sample, or instructing or prompting a user to perform a particular operation, such as replacing a test sample, initiating a test, or viewing collected data. The processor 124 may also execute instructions to receive data from the measurement hardware 128, perform one or more analyses on the received data, and display test results or other information on the input panel / display panel 130.

[0036] The measurement hardware 128 preferably includes a sample receptacle configured to accept one or more samples into the analytical device for testing. Preferably, the measurement hardware is configured to receive samples or specimens stored in vials. Most preferably, it is configured to receive multiple vials and extract samples or analytes from any desired specimen vial for testing and analysis. In further embodiments, the measurement hardware 128 can include an external turntable, loader, or other mechanism to facilitate sample loading and unloading and allow samples to be loaded under command of the analytical device.

[0037] As shown in FIG. 2, the measurement hardware is configured for use with material samples 132a, 132b, 132c, and 132d, which may be QC material, patient test specimens, or other specimens known in the art. In one embodiment, the material samples are contained in vials, which are loaded or inserted into the clinical diagnostic analyzer 110a by a user. Samples can be loaded individually or in groups, for example, into trays that are loaded into the analyzer. In another embodiment, samples can be loaded using an automated loading mechanism, such as a turntable, in response to commands from the analyzer 110a. Material samples in the form of QC material are typically provided in lots, with lots of essentially identical samples derived from the same batch source of material being assigned a unique lot number. The analyzer 110a preferably allows the user to input information related to the QC material, including statistical information such as the mean or standard deviation for that material lot. In other embodiments, information can be retrieved over a network or from a server, for example, using a QR code on the sample vial or container, to uniquely identify the sample or lot.

[0038] The input panel / display 130 is operable to communicate with the processor, present controls to facilitate operation of the analytical device, present prompts and instructions to the user, and receive input commands and / or data from the user. The input panel / display 130 is preferably a touchscreen capable of displaying text and graphics as well as icons, push buttons, a keyboard, etc., for presenting data to the user and receiving input from the user of the analytical device. Preferably, the input panel / display 130 includes an audible alarm device, such as a buzzer or beeper.

[0039] See Figures 3A, 3B, 3C, and 3D. The input panel / display may present prompts to the user, for example, to load a QC sample and press a READY button when complete (Figure 3A), to begin an analysis (Figure 3B), to load a patient sample (Figure 3C), or to select another desired function, such as reviewing data, recording data, or running an analysis (Figure 3D). It will be understood that the clinical diagnostic analyzer 100a may have multiple programs and functions available, and menus or selection prompts are preferably presented to guide the user through the operation of the analyzer and the selection of desired functions and operations.

[0040] The clinical diagnostic analyzer 110a may be any type of analyzer known in the art, such as a biochemistry analyzer, hematology analyzer, immunoassay analyzer, or other clinical diagnostic analyzer known in the art. Preferably, the analyzer 110a is configured to allow a user to determine the accuracy of the analyzer by testing quality control materials of known properties and to assure the user that the analyzer is operating within acceptable error margins. The clinical diagnostic analyzer 110a may be configured for use with a variety of quality control materials, whether in liquid or lyophilized form, and may be configured for use in immunoassays, serum chemistry, immunology, hematology, and other fields.

[0041] 1 through 3 combined, in a typical use case when performing tests on patient samples, the analyzer 110a prompts the user to load the patient sample, as shown in FIG. 3C, and then, once the sample is loaded, prompts the user to run the analysis, as shown in FIG. 3B. Once the test is complete, the analyzer may prompt the user to record or review data, as shown in FIG. 3D. Similarly, the analyzer may prompt the user to begin a QC material test, as shown in FIG. 3A.

[0042] It should be understood that operations of analytical device 100a may be performed locally on the analytical device, or, if the analytical device is operating in system 100 as shown in Figure 1, operations may be coordinated by server 112. It should further be understood that any data may be stored locally on analytical device 110a, on server 112, or on database 114, or may be made available throughout system 100 and via network 116 so that remote servers and analytical devices can similarly access the stored data. Similarly, analyses may be performed on the analytical device itself, on a server, or distributed among multiple analytical devices and / or servers.

[0043] In embodiments of the invention described herein, analyses performed and data collected on multiple analytical devices can be combined and analyzed to provide an output or result based on data collected across multiple analytical devices.

[0044] Known methods for conducting crossover testing rely on collecting and analyzing data over a relatively long period of time (typically at least 20 days), collecting a data point on each of these days, and analyzing the data once all data has been collected. In contrast, the systems and methods of the present invention calculate the optimal number of data points / samples required to achieve the desired accuracy without requiring the collection of a fixed number of data points over a long and / or fixed period of time. The systems and methods can also perform customized automated crossover testing, in which a QC crossover test using the optimal number of data points / samples to achieve the desired accuracy is performed.

[0045] Thus, the systems and methods of the present invention provide an improvement in the field of clinical diagnostic processes by requiring testing of only an optimal number of samples, avoiding the cost, effort, and expense that can be incurred when performing testing using the traditional 20-day sample and data analysis process, and allowing that optimal number of samples to be used when performing customized automated crossover testing.

[0046] Having described the clinical diagnostic analyzer and system configuration, a system and method for performing customized automated crossover testing in accordance with an embodiment of the present invention will now be described.

[0047] As noted above, in one embodiment, performing a customized crossover study in accordance with the present invention involves determining the optimal number of samples required to achieve the desired precision for a new lot of control material. Because clinical diagnostic analyzers used to analyze test and patient samples require calibration and validation, until the laboratory can confirm the parameters of the new control material, it cannot be certain of the accuracy of the results of the analysis performed on actual samples.

[0048] To determine the optimal number of samples to perform on a new QC material according to an exemplary embodiment of the present invention, one generally proceeds as follows.

[0049] First, an estimate of the historical relationship between an instrument and its peer group is determined. Next, a new control mean is estimated based on the historical relationship between the instrument and its peer group and new control peer data, and a reliability coefficient for the new control mean is also estimated. Next, the estimated new control mean, reliability coefficient, and instrument variability are used to determine the variability of the new control mean due to sample size. Thereafter, a customized sample size that meets the desired performance criteria can be selected when conducting a crossover study.

[0050] Having described the general steps of the method for determining a customized sample size for conducting an automated crossover study, the calculations for making that determination will now be described with reference to the flow diagram in Figure 4. In block 400, parameters for the calculation are defined and initialized as follows:

[0051] L ij : the QC mean of the ith instrument in a peer group at the jth QC lot and concentration level, where

[0052] i = 1,…,I (number of instruments in the peer group)

[0053] j = 1,…,J i (Total number of QC lots and levels for the i-th instrument in the peer group)

[0054] G j is the QC peer group mean for the jth QC lot and level.

[0055] With these initial parameters set, in block 402, an estimate of the historical relationship between an instrument and its peer group is determined by defining a linear model for the relationship between an individual instrument's QC average and the peer group average, as follows:

[0056] L ij =α i +β i G j +e ij

[0057] where α i and β i defines the linear relationship between the QC mean of an instrument and the peer group mean. ij is a random deviation in this relationship,

[0058]

number

[0059] Next, for all i, α i =0 and f(G) = G 2 Assuming this, the model becomes:

[0060]

number

[0061]

number

[0062] Here, β i defines the proportional relationship between the instrument's QC mean and the peer group mean, such that random deviations in that relationship are proportional to the peer group mean.

[0063] Y ij =L (ij) / G j If we define it as:

number

[0064] Then the proportional relationship between the instrument and the group mean is

[0065]

number

[0066] Then, in block 404: The expectation and variance of JPEG2025528710000007.jpg73 are

[0067]

number

[0068] Therefore, the variance of the proportional relationship between the i-th instrument and the peer group mean is

[0069]

number

[0070] In block 406, an estimate of the variance of the new control mean is determined. Assuming the variance of the proportional relationship between the instrument and the peer group mean is the same for all instruments, the pooled estimate of variance is:

number

[0071] Estimating QC material for a new lot of an instrument is based on the historical relationship of the instrument to its peer group and measurements of the new QC lot, and requires determining the following at each concentration level for the new lot:

[0072] is an estimate of the historical proportionality between the i-th laboratory's QC lot means and its peer group means. Given JPEG2025528710000011.jpg73, the peer group mean G for a new QC lot and density level l new,l , and {x ilq}, where q=1,…,Q l is the QC concentration level l at the ith instrument. l Represents measurements from new QC lots.

[0073]

number

[0074] The initial estimate of the new QC lot / level for device i is Define it as JPEG2025528710000013.jpg1022.

[0075] Define JPEG2025528710000014.jpg1037. k il The estimate of is derived as follows:

[0076]

number

[0077]

number

[0078]

number

[0079] Therefore, in block 408, the customized sample size of the instrument is calculated using the formula

[0080]

number

[0081] It can be determined using k il The estimate of is

[0082]

number

[0083] Using these equations described here, a customized sample replicate size can be determined using simulations to determine how many sample replicates are needed to keep the increase in probability of false rejection due to mean uncertainty below a specified threshold, such as 0.015.

[0084] First, calculate the false rejection probability using:

[0085] Φ = normal cumulative distribution function (Z, mean, SD), and

[0086] Pfr(average, SD)=1-Φ(3, average, SD)-Φ(-3, average, SD)

[0087] The baseline false rejection probability (Pfr0) is calculated from the instrument mean and SD as follows:

[0088] Pfr0 = Pfr(instrument mean, instrument SD) It is calculated as follows.

[0089] For a given CV and k, we simulate the false rejection probability for a given mean calculated from q samples:

[0090] r0 = Random sampling from normal distribution N(0,1)

[0091] r1 = Random sampling from normal distribution N(0,1)

[0092] w0= 1 / (1+q*k^2)

[0093] minst = instrument average

[0094] sinst=CV*minst

[0095] Simulated mean (smean) = minst+w0*r0*k*sinst+((1-w0)*r1*sinst) / √q

[0096] ssd=smean*CV

[0097] Simulated Pfr = Pfr(smean,ssd)

[0098] Next, starting from one sample (q=1), we simulate 10,000 Pfrs.

[0099] sPfr = 80th percentile of 10,000 simulated Pfrs Let's say.

[0100] If sPfr≦Pfr0+threshold, then q samples are needed.

[0101] If sPfr>Pfr0+threshold, increase q by 1 and calculate a new sPfr.

[0102] Continue increasing q until sPfr≦Pfr0+threshold or q=10.

[0103] The number of sample replicates q (sPfr≦Pfr0+threshold) is the number of samples required for the customized virtual crossover test.

[0104] It should be appreciated that by collecting historical instrument, peer group, and calculation data as described above, the approach described above can be used to estimate k over a range of analytes and peer groups. It is further appreciated that the distribution of k estimates can be used to determine the applicability and validity of the estimate in reducing sample size requirements for new QC lots and in establishing new QC lot means for a given instrument.

[0105] Using the above method for determining customized optimum values ​​to use for QC materials for a new lot of instruments, an exemplary method for performing customized automated crossover testing of an instrument according to an exemplary embodiment of the present invention will now be described with reference to FIG.

[0106] Referring to the flow diagram of Figure 5, the customized automated crossover test begins at block 500. In block 500, the customized QC crossover test plan, including the number of samples required for each QC item to be evaluated and the samples per day, is loaded or entered into the instrument and / or server for execution on the instrument.

[0107] The required QC materials are loaded into the analyzer / meter in block 502. As mentioned above, the materials can be loaded manually, but in some cases the meter and / or testing laboratory may have an automated system for loading the materials.

[0108] At block 504, the number of samples to be examined and the number of samples to examine today are set to initial values.

[0109] At block 506, an automatic crossover test is initiated using the customized optimal number of samples as previously determined.

[0110] Testing continues until the desired number of samples has been tested at block 508. If the number of samples tested has not been reached, one QC sample is loaded and tested, and the count of the number of samples already tested is incremented at block 510. The process of testing and loading samples is repeated until the desired number of samples has been tested.

[0111] At block 512, once the optimal number of samples has been tested, the crossover test is completed, the results are reported, the results are analyzed and recorded, and alerts may be generated based on the results.

[0112] It will be appreciated that the systems and methods of the present invention described herein with respect to the exemplary embodiments provide for determining the optimal number of samples / data points for conducting a crossover study, saving cost, time, and effort compared to crossover studies employing a fixed sample number as known in the prior art.

[0113] While the invention has been described and illustrated herein with reference to various exemplary embodiments, it should be understood that various changes can be made to these embodiments without departing from the scope of the invention. Accordingly, the invention is not limited to the exemplary embodiments described and illustrated herein, except insofar as such limitations are included in the scope of the following claims.

Claims

1. 1. A clinical diagnostic analyzer for performing a customized automated crossover test, comprising: a processor; measurement hardware in communication with the processor and configured to measure a property of the analyte; A memory device storing executable instructions that, when executed by the processor, cause the clinical diagnostic analyzer to: Determining the number of customized samples required to achieve the desired accuracy for new lots of quality control material; analyzing a number of QC specimens corresponding to the customized number of samples over a period of time; recording the results for each of the QC samples analyzed; and Loading and testing analytes from patient specimens with said results recorded. a memory device that executes a process including 1. A clinical diagnostic analyzer comprising:

2. Determining the customized number of samples required to achieve a desired accuracy includes estimating a historical relationship between the clinical diagnostic analyzer and a peer group. The clinical diagnostic analyzer of claim 1 .

3. and estimating a new control mean based on the estimated historical relationship. The clinical diagnostic analyzer of claim 2 .

4. and calculating a confidence coefficient based on the estimated historical relationship. The clinical diagnostic analyzer of claim 3 .

5. determining the variability of the new control mean. The clinical diagnostic analyzer of claim 3 .

6. further comprising an input panel and display operable to present information and data from said processor to a user and to accept inputs and selections from the user; The clinical diagnostic analyzer of claim 1 .

7. The memory device, when executed, causes the clinical diagnostic analyzer to: presenting a prompt to a user on the input panel and display to load a QC sample into the measurement hardware; and receiving input from the user indicating that the analyte has been loaded; 7. The clinical diagnostic analyzer of claim 6, further comprising instructions to:

8. 1. A system for performing customized automated crossover testing, comprising: a server having a processor, a memory, and a database; a plurality of clinical diagnostic analyzers in communication with the server; Equipped with each of the plurality of clinical diagnostic analyzers a processor; measurement hardware in communication with the processor and configured to measure a property of the analyte; a memory device storing executable instructions that, when executed by the processor, cause the clinical diagnostic analyzer to perform processes including loading a specimen from a new lot of quality control material into the measurement hardware; and Equipped with The memory of the server, when executed by the processor of the server, causes the server to: Determining the number of customized samples required to achieve the desired accuracy for a new lot of quality control material; analyzing a number of QC specimens corresponding to the customized number of samples over a period of time; recording the results for each of the QC samples analyzed; and loading and testing analytes from patient specimens with recorded results; storing executable instructions for performing operations including system.

9. 10. The system of claim 8, wherein the memory of the server includes instructions that, when executed, further cause the server to perform operations including estimating a historical relationship between the clinical diagnostic analyzer and a peer group.

10. 10. The system of claim 8, wherein the memory of the server includes instructions that, when executed, cause the server to perform further operations including prompting one or more users of the plurality of clinical diagnostic analyzers to load and test analytes from patient samples.

11. each of the plurality of clinical diagnostic analyzers includes an input panel and a display; The memory of the server, when executed, causes the server to: sending instructions to at least one of the plurality of clinical diagnostic analyzers to present on the input panel and display a prompt to a user to load an analyte into the measurement hardware; and receiving input from the user of at least one of the plurality of clinical diagnostic analyzers indicating that the analyte has been loaded; The system of claim 8 , further comprising instructions to perform operations including:

12. 1. A method for performing a customized automated crossover test, comprising: Determining the number of customized samples required to achieve the desired accuracy for a new lot of quality control material; loading a sample of the new lot of quality control material into measurement hardware of a clinical diagnostic analyzer; analyzing the sample to obtain a data value corresponding to an attribute of the sample; obtaining and recording data values ​​from a number of QC samples corresponding to the customized number of samples; recording the data values ​​obtained for each specimen; Loading and testing analytes from patient specimens using the recorded mean and standard deviation; A method comprising:

13. and estimating a historical relationship between the clinical diagnostic analyzer and a peer group. The method of claim 12.

14. and estimating a new control mean based on the estimated historical relationship. The method of claim 12.

15. and calculating a confidence coefficient based on the estimated historical relationship. The method of claim 12.

16. 1. A clinical diagnostic analyzer for conducting a crossover study, comprising: a processor; measurement hardware in communication with the processor and configured to measure a property of the analyte; A memory device storing executable instructions that, when executed by the processor, cause the clinical diagnostic analyzer to: determining the optimal number of samples required to achieve the desired accuracy for a given lot of quality control material; analyzing a number of QC specimens corresponding to said optimal sample number over a period of time; and loading and testing analytes from patient specimens; A clinical diagnostic analyzer that performs a process including:

17. determining an optimal number of samples necessary to achieve a desired accuracy includes estimating a historical relationship between the clinical diagnostic analyzer and a peer group; 17. The clinical diagnostic analyzer of claim 16.

18. further comprising estimating a new control mean based on the estimated historical relationship.

18. The clinical diagnostic analyzer of claim 17.

19. further comprising calculating a confidence coefficient based on the estimated historical relationship.

20. The clinical diagnostic analyzer of claim 18.

20. determining the variability of the new control mean.

20. The clinical diagnostic analyzer of claim 18.

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