A parathyroid hormone rapid detection and data analysis integrated system

Abstract

This application provides an integrated system for rapid detection and data analysis of parathyroid hormone, relating to the field of intelligent medical technology. The system includes: a detection unit configured to receive a patient's bodily fluid sample and perform rapid quantitative detection of parathyroid hormone, outputting a detection signal within a single detection cycle; an analysis unit configured to receive the detection signal and convert it into corresponding hormone concentration data; and a decision support unit comprising: an individualized baseline module configured to establish an individualized hormone concentration reference baseline for the patient based on at least one historical detection data of the target patient under a specific physiological or pathological state; a dynamic comparison module configured to compare and analyze the trend of real-time hormone concentration data obtained from subsequent detections of the patient with the individualized reference baseline to obtain comparison results; and a diagnostic and treatment assistance prompt module configured to generate prompt information to assist in clinical diagnosis or disease assessment based on the comparison results.

Description

Technical Field

[0001] This invention belongs to the field of intelligent medical technology, specifically relating to an integrated system for rapid detection and data analysis of parathyroid hormone. Background Technology

[0002] Rapid detection technologies for parathyroid hormone (PTH), such as bedside testing, have been widely used in the diagnosis of primary or secondary hyperparathyroidism, immediate assessment of surgical efficacy, and long-term management of patients with chronic kidney disease. Current technologies enable a single test to obtain PTH concentration values ​​within a short time (e.g., 15-30 minutes).

[0003] However, the clinical significance of PTH levels highly depends on their dynamic trends rather than a single absolute value. For example, whether PTH has decreased to the target range post-surgery, or whether the trend of PTH changes during treatment of chronic diseases has met the target, both need to be compared with the patient's specific baseline level. Currently, rapid testing devices only serve as data acquisition terminals, outputting an isolated test value. Clinicians must manually review the patient's historical records, relying on memory or experience to make comparisons and trend judgments, a process that is time-consuming and subject to subjective differences. In long-term follow-ups with complex conditions or numerous data points, doctors find it difficult to quickly and accurately capture key trends, potentially leading to diagnostic delays or insufficient basis for decision-making.

[0004] Therefore, the prominent problem with existing technologies is that rapid testing devices are disconnected from clinical decision support, lack the ability to automatically integrate and intelligently analyze individualized historical data of patients, resulting in low efficiency from obtaining test data to forming clinical insights. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the prior art, an integrated system for rapid detection and data analysis of parathyroid hormone is provided, comprising: The detection unit is configured to receive patient body fluid samples and perform rapid quantitative detection of parathyroid hormone, and output a detection signal within a single detection cycle. An analysis unit, communicatively connected to the detection unit, is configured to receive the detection signal and convert it into corresponding hormone concentration data; A decision support unit, integrated within the analysis unit, includes: The individualized baseline module is configured to establish an individualized hormone concentration reference baseline for a target patient based on at least one historical test data under a specific physiological or pathological condition. The dynamic comparison module is configured to compare and analyze the real-time hormone concentration data obtained from subsequent tests of the patient with the individualized reference baseline to obtain the comparison results. The diagnostic assistance prompt module is configured to generate prompt information to assist in clinical diagnosis or disease assessment based on the comparison results.

[0006] According to the technical solution provided in this application, the detection unit is a rapid detection device based on immunoassay, configured to output or correlate its cross-reactivity characteristic data for different molecular forms of parathyroid hormone.

[0007] According to the technical solution provided in this application, the decision support unit further includes: The detection specificity correction module is configured for: Based on the cross-reactivity characteristic data of the detection unit and at least one key clinical parameter of the target patient for assessing the background of parathyroid hormone metabolism, the hormone concentration data converted by the analysis unit is corrected to obtain corrected concentration data. Specifically, the dynamic comparison module and the diagnostic assistance prompt module are configured to operate based on the corrected concentration data.

[0008] According to the technical solution provided in this application, the decision support unit further includes: The multi-dimensional clinical pathway mapping module is configured for: Receive the comparison results and obtain a comprehensive clinical dataset of the target patient containing key clinical parameters and other multidimensional diagnostic and treatment information; Based on predefined evidence-based medicine rules, the comparison results are fused and analyzed with the comprehensive clinical dataset to generate at least one structured candidate treatment path; The diagnostic assistance prompting module is further configured to output the candidate diagnostic path as part of the prompting information.

[0009] According to the technical solution provided in this application, the dynamic comparison module is further configured to: Identify specific patterns in the changes of hormone concentration data over time in the comparison results; The specific pattern is matched with a predefined pattern library associated with different clinical meanings; The diagnostic and treatment assistance prompt module generates or adjusts the prompt information based on the matched specific pattern and its associated target clinical significance.

[0010] According to the technical solution provided in this application, the predefined pattern library associated with different clinical meanings includes a decline-early rebound pattern; The decline-early rebound pattern is defined as follows: in early monitoring after parathyroidectomy, hormone concentration data first drops sharply from the preoperative baseline to a first threshold within a first predetermined time, and then rebounds from the low point to a second threshold within a second predetermined time. The clinical significance of the decline-early rebound pattern is that it suggests the presence of residual or regenerative diseased parathyroid tissue.

[0011] According to the technical solution provided in this application, the dynamic comparison module is further configured to: When the decline-early rebound pattern is matched, the blood calcium concentration data of the target patient within the second predetermined time period are acquired simultaneously. Analyze the coupling relationship between the upward trend of the hormone concentration data and the changing trend of the blood calcium concentration data; The diagnostic and treatment assistance prompt module is further configured to: based on the coupling relationship, identify the nature or classify the probability of residual or regenerative risk of the diseased parathyroid tissue in the prompt information.

[0012] According to the technical solution provided in this application, the dynamic comparison module is further configured to: Extract dynamic feature parameters from the coupling relationship, including the time delay between the rate of recovery of the hormone concentration data and the rate of change of the blood calcium concentration data, and / or the ratio of the magnitude of change of the two. The dynamic feature parameters are input into a pre-trained evaluation model to output quantitative evaluation parameters that characterize the functional activity intensity or anatomical localization tendency of residual lesion tissue. The diagnostic and treatment assistance prompt module is further configured to generate prompt information containing suggestions for targeted examinations or intervention strategies based on the quantitative assessment parameters.

[0013] According to the technical solution provided in this application, it also includes: Intervention strategy simulation module, configured for: The quantitative assessment parameters are matched with multiple preset risk threshold ranges; Based on the matched risk threshold range, the system calls and combines a set of intervention actions corresponding to that range, which have clear execution priorities and timeliness requirements, from a predefined intervention strategy library. The prompt information specifically includes the set of intervention actions, and marks the priority of each intervention action and the recommended execution time window.

[0014] According to the technical solution provided in this application, the different molecular forms of parathyroid hormone targeted by the cross-reactivity characteristic data include at least the complete parathyroid hormone molecule and one or more carboxyl-terminal fragments.

[0015] Compared with the prior art, the beneficial effects of this application are as follows: I. Automatic fusion of test data and individualized historical data: By integrating the individualized baseline module, the system can automatically establish a unique hormone concentration reference baseline for each patient, interpreting the results of each rapid test in the specific physiological or pathological context of the patient, thus eliminating the over-reliance on non-targeted normal ranges.

[0016] Second, it provides dynamic and intelligent trend analysis: Through the dynamic comparison module, the system automatically compares real-time detection data with individualized baselines and analyzes the changing trends, transforming the original concentration values ​​into clinically relevant comparison results (such as a 50% increase from the baseline with a continuous upward trend). This frees doctors from tedious manual data comparison and trend analysis.

[0017] Third, it improves the efficiency and accuracy of clinical decision-making: The system ultimately outputs prompts that integrate intelligent analysis conclusions, providing direct assistance for diagnosis or disease assessment. This allows clinicians to quickly focus on clinically significant changes, shortening the path from detection to decision-making. It is particularly suitable for chronic disease management or perioperative rapid assessment scenarios that require frequent monitoring and dynamic evaluation, effectively avoiding the risk of misjudgment caused by oversights or delays in manual analysis.

[0018] In summary, this application represents a fundamental leap from single-point numerical reporting to personalized trend intelligent analysis, significantly enhancing the clinical practical value of PTH testing. Attached Figure Description

[0019] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 Provided for this application; The text labels in the image represent: 1. Detection unit; 2. Analysis unit; 3. Decision support unit. Detailed Implementation

[0020] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] As mentioned in the background section, this application proposes an integrated system for rapid detection and data analysis of parathyroid hormone, such as... Figure 1 As shown, it includes: Detection unit 1 is configured to receive patient body fluid samples and perform rapid quantitative detection of parathyroid hormone, and output a detection signal within a single detection cycle. Analysis unit 2 is communicatively connected to detection unit 1 and is configured to receive the detection signal and convert it into corresponding hormone concentration data; Decision support unit 3, integrated into analysis unit 2, includes: The individualized baseline module is configured to establish an individualized hormone concentration reference baseline for a target patient based on at least one historical test data under a specific physiological or pathological condition. The dynamic comparison module is configured to compare and analyze the real-time hormone concentration data obtained from subsequent tests of the patient with the individualized reference baseline to obtain the comparison results. The diagnostic assistance prompt module is configured to generate prompt information to assist in clinical diagnosis or disease assessment based on the comparison results.

[0023] Specifically, the system can be physically embodied as an integrated bedside testing device, or it can consist of a portable testing instrument and a tablet or mobile terminal loaded with dedicated analysis software.

[0024] The detection unit 1 is specifically implemented as a rapid quantitative detection device based on immunological principles, such as a cartridge-type detector using fluorescence immunochromatography or chemiluminescence immunoassay. Internally, it includes a sample well for receiving patient bodily fluid samples (such as whole blood, serum, or plasma), and a reaction membrane or reaction cup pre-embedded with antibodies specific to parathyroid hormone (PTH). When the sample is added, the PTH molecules specifically bind to the labeled antibody, and the intensity of the reaction signal is detected by an optical sensor (such as a fluorescence reader or photomultiplier tube). This signal is the detection signal. The entire process, i.e., a single detection cycle, can typically be completed within 15 minutes.

[0025] The analysis unit 2 can be an embedded microprocessor or a standalone computing device (such as the main control chip of a tablet computer) connected to the detection unit 1. It receives detection signals from the detection unit 1 in real time via wired (e.g., USB) or wireless (e.g., Bluetooth, Wi-Fi) communication. Internally, the analysis unit 2 runs a calibration algorithm that compares and calculates the received raw electrical or optical signals with a pre-stored standard curve, ultimately converting them into hormone concentration data with a defined unit of measurement (e.g., pg / mL).

[0026] Furthermore, the analysis unit 2 internally runs a dedicated concentration calibration program. This program operates based on the following principles and steps: Before the device leaves the factory or when the reagent kit batch is used, a series of parathyroid hormone (PTH) standards with known precise concentrations are used for calibration tests to establish a corresponding functional relationship between the detection signal intensity (such as fluorescence value, luminescence value, or current value) and PTH concentration. This functional relationship is stored in the device in the form of a data table or mathematical formula, called a standard curve. During each test, after receiving the raw signal value from the patient sample from the detection unit 1, the analysis unit 2 calls up this standard curve and maps the raw signal value to the corresponding hormone concentration value through interpolation or formula calculation. For example, if the standard curve is a linear function: concentration = a × signal value + b, the program will substitute the measured signal value into the formula to directly calculate the concentration data in pg / mL.

[0027] Decision support unit 3 is physically integrated into the software layer of analysis unit 2, running as a core functional module of a dedicated application. It comprises three cooperating sub-modules.

[0028] The personalized baseline module establishes a personalized hormone concentration reference baseline for each patient. The process is as follows: When the system performs its first test on a target patient, it creates or associates the patient's unique electronic record. After the test, the module prompts the operator (e.g., a nurse or doctor) to confirm or enter a specific physiological or pathological status label corresponding to this test, such as "preoperative baseline," "maintenance phase of chronic kidney disease," or "stable phase after drug treatment." The module stores the hormone concentration data obtained from this test, along with the status label, as historical data. When a reference baseline needs to be established, the module retrieves at least one historical test result for the patient under the same specific status label. This baseline can be a single value or an average or statistical range calculated from multiple tests. Crucially, this baseline is derived from the patient's own data, not from the statistical range of healthy individuals, thus making it personalized.

[0029] The dynamic comparison module performs dynamic comparisons and trend analysis. The process is as follows: when the patient undergoes further testing, the module automatically retrieves the previously established individualized reference baseline that best matches the current clinical situation. For example, during post-parathyroidectomy monitoring, the pre-operative baseline is automatically retrieved. The module compares the real-time acquired hormone concentration data with this baseline, calculating the percentage change (e.g., a 75% decrease from the baseline). Simultaneously, if multiple post-operative test data points exist, the module also analyzes the trends of these data points over time, such as using linear fitting to determine whether it is a "continuous decline," a "plateau," or a "start to rise." The numerical results of the comparison and the trend assessment together constitute the comparison result.

[0030] The diagnostic assistance prompt module generates prompt messages. Its implementation process is as follows: This module has multiple pre-set prompt templates, which are associated with different comparison result modes. For example, when receiving a comparison result of "a decrease of >50% from preoperative baseline," the module will call the "good surgical response" template; when receiving a result of "the trend has changed from declining to plateauing or rebounding," it will call the "beware of rebound" template. The module fills the specific numerical values ​​and trend keywords into the templates, generating prompt messages in natural language descriptions such as "This test value is 85% lower than the preoperative baseline, indicating a good surgical response" or "PTH levels have shown an upward trend within 24 hours post-surgery; please pay attention," and outputs them through the device's screen display or voice broadcast, thus providing direct assistance to doctors in rapid diagnosis or disease assessment.

[0031] This implementation method deeply integrates rapid testing hardware with intelligent analysis software, solving the problem of rapid testing devices being limited to single functions and providing only isolated data points. Its core technical principle lies in personalized dynamic reference and automated analysis output. By establishing a reference baseline for each patient under specific conditions, new test values ​​are interpreted within a highly personal context, which is more accurate than using a fixed population reference range. By automatically performing comparisons, trend analysis, and generating text prompts, doctors are freed from the tedious work of manually reviewing historical records, mentally calculating percentages, and subjectively judging trends, achieving instantaneous transformation from raw data to clinical insights. The ultimate technical effect is a significant improvement in the efficiency and accuracy of clinical decision support for bedside PTH testing.

[0032] In a preferred embodiment, the detection unit 1 is a rapid detection device based on immunoassay, configured to output or correlate its cross-reactivity characteristic data for different molecular forms of parathyroid hormone.

[0033] Specifically, detection unit 1 is implemented as a rapid detection device based on immunoassay. This clarifies that its core detection technology is immunoassay, such as a fluorescence immunochromatographic detection platform using a double-antibody sandwich method. The antibody pairs coated in the detection cartridges or reagent chips used in this platform are screened to specifically recognize specific antigenic epitopes of parathyroid hormone molecules. The detection device is configured to output or correlate its cross-reactivity characteristic data for different molecular forms of parathyroid hormone. Here, cross-reactivity characteristic data refers to data that quantitatively describes the degree of reaction of the antibodies used in the detection device to other relevant molecular forms besides the target analyte (usually the fully bioactive PTH, i.e., PTH(1-84)). In specific implementations, output or correlation can be achieved through the following methods: Firstly, the direct output method. After each test, in addition to outputting the main PTH concentration value, the analysis software or reporting system of the detection device can simultaneously list a concise characteristic data table, such as noting that "the recovery rate of PTH(1-84) in this test is 100%, the cross-reactivity rate of common carboxyl-terminal fragments PTH(34-84) is <5%, and the cross-reactivity rate of fragment PTH(7-84) is about 60%".

[0034] Secondly, indirect association. A more common implementation is to associate the feature data as an inherent attribute of the batch number of the detection device or accompanying reagents. For example, each batch of test kits is accompanied by a unique QR code or serial number. This QR code is linked to an entry in the manufacturer's cloud database, which records in detail the precise cross-reactivity data of the antibodies used in that batch of reagents against various PTH molecular forms (such as PTH(1-84), PTH(7-84), PTH(34-84), PTH(39-84), etc.). Analysis unit 2 automatically reads this QR code during operation and downloads the corresponding feature data from local cache or the cloud for subsequent module calls. Alternatively, this feature data can also be pre-stored in the micro-storage chip built into the test kit for analysis unit 2 to read.

[0035] This implementation addresses the industry pain points of poor comparability of results between different PTH testing methodologies and the potential for confusion in clinical interpretation. Its technical principle lies in digitizing and making readable the fingerprint information of the testing method, namely its cross-reactivity characteristics. Traditionally, this data exists only in reagent instructions or academic literature, remaining invisible at the moment of clinical testing, and inaccessible to physicians. This solution uses technical means to make these key methodological parameters available and transferable. This lays an indispensable foundation for subsequent data correction, comparability analysis of test results at different times, and accurate understanding of the biological significance of test values.

[0036] In a preferred embodiment, the decision support unit 3 further includes: The detection specificity correction module is configured for: Based on the cross-reactivity characteristic data of the detection unit 1 and at least one key clinical parameter of the target patient for assessing the background of parathyroid hormone metabolism, the hormone concentration data converted by the analysis unit 2 is corrected to obtain the corrected concentration data. Specifically, the dynamic comparison module and the diagnostic assistance prompt module are configured to operate based on the corrected concentration data.

[0037] Specifically, the detection-specific correction module is a software algorithm module whose operation relies on cross-reactivity characteristic data and key clinical parameters of the target patient. Key clinical parameters here refer specifically to critical physiological or pathological indicators that directly affect the metabolic clearance of parathyroid hormone and its fragments in the body. The most crucial of these is renal function indicators, such as estimated glomerular filtration rate (eGFR) or serum creatinine (Cr) values. Other parameters may include the type of underlying disease (primary / secondary hyperparathyroidism), whether the patient is pregnant, and other special physiological states.

[0038] The detailed implementation steps for this module are as follows: The first step is data acquisition. When the module is triggered (for example, each time new raw hormone concentration data is obtained), it will acquire two types of information simultaneously: 1) read the cross-reactivity characteristic data used in the current test from detection unit 1 or related information; 2) read the key clinical parameters of the target patient, including at least their current renal function status, through manual input, retrieval from the hospital information system (HIS / LIS) via the device interface, or read from the patient's electronic record.

[0039] The second step is the correction calculation. The module has a built-in correction algorithm. The core logic of this algorithm is to assess and subtract the artificially inflated effect of inactive PTH fragments on the current detection value. For example, it is known that the current detection method has a 60% cross-reactivity to C-terminal fragments. For patients with normal renal function, these fragments are rapidly cleared, and their concentration in the blood is very low, so the interference is negligible, and the correction coefficient is close to 1.0. However, for patients with severe renal insufficiency (e.g., eGFR < 15 mL / min), these fragments accumulate in large quantities. The correction algorithm estimates the approximate level of fragment accumulation based on the patient's eGFR value, and then, combined with the cross-reactivity data, calculates a correction coefficient greater than 1.0 (e.g., 1.3). Then, the original hormone concentration data obtained from analysis unit 2 is divided by this correction coefficient (or calculated using other mathematical models) to obtain a corrected concentration data that more closely approximates the true biologically active PTH level.

[0040] The third step is data transfer. After calibration, the module outputs the calibrated concentration data, replacing the original hormone concentration data, and sends it to the subsequent dynamic comparison module and diagnostic assistance prompt module. This means that all subsequent individualized comparisons, trend analyses, and prompt generation in the system will be based on this more accurate data, which has been calibrated according to methodology and individual metabolic background.

[0041] This implementation method directly addresses the specific problem of clinical misinterpretation risks caused by differences in testing methods and fragment interference, particularly in patients with renal insufficiency, in PTH testing. Its technical principle is dynamic two-factor correction: first, correcting for inherent biases in the testing method itself (through cross-reactivity data); and second, correcting for the influence of the patient's individual pathophysiological state on the composition of the analyte (through key clinical parameters). Software algorithms quantify these two factors and apply them to the raw data, essentially standardizing the test values ​​and striving to restore the portion reflecting true biological activity. This overcomes the difficulty of directly comparing results between different testing platforms and eliminates the misinterpretation of results from the same method due to differences in patient renal function. The ultimate technical effect is that regardless of the testing equipment used or the patient's renal function, the system can output a standardized PTH concentration value that is more clinically comparable and consistent in interpretation, greatly improving the consistency of data and the accuracy of decision-making in long-term follow-up and cross-center diagnosis and treatment.

[0042] In a preferred embodiment, the decision support unit 3 further includes: The multi-dimensional clinical pathway mapping module is configured for: Receive the comparison results and obtain a comprehensive clinical dataset of the target patient containing key clinical parameters and other multidimensional diagnostic and treatment information; Based on predefined evidence-based medicine rules, the comparison results are fused and analyzed with the comprehensive clinical dataset to generate at least one structured candidate treatment path; The diagnostic assistance prompting module is further configured to output the candidate diagnostic path as part of the prompting information.

[0043] Specifically, the multidimensional clinical pathway mapping module is a complex clinical decision support software engine. Its implementation first requires defining a comprehensive clinical dataset. This dataset extends key clinical parameters and aims to construct a comprehensive diagnostic and treatment profile for the target patient. In addition to key parameters such as eGFR, it should systematically incorporate other multidimensional diagnostic and treatment information, such as: current and past medication history (especially the type, dosage, and duration of use of active vitamin D, calcimidines, and phosphate binders); known complications (such as cardiovascular disease, osteoporosis, and renal osteodystrophy); relevant imaging examination history and results (such as the location and size of the parathyroid gland indicated by ultrasound and isotope scanning); past surgical history; and other relevant recent biochemical indicators (such as serum calcium, serum phosphorus, alkaline phosphatase, and 25-hydroxyvitamin D levels). This data can be quickly entered by physicians through a structured input interface or automatically integrated from the hospital's electronic medical record (EMR) system via standard interfaces (such as HL7 and FHIR).

[0044] The specific workflow of this module is as follows: The first step is information integration. The module receives the comparison results from the dynamic comparison module (e.g., PTH increased by 50% from the individualized baseline, showing a continuous upward trend), and simultaneously initiates the acquisition and updating of the comprehensive clinical dataset for the target patient.

[0045] The second step is rule matching and fusion analysis. At the core of the module is a predefined evidence-based medicine rule base. This rule base transforms relevant domestic and international clinical practice guidelines (such as the KDIGO guidelines and the consensus on the diagnosis and treatment of hyperparathyroidism) and expert experience into computer-executable logical rules (usually production rules or decision trees in the form of "if...then..."). For example, a rule might be: "If a patient has stage 5 chronic kidney disease, and their adjusted PTH is consistently >600 pg / mL, accompanied by hypercalcemia and / or hyperphosphatemia, and is unresponsive to the maximum tolerated dose of active vitamin D and calcimimetic agents, then the recommended treatment pathway includes: 1. Imaging assessment of parathyroid gland localization; 2. Assessment of the indications and risks of parathyroidectomy (PTX)." The module sends the current comparison results along with the complete comprehensive clinical dataset to this rule base for reasoning. It iterates through all relevant rules, searching for those whose preconditions match the current patient's condition.

[0046] The third step involves generating and outputting candidate treatment paths. The conclusions (suggested treatment steps) from successfully matched rules are extracted, integrated, and formatted by the module into one or more structured candidate treatment paths. Each path is a clear, step-by-step list of action suggestions. For example, for the matching scenario above, the module might generate a path: "Path A (Surgical Intervention Assessment): Step 1, schedule a neck ultrasound and isotope scan; Step 2, request a joint consultation with nephrologists and thyroid surgeons; Step 3, complete the preoperative cardiovascular risk assessment." Finally, the treatment assistance module will include this structured candidate path as supplementary information in the final output prompts for the doctor's reference.

[0047] This implementation addresses the final gap between rapid detection systems and comprehensive clinical decision-making. Its technical principle is contextualized rule-based reasoning. It goes beyond simply reporting changes in hormone levels; instead, it places these changes within the complex context of the patient's overall condition, using coded medical knowledge for automated reasoning. This simulates the decision-making process of a seasoned clinician: combining the latest test results, a comprehensive medical history, and established treatment principles to deduce the most appropriate treatment options. The resulting technical effect is to upgrade the system from an anomaly alarm to a treatment navigator, providing physicians (especially those with less experience) with an evidence-based, personalized framework for clinical action. This helps standardize treatment practices, reduce decision-making oversights, and promote multidisciplinary collaborative treatment approaches.

[0048] In a preferred embodiment, the dynamic comparison module is further configured to: Identify specific patterns in the changes of hormone concentration data over time in the comparison results; The specific pattern is matched with a predefined pattern library associated with different clinical meanings; The diagnostic and treatment assistance prompt module generates or adjusts the prompt information based on the matched specific pattern and its associated target clinical significance.

[0049] Specifically, a specific pattern does not refer to a simple increase or decrease, but rather to a clinically significant trajectory in hormone concentration data over time. These patterns are defined by a set of rules based on time and concentration thresholds. For example, a step-like rise pattern might be defined as follows: after a recorded effective intervention, the concentration initially decreases and stabilizes at a new plateau exceeding time T1, subsequently rising continuously from that plateau above the threshold ΔC1. A resistance plateau pattern might be defined as follows: during continuous treatment, the concentration consistently fluctuates above the upper limit of the target range, and the amplitude of this fluctuation (maximum value - minimum value) is less than a specific percentage of its average. This predefined set of rules constitutes a pattern library, where each rule (i.e., a pattern) is associated with a specific clinical meaning, such as the possibility of treatment escape, indication of drug resistance, or warning of postoperative recurrence risk.

[0050] The specific implementation steps are as follows: The first step is data sequence preparation. After comparing the basic real-time data with the individualized baseline, the module will extract continuous hormone concentration data within a certain time window (e.g., all detection points in the most recent week or within 48 hours after surgery) to form a time series.

[0051] The second step is pattern recognition and matching. The recognition engine running within the module sequentially compares and verifies the aforementioned time series data against each rule in the pattern library. This process is not a simple graphical matching, but rather a verification of logical rules. Taking the step-like leap pattern as an example, the engine first searches for a significant descent point and the subsequent stable segment in the sequence, verifying whether its duration is greater than T1; then it checks whether the data points after the stable segment show a statistically significant upward trend, and whether the cumulative increase exceeds ΔC1. If the sequence data meets all the preset conditions of the rule, it is determined that the specific pattern has been matched.

[0052] The third step involves triggering an adjustment of the prompt. Once a match is successful, the module outputs the identifier of the pattern and its associated target clinical meaning (such as the possibility of treatment escape). The diagnostic and treatment assistance prompt module then receives this result. At this point, the prompt module no longer generates a general prompt based solely on the original percentage change, but instead invokes a dedicated prompt template corresponding to the specific clinical meaning of "the possibility of treatment escape." This template generates more targeted text, such as: "A 'recurrence after treatment' pattern has been detected, indicating a possible escape from the current treatment regimen. It is recommended to assess medication adherence and consider adjusting the treatment plan." This achieves dynamic adjustment of the prompt information based on the pattern matching result.

[0053] This implementation addresses the challenge of traditional monitoring methods that focus only on single-point values ​​or simple trends, failing to intelligently identify complex, atypical, yet clinically significant patterns of change. Its technical principle lies in the formalization and automatic matching of clinical knowledge. It transforms the patterned experience accumulated by senior physicians through long-term practice—experiences with early warning value for dynamic changes in PTH—into precise, computer-executable rule logic. Through automated time-series data scanning and rule matching, the system achieves early and objective identification of these potential risk patterns. The resulting technical effect is to deepen the granularity of data analysis from trend direction to pattern morphology, enabling the system to discover characteristic risk signals hidden within massive amounts of data. This provides earlier and more accurate clinical decision-making warnings, assisting physicians in early intervention and preventing disease progression.

[0054] In a preferred embodiment, the predefined pattern library associated with different clinical meanings includes a decline-early rebound pattern; The decline-early rebound pattern is defined as follows: in early monitoring after parathyroidectomy, hormone concentration data first drops sharply from the preoperative baseline to a first threshold within a first predetermined time, and then rebounds from the low point to a second threshold within a second predetermined time. The clinical significance of the decline-early rebound pattern is that it suggests the presence of residual or regenerative diseased parathyroid tissue.

[0055] Specifically, this model is designed for immediate efficacy assessment and risk warning after parathyroidectomy. Its implementation definition includes three core elements: two predetermined time windows (first predetermined time and second predetermined time) and two change thresholds (first threshold and second threshold). The first predetermined time refers to the maximum permissible time range for reaching the lowest hormone concentration post-surgery, typically within minutes to hours, e.g., defined as "within 2 hours post-surgery." The first threshold refers to the required decrease in hormone concentration from the "preoperative baseline" (i.e., the concentration value at the preoperative stable state stored in the individualized baseline module) within this time, e.g., "a decrease of more than 70%." This defines the quantitative standard for a "sharp decrease." The second predetermined time refers to the subsequent time window for observation starting from the lowest concentration point, used to monitor for possible rebound, e.g., defined as "within 24 to 48 hours post-surgery." The second threshold refers to the minimum increase in hormone concentration from its lowest point within this time window, e.g., "a rebound of more than 10% or an absolute increase exceeding a certain value." This defines the quantitative standard for early rebound.

[0056] In practice, the system is activated during post-parathyroidectomy monitoring. The dynamic comparison module first retrieves the patient's preoperative baseline. Then, it continuously receives real-time postoperative monitoring data and performs the following logical checks: Check if, within a first predetermined time (e.g., 2 hours post-surgery), there exists a detection point whose concentration value decreases by a percentage greater than a first threshold (e.g., 70%) from the pre-operative baseline. If not, the pattern is mismatched.

[0057] If the conditions are met, the system records the concentration at that point as the lowest point and starts timing to enter the second predetermined time window.

[0058] During the second predetermined time period (e.g., 24-48 hours post-surgery), new data points are continuously monitored. If the concentration value of any data point is found to have increased by more than a second threshold (e.g., 10%) from the recorded lowest point, it is immediately identified as a match to the decline-early rebound pattern. Once matched, the clinical significance associated with this pattern, "suggesting the presence of residual or regenerative diseased parathyroid tissue," is activated and transmitted to the alerting module to generate a high-risk warning.

[0059] This implementation addresses a very specific and critical clinical problem: how to objectively and systematically identify high-risk patients with potential surgical incompleteness or recurrence in the very early postoperative period. Its technical principle is a dual-threshold triggering mechanism based on time-series events. It transforms the two core clinical expectations of postoperative PTH "should decrease" and "should not rebound prematurely" into automated judgment rules with clear time boundaries and numerical thresholds. By precisely setting time and amplitude parameters, the system can exclude normal postoperative fluctuations and accurately capture clinically significant abnormal rebound signals. The achieved technical effect is early, automated warning of postoperative recurrence risk, changing the traditional method that relies on single-point measurements within 24 hours postoperatively or subjective judgment by physicians. It provides immediate and objective decision-making basis for possible secondary interventions or enhanced imaging follow-up, contributing to improved long-term surgical outcomes.

[0060] In a preferred embodiment, the dynamic comparison module is further configured to: When the decline-early rebound pattern is matched, the blood calcium concentration data of the target patient within the second predetermined time period are acquired simultaneously. Analyze the coupling relationship between the upward trend of the hormone concentration data and the changing trend of the blood calcium concentration data; The diagnostic and treatment assistance prompt module is further configured to: based on the coupling relationship, identify the nature or classify the probability of residual or regenerative risk of the diseased parathyroid tissue in the prompt information.

[0061] Specifically, the coupling relationship refers to the interaction between the trend of parathyroid hormone (PTH) concentration changes and the trend of concurrently collected serum calcium concentration changes. The physiological principle is that the primary function of biologically active PTH is to increase serum calcium. Therefore, a PTH increase caused by secretion from functional residual or regenerating parathyroid tissue should usually be accompanied by an increase in serum calcium or insensitivity to calcium-lowering therapy (i.e., no decrease in serum calcium). Conversely, a PTH increase caused by surgical trauma, tissue necrosis releasing inactive PTH fragments, or physiological fluctuations may not be accompanied by a corresponding change in serum calcium; in fact, serum calcium may remain normal or decrease due to postoperative calcium supplementation strategies.

[0062] The specific implementation steps are as follows: The first step is synchronous data acquisition. This step is triggered when the dynamic comparison module matches a decline-early rebound pattern. The module immediately attempts to acquire serum calcium concentration data for the same target patient within the same second predetermined time window (i.e., the same period during which PTH rebound is observed). This data can come from: 1) synchronous detection within the same integrated system if it includes a serum calcium detection module; 2) automatic retrieval of serum calcium test results for that period from a connected hospital laboratory information system (LIS); or 3) manual input of recent serum calcium values ​​through the user interface.

[0063] The second step is coupling analysis. The module's built-in analysis logic will compare and analyze the two trend lines. This analysis can be qualitative, such as determining whether the two trends are in the same direction (PTH increases, blood calcium also increases or remains the same), divergent (PTH increases, blood calcium decreases), or unrelated (PTH changes significantly, blood calcium shows no significant change). It can also be semi-quantitative, such as calculating the sign or magnitude of the slope of blood calcium changes during the PTH recovery period.

[0064] The third step is to generate discriminative suggestions. Based on the results of the coupling relationship analysis above, the diagnostic assistance suggestion module identifies the nature or classifies the probability of the given universal risks (residual or recurrent risks). For example: If the analysis result is "PTH rebounds, and blood calcium is simultaneously abnormally elevated or remains high", the message may be upgraded to: "PTH rebound was detected, accompanied by elevated blood calcium, which strongly suggests the presence of functional residual glands. Intervention should be actively considered." If the analysis result is "PTH rebounds, but blood calcium remains normal or continues to decline", the message may be adjusted to: "PTH rebound was detected, but it was not accompanied by typical changes in blood calcium. It may be a transient fluctuation after surgery or no functional tissue release. Short-term follow-up examination is recommended to confirm the trend." This implementation addresses a key clinical question remaining after pattern recognition: what is the pathophysiological significance of detected PTH rebound? Its technical principle lies in "cross-validation using physiological correlation parameters." By introducing blood calcium, which has a strong negative feedback physiological link with PTH, as a reference, the system can differentiate the quality of PTH changes. This is essentially an endogenous, biology-based validation mechanism. The technical effect is to elevate risk warning from quantitative alerts to qualitative identification, greatly improving the clinical relevance and decision-making value of the warning information. It can help doctors distinguish between high-risk situations requiring urgent treatment and benign situations that can be observed, thereby avoiding overtreatment or delays in treatment and achieving more precise postoperative management.

[0065] In a preferred embodiment, the dynamic comparison module is further configured to: Extract dynamic feature parameters from the coupling relationship, including the time delay between the rate of recovery of the hormone concentration data and the rate of change of the blood calcium concentration data, and / or the ratio of the magnitude of change of the two. The dynamic feature parameters are input into a pre-trained evaluation model to output quantitative evaluation parameters that characterize the functional activity intensity or anatomical localization tendency of residual lesion tissue. The diagnostic and treatment assistance prompt module is further configured to generate prompt information containing suggestions for targeted examinations or intervention strategies based on the quantitative assessment parameters.

[0066] Specifically, dynamic characteristic parameters are quantifiable measures extracted from the coupled changes in PTH and blood calcium. In practice, two main types of parameters are extracted: first, the time delay, i.e., the time difference (Δt) between the point when PTH begins to rise and the point when blood calcium begins to show a corresponding change. This has potential significance for determining the location of hormone secretion sources (e.g., the blood calcium response rate may differ between in situ remnants in the neck and distant ectopic remnants). Second, the ratio of change magnitude, for example, calculating the ratio (R = |ΔPTH / ΔCa|) of the increase in PTH within the same observation time window. This ratio may reflect the functional activity intensity of the residual tissue.

[0067] The pre-trained evaluation model is a trained mathematical or machine learning model. Its training data comes from a historical case database containing a large number of cases of postoperative PTH rebound. This database not only records the dynamic sequences of postoperative PTH and serum calcium (used to extract dynamic feature parameters), but more importantly, it includes the final clinical outcomes of these cases (such as the location and size of residual tissue confirmed by a second surgery, or the recurrence status confirmed by long-term follow-up) or authoritative imaging results (such as precise localization by SPECT-CT). By learning the correlation between these dynamic feature parameters and the final outcome through an algorithm, the model is trained to predict output quantitative evaluation parameters based on new input feature parameters.

[0068] The implementation process is as follows: The first step is feature extraction. After completing the coupling relationship analysis, the dynamic comparison module will accurately calculate dynamic feature parameters such as "time delay" and "proportion of change" from the synchronous time-series data of PTH and blood calcium.

[0069] The second step is model evaluation. The module takes these calculated dynamic feature parameters as input variables and feeds them into the loaded "pre-trained evaluation model." This model then runs its internal algorithms (such as regression models, classifiers, or neural networks) to process the data.

[0070] The third step is to generate quantitative output. The model output is "quantitative evaluation parameters". This may be one or more values, such as: 1) functional activity intensity index (e.g., 0-100 points, the higher the score, the stronger the secretory function of the residual tissue); 2) anatomical location tendency probability (e.g., "probability of cervical in situ: 75%, probability of mediastinal ectopic location: 25%").

[0071] The fourth step is to generate precise recommendations. After receiving these quantitative assessment parameters, the diagnostic and treatment assistance module can generate highly targeted prompts. For example: "Quantitative assessment indicates that the residual tissue has strong functional activity (index 85) and is highly likely to be located in the neck (probability 78%). It is recommended to prioritize high-frequency ultrasound and isotope scanning of the neck for localization confirmation." This implementation addresses the problem of how to more precisely and objectively quantify, classify, and locate identified risks. It no longer relies solely on fixed physiological rules, but instead uses historical real-world data to train models, uncovering the complex correlation between dynamic biochemical characteristics and the final clinical reality. The technical effect achieved is the quantification and spatialization of postoperative risk assessment. The system no longer provides qualitative descriptions of "risky" or "high-risk," but rather quantitative indicators of "how high the risk" and "where the risk might be located." This provides clinicians with unprecedented, data-driven decision support for developing highly specific next-step examination strategies (selecting which imaging examinations, focusing on which sites), and predicting the difficulty of intervention, elevating intelligent assisted diagnosis to a new level.

[0072] In a preferred embodiment, it further includes: Intervention strategy simulation module, configured for: The quantitative assessment parameters are matched with multiple preset risk threshold ranges; Based on the matched risk threshold range, the system calls and combines a set of intervention actions corresponding to that range, which have clear execution priorities and timeliness requirements, from a predefined intervention strategy library. The prompt information specifically includes the set of intervention actions, and marks the priority of each intervention action and the recommended execution time window.

[0073] Specifically, implementation relies on two pre-built knowledge bases: multiple risk threshold ranges and a predefined intervention strategy library.

[0074] Risk threshold intervals are discrete classifications of quantitative assessment parameters (such as functional activity intensity index and localization probability). For example, the "functional activity intensity index" is divided into: 0-30 (low risk), 31-70 (medium risk), and 71-100 (high risk). Or, the "probability of localization of neck cancer" is divided into: <50% (low probability), 50%-80% (medium probability), and >80% (high probability). These intervals are set based on historical data review and clinical expert consensus, aiming to transform continuous model outputs into clear clinical risk levels. The predefined intervention strategy library is a structured database of action plans. It pre-sets one or more corresponding sets of intervention actions for each risk threshold interval (or combination of intervals). Each intervention action is a clear clinical operation recommendation, such as "request neck ultrasound examination," "increase the calcimulator dose to XX mg," or "schedule a multidisciplinary consultation." Crucially, each action is assigned an execution priority (e.g., "priority," "simultaneous," "follow-up") and a recommended execution time window (e.g., "within 24 hours," "within 1 week," "at the next follow-up"). The strategy library is built closely according to evidence-based medicine guidelines to ensure that different risk levels correspond to different urgency and intensity of medical responses.

[0075] The implementation process is as follows: The first step is risk matching. The intervention strategy simulation module receives quantitative assessment parameters from a weighted average (e.g., functional activity strength index = 85, in situ probability of neck injury = 78%). The module compares these parameters with pre-stored risk threshold ranges. In the example above, an index of 85 matches the "high-risk" range, and a probability of 78% matches the "medium-probability" range.

[0076] The second step is strategy invocation and combination. Based on the matched risk interval combination (e.g., "high risk" + "moderate probability of neck tendency"), the module retrieves the corresponding "intervention action set" from the intervention strategy library. This set is not a single action, but a logically ordered series of actions. For example, for the above combination, the retrieved action set might include: Action 1 (priority, within 12 hours): Notify the attending physician and complete the neck ultrasound examination request; Action 2 (simultaneous, within 24 hours): Repeat blood calcium, phosphorus, and PTH tests; Action 3 (follow-up, within 1 week): Decide whether to schedule a SPECT-CT or surgical consultation based on the ultrasound results.

[0077] The third step involves generating and outputting structured prompts. The module outputs this curated set of intervention actions, with priorities and time window labels, to the diagnostic and treatment assistance prompt module. Instead of generating narrative text, the prompt module directly displays this structured list as the core content of the prompt information. For example, the device screen might display: "Recommended Intervention Action List: 1. [Priority, within 12 hours] Complete a neck ultrasound examination. 2. [Simultaneous, within 24 hours] Repeat blood calcium, phosphorus, and PTH tests. 3. [Follow-up, within 1 week] Determine the next imaging examination based on the ultrasound results." This implementation addresses the challenge of bridging advanced risk assessment results with specific clinical procedures. It directly transforms abstract, quantified risk scores into a series of standardized operational instructions with clear timing and priorities, using predefined, clinically validated mapping rules. This simulates the standard procedures that experienced clinicians would immediately initiate when faced with a specific risk level. The technical effect is a direct transformation from "what is the risk" to "what should be done now, in what order, and within what timeframe." This significantly reduces the decision-making burden, ensures consistent and standardized responses to different levels of risk, avoids delays or omissions due to differences in physician experience or busy schedules, and improves medical quality and patient safety.

[0078] In a preferred embodiment, the different molecular forms of parathyroid hormone targeted by the cross-reactivity characteristic data include at least the complete parathyroid hormone molecule and one or more carboxyl-terminal fragments.

[0079] Specifically, in the field of parathyroid hormone (PTH) detection, different molecular forms are mainly distinguished based on their peptide chain length and biological activity. The complete PTH molecule typically refers to PTH(1-84) with full biological activity and is the primary target molecule for clinical assessment of parathyroid function. C-terminal fragments, on the other hand, are a class of inactive or minimally active metabolites. Lacking a complete N-terminal structure, they cannot effectively activate PTH receptors, but they are abundant in the blood, especially accumulating significantly in patients with renal insufficiency. The most common and most interfering C-terminal fragments include PTH(7-84), PTH(34-84), and PTH(39-84). Different immunoassay methods exhibit vastly different cross-reactivity to these fragments due to the different antibody recognition epitopes (antigenic determinants) they use.

[0080] In practice, the cross-reactivity characteristic data must encompass a quantitative description of the specific molecular forms mentioned above. This is achieved through the following methods: During the development of detection devices or kits, manufacturers need to use purified standards in various molecular forms (including complete PTH(1-84) and representative carboxyl-terminal fragments such as PTH(7-84)) to conduct rigorous cross-reactivity tests.

[0081] The test will quantify the cross-reactivity of the detection system for each non-target fragment relative to the complete PTH(1-84). For example, the test results may be recorded as: "Detection efficiency for PTH(1-84) is 100%; cross-reactivity for PTH(7-84) is 65%; cross-reactivity for PTH(34-84) is <5%". These test data, targeting specific molecular forms, constitute the core content of the "cross-reactivity characteristic data" of detection unit 1. This data is linked to the device or reagent batch number (e.g., stored in a cloud database and linked via QR code) for use by the detection specificity calibration module in analysis unit 2.

[0082] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. An integrated system for rapid detection and data analysis of parathyroid hormone, characterized in that, include: The detection unit (1) is configured to receive patient body fluid samples and perform rapid quantitative detection of parathyroid hormone, and output a detection signal within a single detection cycle. The analysis unit (2) is communicatively connected to the detection unit (1) and is configured to receive the detection signal and convert it into corresponding hormone concentration data; The decision support unit (3), integrated into the analysis unit (2), includes: The individualized baseline module is configured to establish an individualized hormone concentration reference baseline for a target patient based on at least one historical test data under a specific physiological or pathological condition. The dynamic comparison module is configured to compare and analyze the real-time hormone concentration data obtained from subsequent tests of the patient with the individualized reference baseline to obtain the comparison results. The diagnostic assistance prompt module is configured to generate prompt information to assist in clinical diagnosis or disease assessment based on the comparison results.

2. The integrated system for rapid detection and data analysis of parathyroid hormone according to claim 1, characterized in that, The detection unit (1) is a rapid detection device based on immunoassay, configured to output or correlate its cross-reactivity characteristic data for different molecular forms of parathyroid hormone.

3. The integrated system for rapid detection and data analysis of parathyroid hormone according to claim 2, characterized in that, The decision support unit (3) further includes: The detection specificity correction module is configured for: Based on the cross-reactivity characteristic data of the detection unit (1) and at least one key clinical parameter of the target patient for assessing the background of parathyroid hormone metabolism, the hormone concentration data converted by the analysis unit (2) is corrected to obtain the corrected concentration data. Specifically, the dynamic comparison module and the diagnostic assistance prompt module are configured to operate based on the corrected concentration data.

4. The integrated system for rapid detection and data analysis of parathyroid hormone according to claim 1, characterized in that, The decision support unit (3) further includes: The multi-dimensional clinical pathway mapping module is configured for: Receive the comparison results and obtain a comprehensive clinical dataset of the target patient containing key clinical parameters and other multidimensional diagnostic and treatment information; Based on predefined evidence-based medicine rules, the comparison results are fused and analyzed with the comprehensive clinical dataset to generate at least one structured candidate treatment path; The diagnostic assistance prompting module is further configured to output the candidate diagnostic path as part of the prompting information.

5. The integrated system for rapid detection and data analysis of parathyroid hormone according to claim 1, characterized in that, The dynamic comparison module is further configured to: Identify specific patterns in the changes of hormone concentration data over time in the comparison results; The specific pattern is matched with a predefined pattern library associated with different clinical meanings; The diagnostic and treatment assistance prompt module generates or adjusts the prompt information based on the matched specific pattern and its associated target clinical significance.

6. The integrated system for rapid detection and data analysis of parathyroid hormone according to claim 5, characterized in that, The predefined pattern library, which is associated with different clinical meanings, includes the decline-early rebound pattern; The decline-early rebound pattern is defined as follows: in early monitoring after parathyroidectomy, hormone concentration data first drops sharply from the preoperative baseline to a first threshold within a first predetermined time, and then rebounds from the low point to a second threshold within a second predetermined time. The clinical significance of the decline-early rebound pattern is that it suggests the presence of residual or regenerative diseased parathyroid tissue.

7. The integrated system for rapid detection and data analysis of parathyroid hormone according to claim 6, characterized in that, The dynamic comparison module is further configured to: When the decline-early rebound pattern is matched, the blood calcium concentration data of the target patient within the second predetermined time period are acquired simultaneously. Analyze the coupling relationship between the upward trend of the hormone concentration data and the changing trend of the blood calcium concentration data; The diagnostic and treatment assistance prompt module is further configured to: based on the coupling relationship, identify the nature or classify the probability of residual or regenerative risk of the diseased parathyroid tissue in the prompt information.

8. The integrated system for rapid detection and data analysis of parathyroid hormone according to claim 7, characterized in that, The dynamic comparison module is further configured to: Extract dynamic feature parameters from the coupling relationship, including the time delay between the rate of recovery of the hormone concentration data and the rate of change of the blood calcium concentration data, and / or the ratio of the magnitude of change of the two. The dynamic feature parameters are input into a pre-trained evaluation model to output quantitative evaluation parameters that characterize the functional activity intensity or anatomical localization tendency of residual lesion tissue. The diagnostic and treatment assistance prompt module is further configured to generate prompt information containing suggestions for targeted examinations or intervention strategies based on the quantitative assessment parameters.

9. The integrated system for rapid detection and data analysis of parathyroid hormone according to claim 8, characterized in that, Also includes: Intervention strategy simulation module, configured for: The quantitative assessment parameters are matched with multiple preset risk threshold ranges; Based on the matched risk threshold range, the system calls and combines a set of intervention actions corresponding to that range, which have clear execution priorities and timeliness requirements, from a predefined intervention strategy library. The prompt information specifically includes the set of intervention actions, and marks the priority of each intervention action and the recommended execution time window.

10. The integrated system for rapid detection and data analysis of parathyroid hormone according to claim 2, characterized in that, The cross-reactivity characteristic data refers to different molecular forms of parathyroid hormone, including at least the complete parathyroid hormone molecule and one or more carboxyl-terminal fragments.

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