Bile duct cancer PD-1 monoclonal antibody treatment prognosis prediction system based on blood magnesium level

Abstract

The invention discloses a biliary duct cancer PD-1 monoclonal antibody treatment prognosis prediction system based on blood magnesium level, and relates to the technical field of biliary duct cancer immunotherapy prognosis, the biliary duct cancer PD-1 monoclonal antibody treatment prognosis prediction system comprises four core modules, a blood magnesium detection module uses a full-automatic biochemical analyzer to detect serum magnesium, the serum magnesium is divided into a low magnesium group and a normal blood magnesium group, and synchronous quality control is performed; the clinical data acquisition module acquires multi-dimensional information, and two persons check and complement data; the prognosis analysis module integrates data, and evaluates prognosis through statistical test, survival analysis and subgroup verification; and the result output module generates an encrypted report, visually presents and synchronizes the encrypted report to the electronic medical record system. The serum magnesium is used as a prediction index, the cost is low, the serum magnesium is easy to obtain, the serum magnesium is adaptive to hospital equipment at all levels, the independent prediction value and subgroup consistency are confirmed through rigorous analysis, prognosis can be dynamically updated, individualized suggestions are generated, prediction accuracy and clinical practicability are improved, and biliary duct cancer immunotherapy precision is promoted.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of cholangiocarcinoma immunotherapy prognosis, in particular to a cholangiocarcinoma PD-1 monoclonal antibody treatment prognosis prediction system based on blood magnesium levels. BACKGROUND

[0002] Cholangiocarcinoma is a malignant tumor originating from the biliary system, which has high invasiveness. When patients are diagnosed, they are mostly in the middle and advanced stages, and the prognosis is generally poor. In current clinical treatment, gemcitabine combined with cisplatin is the standard first-line chemotherapy regimen, but most patients will have disease progression after treatment. At this time, PD-1 monoclonal antibody becomes the key choice for second-line treatment, which can prolong the survival period of patients to a certain extent. However, the treatment effect of PD-1 monoclonal antibody has significant individual differences. Some patients not only fail to benefit, but also may increase the treatment burden due to drug adverse reactions. Therefore, effective prognosis prediction indicators are urgently needed to screen suitable treatment populations and optimize treatment strategies.

[0003] The existing research reports of cholangiocarcinoma immunotherapy prognosis biomarkers, such as PD-L1 expression level, tumor mutation burden (TMB), microsatellite instability / mismatch repair (MSI / MMR) status, EBV infection and circulating tumor DNA (ctDNA), have obvious limitations. PD-L1 detection depends on tumor tissue samples, which is difficult to obtain and has poor consistency due to the influence of antibody clone number and positive judgment threshold; TMB and MSI / MMR detection require high-throughput sequencing technology, which is costly and time-consuming, and is difficult to popularize in primary hospitals; EBV infection and ctDNA detection have limited scope due to low positive rate. These markers cannot meet the demand of clinical "economic, convenient, efficient and universal" prognosis prediction indicators, resulting in the "blind use of drugs" of cholangiocarcinoma PD-1 monoclonal antibody treatment, and it is difficult to achieve precise stratified treatment.

[0004] Serum magnesium ions, as essential trace elements in the human body, are involved in cell metabolism, signal transduction and immune regulation, and other physiological processes. Its detection only needs a routine intravenous blood sample, which can be completed by a fully automatic biochemical analyzer, and has the advantages of low cost, simple operation and rapid results. Current studies have confirmed that serum magnesium levels are associated with the prognosis of various diseases, such as cardiovascular disease, kidney disease and chemotherapy of some solid tumors, but there is no research on the correlation between serum magnesium levels and cholangiocarcinoma immunotherapy prognosis. In clinical practice, cholangiocarcinoma patients often have abnormal serum magnesium levels due to biliary obstruction, malnutrition, treatment-related adverse reactions and other factors, but they do not realize that this indicator may reflect the patient's immune function status and treatment tolerance, missing the opportunity to optimize prognosis evaluation through simple detection. Therefore, it is necessary to explore the value of serum magnesium ions in the prognosis prediction of cholangiocarcinoma PD-1 monoclonal antibody treatment, fill the gap of existing prognosis prediction indicators, and become a key breakthrough in promoting the precision of cholangiocarcinoma immunotherapy. SUMMARY

[0005] The application provides a cholangiocarcinoma PD-1 monoclonal antibody treatment prognosis prediction system based on blood magnesium level to solve the problems in the prior art.

[0006] To achieve the above-mentioned purpose, the application adopts the following technical scheme: A cholangiocarcinoma PD-1 monoclonal antibody treatment prognosis prediction system based on blood magnesium level comprises the following modules: A blood magnesium detection module: an automatic biochemical analyzer is used to detect the serum magnesium ion concentration by the arsenazo III method, 0.5 mmol / L, 1.0 mmol / L and 1.5 mmol / L standard products are used to calibrate the instrument before detection, and the calibration error is less than or equal to 2%; the detection time is within 1 week before PD-1 monoclonal antibody treatment and every 2 cycles during treatment, 3 mL of fasting venous blood is collected each time, the serum is separated by centrifugation at 3000 rpm for 10 minutes, the detection is completed within 2 hours, the blood magnesium reference range is set to 0.75-1.02 mmol / L, and the blood magnesium is divided into a low magnesium group and a normal blood magnesium group; the level 1 and 2 internal quality control products are used for each batch; A clinical data collection module, the collected indexes include basic information, tumor-related indexes, ECOG PS scores, tumor markers, liver function indexes and previous treatment history; the collection time is synchronized with the blood magnesium detection, the data is entered by using a structured form, two medical staffs check the data, the data error rate is less than or equal to 0.5%, and the missing rate is less than or equal to 5%; the multiple imputation method is used for completion when the missing rate is less than or equal to 5%; A prognosis analysis module, independent sample t test is used for measurement data, chi-square test is used for count data, and no significant correlation index is screened; PFS and OS curves are drawn by the Kaplan-Meier method, and the Log-rank test is used for comparing the differences between groups; a multi-factor Cox model is constructed, the blood magnesium grouping and related factors are included, the HR and 95% CI are calculated, the ECOG score subgroup analysis is performed, and the subgroup test is meaningful; A result output module, a report includes patient information, blood magnesium results, analysis conclusions and risk levels; visual charts include survival curves and correlation heat maps; the report is encrypted by using SM4, meets the Medical Data Security Guidelines, is synchronized to an electronic medical record system, and supports viewing, downloading and printing.

[0007] Further, a prognosis risk score calculation module is further included, which is used for quantifying the prognosis risk of a patient, and a risk score calculation formula is as follows: , wherein R is a prognosis risk score of a patient in PD-1 monoclonal antibody treatment, M is blood magnesium grouping assignment, E is ECOG PS score assignment, C is CA199 level assignment, a is a blood magnesium grouping weight coefficient, b is an ECOG score weight coefficient, c is a CA199 level weight coefficient, and d is a basic score constant.

[0008] Further, it also includes a data storage and backtracking module, which stores content including patient blood magnesium detection raw data, clinical data forms, prognosis analysis intermediate results, and final output reports; adopts MySQL cluster distributed database storage, data transmission is encrypted using HTTPS protocol, storage period ≥ 15 years, in line with the “Medical Institutions Medical Record Management Regulations”; supports keyword-based retrieval; daily incremental backup, weekly full backup, backup data is stored in a remote disaster recovery center; the backtracking function can call historical data to re-run the prognosis analysis module to verify the consistency of the analysis model at different times.

[0009] Further, it also includes a dynamic monitoring and updating module, which sets the blood magnesium monitoring frequency during treatment: low magnesium group patients are detected once every 1 treatment cycle, and normal blood magnesium group patients are detected once every 2 treatment cycles; after each detection, the latest blood magnesium data and contemporaneous clinical data are automatically retrieved, and the prognosis analysis module is re-run: low magnesium group patients whose blood magnesium returns to normal and lasts for 2 cycles are reclassified as “normal blood magnesium maintenance group” and the prognosis risk level is lowered; normal blood magnesium group patients whose blood magnesium drops to the low magnesium range are immediately marked as “new low magnesium group” and the risk level is raised, triggering an early warning; update the Kaplan-Meier survival curve and risk score, generate a dynamic update report, and push it to the attending physician's mobile terminal to remind them to adjust the treatment plan.

[0010] Further, it also includes a survival period prediction module, which is used to predict the specific survival period of patients receiving PD-1 monoclonal antibody treatment based on baseline blood magnesium and clinical indicators, providing quantitative reference for clinical intervention, and the total survival period prediction calculation formula is: , where OS is the predicted total survival period of patients after PD-1 monoclonal antibody treatment, is the baseline blood magnesium concentration before treatment, A is the patient's age, E is the ECOG PS score, e is the baseline blood magnesium concentration coefficient, f is the age coefficient, g is the ECOG score coefficient, and h is the correction constant.

[0011] Further, it also includes a clinical suggestion generation module, which matches the suggestion library according to the prognosis risk level: high-risk patients are monitored for blood magnesium once a week, and the efficacy is evaluated every 2 weeks during PD-1 monoclonal antibody treatment through imaging examination, and the PD-1 monoclonal antibody dose is adjusted when the blood magnesium remains below 0.7 mmol / L; medium-risk patients are advised to take oral magnesium, monitor blood magnesium every 2 weeks, and evaluate efficacy every 3 weeks; low-risk patients are advised to supplement magnesium through regular diet, monitor blood magnesium every 3 weeks, and evaluate efficacy every 4 weeks.

[0012] Further, further comprising a subgroup adaptation optimization module, which divides subgroups according to key clinical indicators: ECOG PS score "0 point subgroup", "1 point subgroup", "2 point subgroup", age "≤59 years old subgroup", "> 59 years old subgroup", CA199 level "normal subgroup", "mildly elevated subgroup", "significantly elevated subgroup"; adjust the Cox regression model parameters for each subgroup: the age weight in the "> 59 years old subgroup" is reduced from 0.2 to 0.1, and the blood magnesium grouping weight is increased from 0.5 to 0.6; the CA199 covariate coefficient in the "CA199 significantly elevated subgroup" is increased from 0.3 to 0.4, and the blood magnesium grouping coefficient is maintained at 0.5.

[0013] Further, further comprising a prognosis prediction consistency verification module, and the consistency verification calculation formula is: , wherein K is the consistency coefficient, n is the number of subgroups included in the verification, is the significant degree of blood magnesium grouping for prognosis prediction in the ith subgroup.

[0014] Further, further comprising a device compatibility module, which has a built-in device driver library covering mainstream full-automatic biochemical analyzers, and preset calibration parameters for different device detection methods: when Beckman AU5800 adopts dimethyl toluidine blue method, the calibration wavelength is set to 520nm, and the blank correction value is set to 0.02; when Roche Cobas8000 adopts arsenazo III method, the calibration curve fitting method is set to linear regression, and the intercept allowable range is ±0.01; supporting users to manually add new device parameters, and the adding process includes parameter verification steps.

[0015] Further, further comprising a user permission management module, which divides users into three roles: system administrator, clinical physician and laboratory technician; adopts "username + password + dynamic verification code" threefold login verification, and the login timeout time is set to 30 minutes; the operation log automatically records all user operation behaviors, the log storage period is ≥5 years, and audit tracking is supported; abnormal access behaviors automatically trigger early warning and temporarily freeze accounts, in line with the "People's Republic of China Data Security Law" and medical industry information security standards.

[0016] Compared with the existing technology, the beneficial effects of the present application are: Firstly, serum magnesium ion detection has extremely high convenience and economy, only requires a conventional intravenous blood sample, and can be completed relying on the existing full-automatic biochemical analyzer in the hospital, without the need for special equipment and technology, and the detection cost is much lower than that of TMB, ctDNA and other molecular markers, and the detection result is fast (within 2 hours), so that primary hospitals can also easily carry out the detection, effectively breaking the dependence of high-end detection technology on medical resources, and realizing the widespread popularity of prognosis prediction indicators.

[0017] Secondly, the application confirms that the serum magnesium level is an independent prognostic factor for PD-1 monoclonal antibody treatment of cholangiocarcinoma through rigorous statistical analysis (excluding confounding factors, subgroup verification), and the prediction effect is stable and reliable. Whether it is different age, ECOG score or different CA199 level of patient subgroup, the serum magnesium level can effectively distinguish the difference in prognosis, avoid the prediction deviation caused by the individual characteristics of the traditional markers, and has a wider application range. In clinical application, doctors can quickly divide patients into low magnesium group and normal blood magnesium group through simple serum magnesium detection, identify the poor prognosis population in advance, develop more active intervention strategies for low magnesium group patients, maintain the standard treatment plan for normal blood magnesium group patients, realize "risk stratification and individualized treatment", and avoid over-treatment or insufficient treatment.

[0018] In addition, the prognosis prediction system constructed by the application integrates serum magnesium data and clinical information, provides multi-dimensional prognosis evaluation results and visual reports, and significantly improves the clinical practicability. The system can not only output the prognosis risk grade of the patient, but also give targeted suggestions combined with the subgroup analysis results, help doctors master the prognosis of the patient comprehensively; at the same time, dynamic monitoring of the change of serum magnesium level can update the prognosis evaluation results in real time, timely adjust the treatment plan, and further improve the treatment effect. Compared with the traditional prognosis evaluation which only relies on the experience of doctors, the application reduces the human judgment error through the standardized and systematic prediction system, improves the objectivity and accuracy of the prognosis evaluation, and provides a reliable stratification index for the clinical research of cholangiocarcinoma immunotherapy, promotes the scientific design of related clinical trials and the more convincing results, and overall helps the cholangiocarcinoma immunotherapy to change from "experience medicine" to "precision medicine". BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A schematic block diagram of a prognosis prediction system for PD-1 monoclonal antibody treatment of cholangiocarcinoma based on blood magnesium level is proposed for the application; Figure 2 A correlation diagram of serum magnesium level grouping and survival period of cholangiocarcinoma patients treated with PD-1 monoclonal antibody; Figure 3 A consistency verification diagram of serum magnesium level for prognosis prediction of PD-1 monoclonal antibody treatment in different clinical subgroups. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the application.

[0021] In the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, which are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0022] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited. In addition, the terms "mounting", "connecting", "connecting" should be broadly understood, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be the communication between two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances, and the present application will be further described in detail below with reference to the drawings.

[0023] Referring to Figures 1-3 A cholangiocarcinoma PD-1 monoclonal antibody treatment prognosis prediction system based on blood magnesium level includes the following modules: A blood magnesium detection module is used to accurately obtain the blood magnesium concentration of the patient and complete grouping. An automatic biochemical analyzer (such as Hitachi 7600-020, detection wavelength 650 nm) is used to detect the serum magnesium ion concentration by the arsenazo III method. Before detection, the instrument is calibrated with standard products with concentrations of 0.5 mmol / L, 1.0 mmol / L and 1.5 mmol / L, and the calibration error is ≤2%. The detection time is within 1 week before PD-1 monoclonal antibody treatment (baseline blood magnesium) and every 2 cycles during treatment (dynamic blood magnesium). Each time 3 mL of fasting venous blood is collected, the serum is separated by centrifugation at 3000 rpm for 10 minutes, and the detection is completed within 2 hours. The reference range of blood magnesium is set to 0.75-1.02 mmol / L, and the patients are divided into a low magnesium group (serum magnesium <0.75 mmol / L) and a normal blood magnesium group (serum magnesium ≥0.75 mmol / L). Level 1 (0.6 mmol / L) and level 2 (0.9 mmol / L) of indoor quality control products are used synchronously in each batch of detection to ensure that the CV is ≤5%.

[0024] A clinical data collection module is used for synchronously collecting clinical information of a patient to exclude confounding factors. The collected indexes include basic information (age, gender, body mass index), tumor-related indexes (tumor location, TNM stage, metastasis or not), ECOG PS score (0-2 points), tumor markers (CA199 and CEA, which are detected by chemiluminescence and electrochemiluminescence, respectively), liver function indexes (ALT, AST, and bilirubin, which are detected by enzyme method), and previous treatment history. The collection time is synchronous with the detection of blood magnesium, the data entry adopts a structured form, the field mandatory rate is greater than or equal to 98%, after the entry, two medical staffs check it, the data error rate is less than or equal to 0.5%, and when the missing rate is less than or equal to 5%, a multiple imputation method is used to complete the data.

[0025] A prognosis analysis module is used for integrating the blood magnesium data and the clinical data to complete prognosis evaluation. The measurement data is subjected to independent sample t test, and the count data is subjected to chi-square test, indexes having no significant association (P greater than or equal to 0.05) with blood magnesium grouping are screened; the Kaplan-Meier method is used to draw progression-free survival (PFS) and overall survival (OS) curves, and the Log-rank test is used to compare the differences between groups (P less than 0.05 is statistically significant); a multivariate Cox proportional hazards regression model is constructed, the blood magnesium grouping and the confounding factors having P less than 0.05 are included, the hazard ratio (HR) and the 95% confidence interval (CI) of the blood magnesium grouping are calculated; subgroup analysis is performed according to ECOG score, age, and CA199 level, and the Log-rank test P less than 0.05 in the subgroup is ensured.

[0026] A result output module is used for generating a standardized prognosis report and visual presentation. The report includes patient basic information, blood magnesium detection results, confounding factor analysis results, survival analysis curves (annotated with HR value and P value), subgroup analysis conclusion, prognosis risk level (high / medium / low risk), PFS / OS prediction range; the visual charts include Kaplan-Meier survival curve (annotated with risk number) and blood magnesium and clinical index association heat map; the report is processed by an SM4 encryption algorithm, meets the Medical Data Security Guidelines, can be synchronized to an electronic medical record system, and supports online viewing, downloading and paper printing.

[0027] In the application, a prognosis risk score calculation module is further included, which is used for quantifying the prognosis risk of a patient and providing specific numerical basis for risk stratification. The risk score calculation formula is: , wherein R is the prognosis risk score of the patient's PD-1 monoclonal antibody treatment (dimensionless, 0-10 points, R>=7 points is high risk, 5<=R<7 points is medium risk, and R<5 points is low risk), M is the blood magnesium group assignment value (3 for the low magnesium group, and 1 for the normal blood magnesium group), E is the ECOG PS score assignment value (1 for 0 points, 2 for 1 point, and 4 for 2 points), C is the CA199 level assignment value (1 for normal reference value <=37 U / mL, 2 for 37-100 U / mL, and 3 for >100 U / mL), a is the blood magnesium group weight coefficient (1.2), b is the ECOG score weight coefficient (0.8), c is the CA199 level weight coefficient (0.6), and d is the basic score constant (0.5). For example, a patient is in the low magnesium group (M=3), has an ECOG score of 1 (E=2), and has a CA199 level of 150 U / mL (C=3), so R=1.2*3+0.8*2+0.6*3+0.5=3.6+1.6+1.8+0.5=7.5 points, which is determined as high risk, and the monitoring frequency during treatment needs to be increased.

[0028] In the present application, a data storage and backtracking module is further included for safely storing detection data and analysis results, supporting subsequent backtracking verification and model optimization. The module stores contents including patient blood magnesium detection original data (detection time, concentration value, and quality control results), clinical data forms (including double-checking records), intermediate results of prognosis analysis (statistical test values, HR values, and 95% CI), and final output reports. MySQL cluster distributed database storage is adopted, data transmission is encrypted by using the HTTPS protocol, the storage period is greater than or equal to 15 years, which meets the Medical Institutions Medical Record Management Regulations, key word retrieval such as patient ID, detection time, and prognosis risk level is supported, the retrieval response time is less than or equal to 3 seconds, incremental backup is performed every day, full backup is performed every week, backup data is stored in an off-site disaster recovery center, the data loss rate is less than or equal to 0.001%, and the backtracking function can call historical data to re-run the prognosis analysis module to verify the consistency of analysis models in different periods.

[0029] In the present application, a dynamic monitoring and updating module is also included, which is used to track the changes of blood magnesium in real time during PD-1 monoclonal antibody treatment, and dynamically update the prognosis evaluation results. The module sets the monitoring frequency of blood magnesium during treatment: the blood magnesium of patients in the low magnesium group is detected once every 1 treatment cycle (usually 21 days), and the blood magnesium of patients in the normal blood magnesium group is detected once every 2 treatment cycles; after each detection, the latest blood magnesium data and the same period clinical data (treatment response, adverse reactions) are automatically retrieved, and the prognosis analysis module is re-run: the blood magnesium of patients in the low magnesium group returns to normal and lasts for 2 cycles, and they are re-divided into the "normal blood magnesium maintenance group" and the prognosis risk level is lowered; the blood magnesium of patients in the normal blood magnesium group decreases to the low magnesium range, and they are immediately marked as "new low magnesium group" and the risk level is raised, triggering an early warning; the Kaplan-Meier survival curve and the risk score are updated, a dynamic update report is generated, and it is pushed to the mobile terminal of the attending physician to remind the adjustment of the treatment plan (supplement magnesium agent, adjust the dose of PD-1 monoclonal antibody).

[0030] In the present application, a survival period prediction module is also included, which is used to predict the specific survival period of patients after receiving PD-1 monoclonal antibody treatment based on the baseline blood magnesium and clinical indicators, and to provide quantitative reference for clinical intervention. The total survival period prediction calculation formula is: , in the formula, OS is the predicted total survival period of patients after PD-1 monoclonal antibody treatment (unit: month, retaining 1 decimal place), is the baseline blood magnesium concentration before treatment (unit: mmol / L), A is the age of the patient (unit: year), E is the ECOG PS score (dimensionless, 0-2 points), e is the baseline blood magnesium concentration coefficient (value 8.2, obtained by linear regression fitting of the survival data of 200 patients, negative correlation, the higher the blood magnesium, the greater the coefficient contribution), f is the age coefficient (value -0.05, negative correlation, the older the age, the smaller the coefficient contribution), g is the ECOG score coefficient (value -1.5, negative correlation, the higher the score, the smaller the coefficient contribution), and h is the normalizing constant (value 12.3, fitted to reflect the basic survival period level). For example, a patient =0.85 mmol / L (normal blood magnesium), A=55 years old, E=0 points, OS=8.2x0.85+(-0.05)x55+(-1.5)x0+12.3=6.97-2.75+0+12.3=16.52≈16.5 months, which indicates that the patient has a longer expected survival period and can maintain the standard PD-1 monoclonal antibody treatment plan.

[0031] In the present application, a clinical suggestion generation module is also included for automatically generating individualized clinical intervention suggestions based on the prognosis analysis results. The module matches the suggestion library according to the prognosis risk level: high-risk patients (low magnesium group + ECOG 2 points) are suggested to immediately supplement magnesium (potassium magnesium aspartate injection, 20 mL intravenous infusion per day), monitor blood magnesium every 1 week, and evaluate the efficacy by imaging every 2 weeks during PD-1 monoclonal antibody treatment. When the blood magnesium is continuously lower than 0.7 mmol / L, adjust the PD-1 monoclonal antibody dose (reduce by 20%); medium-risk patients (low magnesium group + ECOG 0-1 points) are suggested to take oral magnesium (magnesium oxide tablets, 0.5 g per day), monitor blood magnesium every 2 weeks, and evaluate the efficacy every 3 weeks; low-risk patients (normal blood magnesium group) are suggested to supplement magnesium through regular diet (daily magnesium intake ≥ 300 mg), monitor blood magnesium every 3 weeks, and evaluate the efficacy every 4 weeks; the suggestion library is associated with the "Guidelines for the Diagnosis and Treatment of Biliary Tract Cancer 2024 Edition", and the content is updated every 6 months. The generated suggestions are prioritized ("urgent magnesium supplementation" is the highest priority, and "regular monitoring" is the low priority).

[0032] In the present application, a subgroup adaptation optimization module is also included for optimizing the prognosis analysis parameters for different clinical subgroups to improve the prediction accuracy within the subgroup. The module divides the subgroups according to the key clinical indicators: ECOG PS score "0 point subgroup" "1 point subgroup" "2 point subgroup", age "≤59 years old subgroup" "> 59 years old subgroup", CA199 level "normal subgroup (≤37 U / mL)" "mildly elevated subgroup (37-100 U / mL)" "significantly elevated subgroup (> 100 U / mL)"; adjust the Cox regression model parameters for each subgroup: in the "> 59 years old subgroup", the age weight is reduced from 0.2 to 0.1, and the blood magnesium grouping weight is increased from 0.5 to 0.6; in the "CA199 significantly elevated subgroup", the CA199 covariate coefficient is increased from 0.3 to 0.4, and the blood magnesium grouping coefficient is maintained at 0.5; after optimization, the AUC value of prognosis prediction in each subgroup is ≥0.82, which is improved by 5%-8% compared with before optimization, solving the problem of low prediction accuracy (AUC <0.75) in the "ECOG 2 point subgroup".

[0033] In the present application, a prognosis prediction consistency verification module is also included for verifying the consistency of blood magnesium level in prognosis prediction in different subgroups to ensure the stability of system prediction. The consistency verification calculation formula is: , wherein K is the consistency coefficient (dimensionless, value 0-1, K≥0.8 indicates excellent consistency, 0.6≤K<0.8 indicates good consistency, and K<0.6 indicates poor consistency), n is the number of subgroups included in the verification (n=6 in the present system, including ECOG 0 points, ECOG 1 points, ECOG 2 points, ≤59 years old, > 59 years old, and CA199 significantly elevated subgroup), is the significance of blood magnesium grouping to prognosis prediction in the i-th subgroup (P<0.01 =1, 0.01≤P<0.05 =0.8, P≥0.05 =0.5). For example, in a certain batch of verification, the K values of the 6 subgroups are 1, 1, 0.8, 1, 0.8, 1, and by substituting, K=1-[(1-1)+(1-1)+(1-0.8)+(1-1)+(1-0.8)+(1-1)] / 6=1-0.4 / 6≈0.93, which is determined to be excellent consistency, indicating that the blood magnesium level can stably predict the prognosis in each subgroup without obvious subgroup bias.

[0034] In the present application, a device compatibility module is also included for adapting different brands and models of serum magnesium detection devices to expand the application scenarios of the system. The module has a built-in device driver library covering mainstream full-automatic biochemical analyzer brands such as Hitachi, Beckman, Roche, and Mindray, and preset calibration parameters for different device detection methods: when Beckman AU5800 adopts the xylene blue method, the calibration wavelength is set to 520 nm, and the blank correction value is set to 0.02; when Roche Cobas8000 adopts the arsenazo III method, the calibration curve fitting method is set to linear regression, and the intercept allowable range is ±0.01; the module supports users to manually add new device parameters (device model, detection method, and calibration concentration), and the addition process includes a parameter verification step (detecting standard samples with an error of ≤3%); it ensures that the blood magnesium data detected by different devices can be directly connected to the system for analysis, with a data deviation rate of ≤2%, and it is suitable for the equipment configuration of hospitals at all levels.

[0035] In the present application, a user permission management module is also included for standardizing the operation permissions of different role users on the system and ensuring the safety of medical data. The module divides users into three roles: system administrator (responsible for device calibration, parameter configuration, and user management, with permissions covering all function modules), clinical physician (responsible for viewing patient data, generating prognosis reports, and adjusting clinical suggestions, without data modification and deletion permissions), and laboratory technician (responsible for entering blood magnesium detection data and uploading quality control results, without prognosis analysis and report generation permissions); the module adopts a triple login verification of "username + password + dynamic verification code", with a login timeout time of 30 minutes; it automatically records all user operation behaviors (operation time, module, content, and result) in the operation log, with a log retention period of ≥5 years and supports audit tracking; it automatically triggers an early warning and temporarily freezes the account for abnormal access behaviors (multiple login failures within 1 hour, batch data download), in line with the "Data Security Law of the People's Republic of China" and the information security standards of the medical industry.

[0036] The specific implementation of the system is further illustrated by two embodiments as follows: ​Example 1: Prognosis prediction of PD-1 mAb treatment for cholangiocarcinoma in a third-grade class-A hospital (application of complete system) This example is aimed at 108 patients with cholangiocarcinoma admitted to the oncology department of a third-grade class-A hospital from January 2021 to December 2023 (all received PD-1 mAb second-line treatment after progression of gemcitabine-cisplatin treatment). The "a prognosis prediction system for PD-1 mAb treatment of cholangiocarcinoma based on serum magnesium level" was applied, and the final prognosis prediction accuracy rate was 88%, providing a basis for clinical stratified treatment.

[0037] I. Implementation process and technical details Serum magnesium detection module operation: Hitachi 7600-020 automatic biochemical analyzer (detection wavelength 650 nm) was used to detect serum magnesium ion concentration by arsenazo III method. One hour before detection, the instrument was calibrated with magnesium ion standard solutions of concentrations 0.5 mmol / L, 1.0 mmol / L, and 1.5 mmol / L (Shanghai Langdao Biological, Lot number LOT20230105). After calibration, the blank absorbance was ≤0.05, and the standard detection errors were 1.2%, 0.8%, and 1.5%, respectively, meeting the requirement of ≤2%. Detection timing: baseline serum magnesium was collected within 72 hours before treatment, and dynamic serum magnesium was collected every 2 cycles (42 days) during treatment; 3 mL of fasting venous blood was collected from each patient (using a coagulation tube containing separation gel), centrifuged at 3000 rpm for 10 min (centrifugation radius 15 cm), and serum was separated for detection within 1.5 hours. The reference range of serum magnesium was set to 0.75-1.02 mmol / L. Among the 108 patients, 38 had serum magnesium <0.75 mmol / L (low magnesium group), and 70 had serum magnesium ≥0.75 mmol / L (normal serum magnesium group). Level 1 (0.6 mmol / L, Lot number LOT20230210) and level 2 (0.9 mmol / L, Lot number LOT20230211) internal quality control materials were used synchronously in each batch of detection, with CVs of 3.2% and 2.8%, respectively, both ≤5%, ensuring reliable detection results.

[0038] Clinical data collection and processing: structured electronic forms were used to collect patient clinical information, and the collection time was synchronized with serum magnesium detection. Basic information: age (35-78 years, median age 59 years), gender (62 males and 46 females), body mass index (18.5-28.3 kg / m 2Tumor-related indicators: tumor location (intrahepatic cholangiocarcinoma 45 cases, extrahepatic cholangiocarcinoma 38 cases, ampullary bile duct carcinoma 25 cases), TNM stage (stage III 52 cases, stage IV 56 cases), metastasis (liver metastasis 48 cases, lymph node metastasis 55 cases, metastasis to other sites 22 cases); ECOGPS score (0 points 32 cases, 1 point 58 cases, 2 points 18 cases); tumor marker: CA199 (chemiluminescence method, Beckman DXI800 instrument, normal reference value ≤37U / mL, elevated values ​​are considered abnormal). The data included: 68 cases of patients with elevated CEA (electrochemiluminescence immunoassay, Roche Cobase 601, normal reference value ≤5 ng / mL, 35 cases with elevated levels); liver function indicators: ALT (enzymatic method, normal reference value 0-40 U / L, 42 cases with abnormal levels), AST (enzymatic method, normal reference value 0-40 U / L, 38 cases with abnormal levels), bilirubin (enzymatic method, normal reference value 3.4-20.5 μmol / L, 51 cases with abnormal levels); and past treatment history (43 cases underwent surgery, 18 cases underwent radiotherapy). After data entry, the data was double-checked by two oncology nurses. Three data entry errors were found (CA199 value entry deviation), resulting in a data error rate of 2.8% after correction. Two missing body mass index data were supplemented using multiple imputation (missing data rate 1.9%) to ensure data integrity.

[0039] Prognostic analysis and formula application: First, exclude confounding factors, measurement data (age, CA199, CEA, etc.) using independent sample t test, count data (tumor location, ECOG score, etc.) using chi-square test, the results showed that there was no significant difference in age, gender, tumor location and other indicators between the two groups (P all ≥ 0.05), only ECOG 2 points accounted for (low magnesium group 21.1%, normal blood magnesium group 8.6%), abnormal rate of bilirubin (low magnesium group 65.8%, normal blood magnesium group 38.6%) There are differences (P<0.05), these two items are included in the multivariate Cox regression model. Kaplan-Meier method was used to draw the survival curve, and Log-rank test showed that the median value of progression-free survival (PFS) in the low magnesium group was 4.2 months, and the median value of progression-free survival (PFS) in the normal blood magnesium group was 7.5 months; The median value of overall survival (OS) was 8.8 months, and the median value of overall survival (OS) in the normal blood magnesium group was 13.2 months, and the difference between the two groups was statistically significant (P<0.001). After constructing the Cox model, the risk ratio (HR=2.093, 95%CI 1.244-3.520, P<0.001) of blood magnesium grouping was calculated, which confirmed that the blood magnesium level was an independent prognostic factor. According to the ECOG score, age (≤59 years / >59 years), CA199 (normal / raised) for subgroup analysis, the prognosis of the low magnesium group in each subgroup was significantly worse than that of the normal blood magnesium group (P all <0.05). Application of risk score formula R=a×M+b×E+c×C+d, for a certain ECOG 1 point (E=2), CA199 150 U / mL (C=3) low magnesium group (M=3), R=1.2×3+0.8×2+0.6×3+0.5=3.6+1.6+1.8+0.5=7.5 points (high risk); Application of survival prediction formula OS=e×M0+f×A+g×E+h, for a certain baseline blood magnesium 0.9 mmol / L (M0=0.9), age 55 years (A=55), ECOG 0 points (E=0) normal blood magnesium group of patients, OS=8.2×0.9+(-0.05)×55+(-1.5)×0+12.3=7.38-2.75+0+12.3=16.93≈16.9 months, close to the actual follow-up OS result of 17.2 months.

[0040] Result output and clinical application: Generate a standardized prognosis report containing patient basic information, blood magnesium test results (low magnesium group marked specific concentration and grouping basis), confounding factor analysis table, Kaplan-Meier survival curve (marked HR=2.093 and P<0.001), subgroup analysis forest plot, risk score (high / medium / low risk), OS / PFS prediction range. In the visualization chart, the survival curve marks the number of risks at each time point (e.g. 66 people in the normal blood magnesium group and 42 people in the low magnesium group at 3 months; 52 people in the normal blood magnesium group and 23 people in the low magnesium group at 12 months), and the heat map shows the correlation strength of blood magnesium, bilirubin, and ECOG score. The report is synchronized to the hospital electronic medical record system after SM4 encryption, and the attending physician develops an intervention plan based on the report: low magnesium group high-risk patients (such as the above R=7.5 points) are given potassium magnesium aspartate injection (20 mL intravenous infusion per day), blood magnesium is monitored every 1 week, and efficacy is evaluated by abdominal enhanced CT every 2 weeks during PD-1 monoclonal antibody treatment; normal blood magnesium group low-risk patients (such as the above OS prediction of 16.9 months) maintain regular diet magnesium supplementation, and blood magnesium is monitored every 3 weeks. Apply the consistency verification formula , 6 subgroups are 1, 1, 0.8, 1, 0.8, 1 respectively, and K=1-(0+0+0.2+0+0.2+0) / 6≈0.93 is calculated, indicating excellent consistency and ensuring stable system prediction.

[0041] Evaluation index Invention system Traditional PD-L1 detection Advantages Detection cost (yuan / example) 25 800-1200 Cost is extremely low Detection time (h) 2 48-72 Time is extremely short Sample acquisition difficulty Venous blood (easy to obtain) Tumor tissue (difficult to obtain) Sample is easy to obtain Prediction accuracy (%) 88 72-78 Accuracy is higher Subgroup consistency (K value) 0.93 (excellent) 0.65 (good) Consistency is better Primary hospital applicability High (adapt to conventional equipment) Low (need sequencing equipment) Wider applicability Table 1 This table 1 is based on the parallel evaluation data of 108 patients, and directly reflects the advantages of the system. The traditional PD-L1 detection relies on tumor tissue samples, and only 28 of the 43 surgical patients successfully obtained qualified samples, with a detection cost of more than 800 yuan per case, a time-consuming of 3 days, and difficulty for primary hospitals to carry out; the detection cost of the system is only 25 yuan, the result is obtained within 2 hours, and all patients successfully obtain venous blood samples (acquisition rate 100%), which is suitable for existing biochemical analyzers in hospitals and can be directly applied in primary hospitals. In terms of prediction accuracy, the accuracy of 88% of the system is significantly higher than the average of 75% of PD-L1 detection, especially in the refractory subgroup with ECOG 2 points and significantly elevated CA199, the accuracy advantage is more obvious. The K value of subgroup consistency is 0.93, which means that the system can stably predict the prognosis in patients with different clinical characteristics, avoiding the prediction deviation caused by individual differences in patients in traditional detection, and providing a more reliable prognosis evaluation tool for clinical application.

[0042] Example 2: Prognosis prediction for cholangiocarcinoma PD-1 monoclonal antibody treatment in primary hospitals (simplified system application) The embodiment is directed to a county-level hospital (secondary hospital) oncology department 2022 March-2024 January treated 62 cases of cholangiocarcinoma patients (all are gemcitabine-cisplatin after progression to receive PD-1 monoclonal antibody treatment), the application of the simplified version of the prognosis prediction system is detected and analyzed within 36 hours, the prediction accuracy is 82%, which meets the clinical needs of primary hospitals.

[0043] I. Implementation process and technical details Blood magnesium detection and equipment adaptation: primary hospitals use Mindray BS-480 automatic biochemical analyzer (detection wavelength 650nm) to detect serum magnesium ion concentration by azoarsenite III method. The system device compatibility module is built-in the device driver of this model, and the preset calibration parameters are: the calibration curve fitting method is linear regression, the intercept allowable range is ±0.01, and the blank correction value is 0.03. Before detection, use 0.5mmol / L, 1.0mmol / L, 1.5mmol / L standard to calibrate, collect 3mL fasting venous blood of patients, centrifuge (2500rpm, 15min) to separate serum, and complete detection within 3h (because of the small sample size of primary hospitals, the detection batch interval is short, and there is no problem of serum placement overtime). Set the same blood magnesium grouping standard as the tertiary hospital, among the 62 patients, 22 cases are low magnesium group (serum magnesium <0.75mmol / L), and 40 cases are normal blood magnesium group (serum magnesium ≥0.75mmol / L). Use level 1 (0.6mmol / L) and level 2 (0.9mmol / L) quality control (provided by local reagent company), and the quality control CV is 4.8% and 4.2% respectively, which meets the detection quality requirements of primary hospitals, and avoids the result deviation caused by equipment difference.

[0044] Clinical data collection and simplified processing: considering the data collection conditions of primary hospitals, the collection range of clinical indicators is simplified, and the following are collected: basic information, tumor-related indicators (tumor location, whether metastasis), ECOG PS score (0-2 points), tumor marker CA199 (chemiluminescence method, routine detection item of primary hospitals), liver function indicator bilirubin (enzyme method), and previous treatment history (whether surgery). After collecting by paper form, a doctor and a nurse check and input the system, the data mandatory rate is 96%, 2 cases of CA199 value are missing (missing rate 3.2%), which is marked as "missing" and excluded from the index in the prognosis analysis because it exceeds the 5% multiple interpolation threshold. Among the finally included indicators, only the proportion of ECOG 2 points in the two groups (low magnesium group 27.3%, normal blood magnesium group 10.0%) is different (P<0.05), which is included in the Cox regression model.

[0045] Prognosis analysis and clinical recommendations: Using a simplified prognosis analysis process, the Kaplan-Meier method was used to draw the survival curve, and the Log-rank test showed that the median PFS of the low magnesium group was 3.8 months, and the normal blood magnesium group was 6.9 months; The median OS was 8.1 months, and the normal blood magnesium group was 12.5 months, and the difference was statistically significant (P<0.001). Cox regression model calculated blood magnesium grouping HR=2.15 (95%CI 1.18-3.92, P<0.01), confirming the independent prognostic value. Subgroup analysis by age (≤59 years / >59 years), CA199 (normal / raised), the prognosis of each subgroup of low magnesium group was poor (P<0.05). Apply risk score formula, for a ECOG2 points (E=4), CA199120U / mL (C=2) low magnesium group patients (M=3), calculated R=1.2x3+0.8x4+0.6x2+0.5=3.6+3.2+1.2+0.5=8.5 points (high risk), the system automatically matches the clinical recommendations: immediately oral magnesium oxide tablets (0.5g daily), every 2 weeks Monitor blood magnesium, evaluate efficacy every 3 weeks during PD-1 monoclonal antibody treatment (primary hospital without enhanced CT, use ultrasound instead); For a patient with normal blood magnesium and ECOG0 points, the survival period is predicted OS=8.2x0.85+(-0.05)x60+(-1.5)x0+12.3=6.97-3+0+12.3=16.27≈16.3 months, and it is recommended to supplement magnesium through regular diet, and monitor blood magnesium every 4 weeks. Consistency verification K=1-[(1-1)+(1-0.8)+(1-1)+(1-0.8)+(1-1)+(1-1)] / 6=1-0.4 / 6≈0.93, the consistency is excellent.

[0046] Data storage and permission management: Local MySQL database is used to store data (primary hospital has no off-site disaster recovery conditions, full backup is performed on the hospital server every day), storage content includes blood magnesium detection raw data, clinical form scan, prognosis analysis results, storage period is set to 15 years. The user permission management module divides the hospital users into three levels: laboratory technicians (only enter blood magnesium data, no analysis permission), oncology doctors (view reports and generate recommendations), and hospital administrators (manage users and backup data). Use "username + password" double login (simplify dynamic verification code in primary hospitals), operation log is saved for 5 years, support audit tracking. When a nurse mistakenly tries to download data in batches, the system triggers an alarm and freezes the account, and the administrator verifies and unlocks it to ensure data security.

[0047] Primary application index Invention simplification system Traditional no prediction scheme Advantages Equipment requirements Routine biochemical analyzer None (experience-based) Adapt to primary equipment Medical training time (h) 4 None (experience-dependent) Low training cost Patient referral rate (%) 18 35 Referral rate is reduced Treatment adjustment timeliness (%) 85 42 Adjustment is more timely Patient satisfaction (%) 90 65 Satisfaction is higher Table 2 The table 2 is based on the application data statistics of 62 primary patients, highlighting the primary adaptability of the simplified system of the application. In the traditional non-prediction scheme, the doctor only judges the treatment effect according to experience, 35% of the patients are transferred to the superior hospital due to poor curative effect, and 42% of the patients cannot timely adjust the treatment scheme (such as low magnesium patients without magnesium supplement); the system of the application is adapted to the primary routine biochemical analyzer, and the medical staff only needs 4 hours of training to operate, through prognosis prediction, high-risk patients are identified in advance, the transfer rate of 18% is reduced by nearly half compared with the traditional scheme, and 85% of the patients can timely adjust the treatment scheme. The patient satisfaction rate is increased from 65% to 90%, mainly because the system provides clear prognosis information and treatment suggestions, reduces the patient's worry about “blind treatment”, and avoids unnecessary transfer and travel, which meets the clinical needs of “convenience, economy and effectiveness” of primary hospitals, and promotes the popularization and application of cholangiocarcinoma immunotherapy prognosis prediction technology in primary hospitals.

[0048] Reference Figure 2 The figure directly presents the influence of blood magnesium level on survival period. At the time of follow-up for 24 months, 18% of patients in the normal blood magnesium group still survived, the survival rate of the low magnesium group decreased to 0, and the survival rate of the normal blood magnesium group at each time node was significantly higher than that of the low magnesium group, HR=2.093, indicating that the death risk of low magnesium patients was more than twice that of normal blood magnesium patients. The risk number mark reflects the reliability of the data, avoiding the deviation of the results caused by sample loss. The chart solves the problem of “non-intuitive data” in traditional survival analysis, provides visual basis for clinicians to quickly judge the correlation between blood magnesium and prognosis, also verifies the clinical value of serum magnesium as an independent prognostic indicator, and supports the inclusion of it into the routine prognostic evaluation system of cholangiocarcinoma PD-1 monoclonal antibody treatment.

[0049] Reference Figure 3 The figure verifies the universality of blood magnesium prediction. The Log-rank test P value of all subgroups is <0.05, indicating that blood magnesium has significant prediction value in each subgroup: the ECOG 0-1 group, the age ≤59 group, and the CA199 normal group S i =1.0 (P<0.01), the prediction effect is better; the ECOG 2 group, the age >59 group, and the CA199 abnormal group S i =0.8 (0.01≤P<0.05), the prediction is still effective. The consistency coefficient K=0.93 (≥0.8) is calculated, which is determined as excellent consistency, confirming that blood magnesium is not affected by the ECOG score, age, and CA199 level of the patient, and can stably predict the prognosis, solving the limitation of traditional markers “only applicable to specific subgroups”. The chart provides evidence for the clinical application of “blood magnesium prediction is applicable to all cholangiocarcinoma PD-1 treatment patients”, especially supporting the evaluation of prognosis of patients in refractory subgroups such as ECOG 2 through blood magnesium detection, expanding the applicable range of prognostic prediction indicators.

[0050] The above merely describes preferred embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can make equivalent replacements or changes within the technical scope disclosed by the present application and according to the technical solutions and inventive concept of the present application, which should be covered within the protection scope of the present application.

Claims

1. A cholangiocarcinoma PD-1 mAb treatment prognosis prediction system based on blood magnesium level, characterized in that, The following modules are included: Blood magnesium detection module: using a fully automatic biochemical analyzer, the serum magnesium ion concentration is detected by the arsenazo III method. Before detection, the instrument is calibrated with 0.5 mmol / L, 1.0 mmol / L, and 1.5 mmol / L standard solutions, and the calibration error is ≤2%. The detection time is within 1 week before PD-1 monoclonal antibody treatment and every 2 cycles during treatment. 3 mL of fasting venous blood is collected each time, centrifuged at 3000 rpm for 10 minutes to separate the serum, and the detection is completed within 2 hours. The reference range of blood magnesium is set to 0.75-1.02 mmol / L, and it is divided into low magnesium group and normal blood magnesium group. Each batch uses level 1 and 2 internal quality control products; Clinical data collection module: the collected indicators include basic information, tumor-related indicators, ECOG PS score, tumor markers, liver function indicators, and previous treatment history. The data collection time is synchronized with the blood magnesium detection, and the data is entered using a structured form. Two medical staff check the data, and the data error rate is ≤0.5%. When the missing rate is ≤5%, multiple imputation method is used to complete the data; Prognosis analysis module: independent sample t-test for quantitative data, chi-square test for count data, screening for no significant correlation indicators; Kaplan-Meier method to draw PFS and OS curves, Log-rank test to compare differences between groups; construct a multivariate Cox model, include blood magnesium grouping and related factors, calculate HR and 95% CI; subgroup analysis according to ECOG score to ensure that the subgroup test is meaningful; Results output module: the report includes patient information, blood magnesium results, analysis conclusions, and risk levels; Visual charts include survival curves and correlation heat maps. The report is encrypted using SM4, complies with the "Medical Data Security Guidelines", and is synchronized to the electronic medical record system, supporting viewing, downloading, and printing.

2. The system for prognosis prediction of cholangiocarcinoma PD-1 mAb treatment based on blood magnesium level according to claim 1, characterized in that, Also included is a prognosis risk score calculation module for quantifying the prognosis risk of the patient, and the risk score calculation formula is: , wherein R is the prognosis risk score of the patient treated by the PD-1 monoclonal antibody, M is the blood magnesium grouping value, E is the ECOG PS score value, C is the CA199 level value, a is the blood magnesium grouping weight coefficient, b is the ECOG score weight coefficient, c is the CA199 level weight coefficient, and d is the basic score constant. 3.The cholangiocarcinoma PD-1 monoclonal antibody treatment prognosis prediction system based on blood magnesium level according to claim 1, characterized in that, It also includes a data storage and retrieval module, which stores patient blood magnesium test raw data, clinical data forms, intermediate results of prognosis analysis, and final output reports. MySQL cluster distributed database is used for storage, data transmission is encrypted using HTTPS protocol, storage period is ≥15 years, which complies with the "Medical Institutions Medical Record Management Regulations"; supports keyword search; daily incremental backup and weekly full backup are performed, and backup data is stored in a remote disaster recovery center; The backtracking function can call historical data to re-run the prognosis analysis module to verify the consistency of the analysis model at different times. 4.The cholangiocarcinoma PD-1 monoclonal antibody treatment prognosis prediction system based on blood magnesium level according to claim 1, characterized in that, It also includes a dynamic monitoring and updating module, which sets the blood magnesium monitoring frequency during treatment: low magnesium group patients are detected once every 1 treatment cycle, and normal blood magnesium group patients are detected once every 2 treatment cycles. After each detection, the latest blood magnesium data and contemporaneous clinical data are automatically retrieved, and the prognosis analysis module is re-run: low magnesium group patients whose blood magnesium returns to normal and lasts for 2 cycles are re-classified as "normal blood magnesium maintenance group" and the prognosis risk level is lowered; normal blood magnesium group patients whose blood magnesium decreases to the low magnesium range are immediately marked as "new low magnesium group" and the risk level is increased, triggering an early warning; Update the Kaplan-Meier survival curve and risk score, generate a dynamically updated report, and push it to the attending physician's mobile terminal to remind them to adjust the treatment plan.

5. The system for prognosis prediction of cholangiocarcinoma PD-1 mAb treatment based on blood magnesium level according to claim 1, characterized in that, Also included is a survival prediction module for predicting the specific survival of patients after receiving PD-1 monoclonal antibody treatment based on baseline blood magnesium and clinical indicators, providing a quantitative reference for clinical intervention, and the total survival prediction calculation formula is: , wherein OS is the predicted total survival of the patient after PD-1 monoclonal antibody treatment, is the baseline blood magnesium concentration before treatment, A is the age of the patient, E is the ECOG PS score, e is the baseline blood magnesium concentration coefficient, f is the age coefficient, g is the ECOG score coefficient, and h is the correction constant.

6. The system for prognosis prediction of cholangiocarcinoma PD-1 mAb treatment based on blood magnesium level according to claim 1, characterized in that, Also included is a clinical recommendation generation module that matches a recommendation library according to a prognosis risk level: high-risk patients, monitor blood magnesium every 1 week, evaluate efficacy every 2 weeks during PD-1 monoclonal antibody treatment through imaging examination, and adjust the PD-1 monoclonal antibody dosage when blood magnesium remains below 0.7 mmol / L; medium-risk patients are recommended to take oral magnesium, monitor blood magnesium every 2 weeks, and evaluate efficacy every 3 weeks; low-risk patients are recommended to supplement magnesium through regular diet, monitor blood magnesium every 3 weeks, and evaluate efficacy every 4 weeks.

7. The system for prognosis prediction of cholangiocarcinoma PD-1 mAb treatment based on blood magnesium level according to claim 1, characterized in that, Also included is a subgroup adaptation optimization module that divides subgroups according to key clinical indicators: ECOG PS score "0 point subgroup", "1 point subgroup", "2 point subgroup", age "≤59 years old subgroup", "> 59 years old subgroup", CA199 level "normal subgroup", "mildly elevated subgroup", "significantly elevated subgroup"; adjust the Cox regression model parameters for each subgroup: in the "> 59 years old subgroup", the age weight is reduced from 0.2 to 0.1, and the blood magnesium grouping weight is increased from 0.5 to 0.6; in the "CA199 significantly elevated subgroup", the CA199 covariate coefficient is increased from 0.3 to 0.4, and the blood magnesium grouping coefficient is maintained at 0.

5.

8. The system for prognosis prediction of cholangiocarcinoma PD-1 mAb treatment based on blood magnesium level according to claim 1, characterized in that, Also included is a prognosis prediction consistency verification module, and the consistency verification calculation formula is: , wherein K is a consistency coefficient, n is the number of subgroups included in verification, is the significant degree of blood magnesium grouping in the i th subgroup to prognosis prediction. 9.The cholangiocarcinoma PD-1 monoclonal antibody treatment prognosis prediction system based on blood magnesium level according to claim 1, characterized in that, Also included is a device compatibility module that has a built-in device driver library covering mainstream fully automatic biochemical analyzers, and preset calibration parameters for different device detection methods: for Beckman AU5800 using xylene blue method, the calibration wavelength is set to 520 nm, and the blank correction value is set to 0.02; for Roche Cobas8000 using arsenazo III method, the calibration curve fitting method is set to linear regression, and the intercept allowed range is ±0.01; support users to manually add new device parameters, and the addition process includes parameter verification steps.

10. The system for prognosis prediction of cholangiocarcinoma PD-1 mAb treatment based on blood magnesium level according to claim 1, characterized in that, Also included is a user permission management module that divides users into three roles: system administrator, clinical physician, and laboratory technician; adopts "username + password + dynamic verification code" triple login verification, and the login timeout time is set to 30 minutes; automatically records all user operation behaviors in the operation log, the log retention period is ≥5 years, and supports audit tracking; automatically triggers early warning and temporarily freezes the account for abnormal access behavior, in line with the "People's Republic of China Data Security Law" and medical industry information security standards.

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