Clinical drug crowd reaction data arrangement and analysis system

Abstract

The invention discloses a clinical drug crowd reaction data arrangement and analysis system, which relates to the technical field of clinical drug crowd reaction data arrangement and comprises a data acquisition module, a data processing module, an arrangement module, a processing module and an analysis module. According to the method, the reactivity of the patient to clopidogrel can be accurately evaluated according to the multi-dimensional information of the patient by calculating the clopidogrel low reactivity index, a scientific basis is provided for timely adjusting a treatment scheme, the risk of adverse reactions such as bleeding is reduced, the risk degree of the adverse reactions can be calculated based on the multi-dimensional information of the patient, and the risk degree of the adverse reactions can be accurately evaluated according to the multi-dimensional information of the patient. The adverse reaction risk possibly occurring when the patient receives double-antibody treatment is predicted, a doctor can take preventive measures in advance, the adverse reaction occurrence rate of the patient is reduced, the treatment safety is improved, personalized pharmaceutical services can be provided according to the individual characteristics of the patient by calculating the medication evaluation index, and the clinical application prospect is wide. Therefore, individual requirements of the patient can be met, and the satisfaction degree and the treatment effect of the patient can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of clinical drug population response data collation, and in particular to a clinical drug population response data collation and analysis system. Background Art

[0002] Ischemic stroke and transient ischemic attack are the most common types of cerebrovascular disease. Among hospitalized patients with cerebrovascular disease in my country, approximately 83% suffer from ischemic stroke, with an annual recurrence rate of approximately 9.6% to 17.7%. Effective secondary prevention strategies are important for reducing recurrence, disability, and mortality. Numerous clinical studies have demonstrated the significant benefits of antiplatelet therapy for the secondary prevention of ischemic cerebrovascular disease. Therefore, antiplatelet drugs are widely used in the treatment of ischemic cerebrovascular disease. Currently, both domestic and international guidelines recommend dual antiplatelet therapy as a primary treatment option for patients. Among them, clopidogrel is an irreversible P2Y12 inhibitor. As a prodrug, it needs to be metabolized into active products by hepatic enzymes to exert its efficacy. However, many factors can affect the blood concentration and clinical efficacy of clopidogrel, resulting in low responsiveness to antiplatelet drug treatment, poor treatment effect, and increased risk of adverse reactions such as bleeding. In order to optimize treatment effects and reduce adverse reactions, clinical pharmacy services need to more accurately assess patients' clopidogrel responsiveness, predict the risk of adverse reactions to dual-antibody therapy, and formulate personalized pharmaceutical service plans accordingly. In recent years, with the deepening of precision medicine concepts and the rapid development of gene sequencing technology, more and more evidence has shown that factors such as patients' genotype, clinical characteristics, and drug interactions have an important influence on clopidogrel responsiveness and the risk of adverse reactions to dual-antibody therapy. Therefore, it is necessary to integrate patients' multi-dimensional information to accurately predict and evaluate patients' clopidogrel responsiveness, the risk of adverse reactions to dual-antibody therapy, and the level of personalized pharmaceutical services.

[0003] However, the current analysis of clinical drug population response data may rely on the patient's clinical manifestations and the doctor's experience and judgment. This method is easily affected by subjective factors, resulting in inaccurate evaluation results. At the same time, there are differences in genotypes and clinical characteristics among different patients. These differences have an important impact on the responsiveness of clopidogrel. Dual antiplatelet therapy may cause adverse reactions such as bleeding and gastrointestinal discomfort. These adverse reactions may be life-threatening, but traditional methods may find it difficult to accurately predict the patient's adverse reaction risk, resulting in a lack of scientific basis for doctors when formulating treatment plans. At the same time, different patients have different needs for dual antiplatelet therapy, but traditional pharmaceutical services may be fixed in a fixed manner and difficult to meet the patient's personalized needs. This may cause patients to experience discomfort or adverse reactions during treatment, reducing treatment efficacy and patient satisfaction. Summary of the Invention

[0004] The purpose of the present invention is to provide a clinical drug population response data collation and analysis system to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a system for collating and analyzing clinical drug population response data, comprising: Data collection module: collects patients' age information, genetic information, drug use information and adverse reaction information from the hospital information system and electronic medical record system, and stores it in the database; Data processing module: cleans, removes duplicates and standardizes the collected patient age information, genetic information, drug use information and adverse reaction information; Arrangement module: inputs the patient's age information, genetic information, drug use information and adverse reaction information, and outputs genotype score, clinical score, impact score, bleeding risk score and respiratory risk score; Processing module: inputs genotype score, clinical score, impact score, bleeding risk score and respiratory risk score, and outputs clopidogrel hyporesponsiveness index, risk level of adverse reactions and medication assessment index; Analysis module: Conduct genetic testing on patients with a high clopidogrel hyporesponsiveness index, focus on monitoring patients with a high risk of adverse reactions, provide medication recommendations for patients with a low medication assessment index, and write a research report based on the analysis results, proposing targeted clinical recommendations and improvement measures.

[0006] Optionally, the processing module includes: a clopidogrel hyporesponsiveness submodule, a dual-antibody therapy adverse reaction submodule, a medication evaluation submodule and an optimization submodule.

[0007] Optionally, the calculation formula of the clopidogrel low responsiveness submodule is as follows: ; in: ROGZ indicates clopidogrel hyporesponsiveness index; GRS represents genotype score, CIS represents clinical score, DCS represents impact score, RAG represents patient age, RBM represents patient body mass index, RA represents genotype score weight coefficient, RB represents clinical score weight coefficient, RC represents impact score weight coefficient, RD represents patient physical condition weight coefficient, Indicates the degree of influence of genotype score, Indicates the degree of influence of clinical scores, Indicates the degree of influence that affects the score; First: input genetic information based on the sorting module; If the patient does not carry a loss-of-function allele, the genotype score GRS output is 0; If the patient carries the CYP2C19 loss-of-function allele, the genotype score GRS output is 1; The first item: if the patient's age is > 65 years old, output 1 point, otherwise output 0 points; The second item: if the patient has liver and kidney dysfunction, output 2 points, otherwise output 0 points; Item 3: If the patient has gastrointestinal bleeding and peptic ulcer, output 2 points, otherwise output 0 points; Item 4: If the patient has hypertension, diabetes, and kidney disease, output 2 points; otherwise, output 0 points; The sum of the outputs of the first, second, third, and fourth items is the clinical score CIS; Third: input drug use information based on the sorting module; The first item is: if the patient is using a proton pump inhibitor, the output is 2 points; otherwise, the output is 0 points; The second item: if the patient is taking other anticoagulant and antiplatelet drugs, output 2 points, otherwise output 0 points; The sum of the outputs of the first and second items is the impact score DCS; The processing process of the clopidogrel hyporesponsiveness submodule is as follows: the genotype score GRS, clinical score CIS, and impact score DCS are input into the clopidogrel hyporesponsiveness submodule, and the clopidogrel hyporesponsiveness index ROGZ is output based on the patient's age RAG and the patient's body mass index.

[0008] Optionally, the calculation formula of the dual-antibody treatment adverse reaction submodule is as follows: ; in: BPOD indicates the risk level of adverse reactions; HRFC represents the bleeding risk score, DRFC represents the respiratory risk score, e represents the base of the natural logarithm, BA represents the weight parameter one, BB represents the weight parameter two, BC represents the constant term, Bx represents the weight parameter of ROGZ, Indicates the weight influence of bleeding risk score, Indicates the weight influence of respiratory risk score; First: input age information and drug use information based on the sorting module; The first item: if the patient's age is > 65 years old, the output is 2 points, otherwise the output is 0 points; Second, if the patient has a history of gastrointestinal bleeding, cerebral hemorrhage, or other bleeding diseases, output 2 points; otherwise, output 0 points; Item 3: If the patient has hypertension, diabetes, or liver or kidney dysfunction, output 2 points; otherwise, output 0 points; Item 4: If the patient is taking anticoagulants that cause bleeding, output 2 points; otherwise, output 0 points; Item 5: If the patient has a genetic variant related to bleeding, the output is 2 points, otherwise the output is 0 points; The sum of the outputs of the above five items is the bleeding risk score HRFC; Second: input age information, drug use information and adverse reaction information based on the sorting module; The first item: if the patient's age is > 65 years old, the output is 2 points, otherwise the output is 0 points; Item 2: If the patient has a history of asthma or chronic obstructive pulmonary disease, output 2 points, otherwise output 0 points; Item 3: If the patient has a history of smoking, output 2 points, otherwise output 0 points; Item 4: Patients who are taking other drugs that affect the respiratory system will receive a score of 2, otherwise a score of 0 will be received. The sum of the outputs of the above four items is the respiratory risk score DRFC; The processing process of the dual-antibody therapy adverse reaction submodule is as follows: the clopidogrel hyporesponsiveness index ROGZ is input into the dual-antibody therapy adverse reaction submodule, and the risk degree of adverse reaction BPOD is output based on the bleeding risk score HRFC and the respiratory risk score DRFC.

[0009] Optionally, the calculation formula of the medication evaluation submodule is as follows: ; in: IODS stands for Index of Drug Use Assessment; CST denotes treatment cost, PPUS denotes the score of proton pump inhibitor usage, CA denotes the weighting factor of ROGZ, CB denotes the weighting factor of BPOD, CC denotes the weighting factor of PPUS, and CD denotes the weighting factor of CST; The processing process of the medication evaluation submodule is as follows: the clopidogrel hyporesponsiveness index ROGZ and the risk level of adverse reactions BPOD are input into the medication evaluation submodule, and the medication evaluation index IODS is output based on the treatment cost CST and the proton pump inhibitor usage score PPUS.

[0010] Optionally, the processing process of the optimization submodule is as follows: first: ; Next: Set the loop termination condition: Termination condition 1: The number of iterations i is 98; Termination condition 2: <0.0053; in: represents the updated clinical score, represents the clinical score before updating, β represents the adjustment coefficient, and IODSA represents the expected medication assessment index; Based on the difference between the medication assessment index IODS and the expected medication assessment index IODSA, the clinical score before the update is adjusted by the adjustment coefficient β The magnitude of change in the output of the updated clinical score , the updated clinical score The clinical score CIS in the clopidogrel hyporesponsiveness submodule is replaced to continuously adjust the clopidogrel hyporesponsiveness index ROGZ, the risk level of adverse reactions BPOD, and the medication evaluation index IODS. Finally, when the CIS has not reached the termination condition 2 within 98 iterations, the iteration is automatically terminated. The setting of 98 iterations is to avoid an infinite iterative loop.

[0011] Optionally, the data processing module is specifically configured to clean the collected information data, remove duplicate, erroneous and invalid data, convert data of different units into a unified unit, and then group and classify the various data.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention outputs the clopidogrel hyporesponsiveness index through the clopidogrel hyporesponsiveness submodule. The clopidogrel hyporesponsiveness index comprehensively considers multiple factors that affect clopidogrel responsiveness, including genotype and clinical characteristics, thereby achieving accurate quantification of patient responsiveness, making the clopidogrel hyporesponsiveness index a significant improvement in guiding the formulation of individualized treatment plans. The clopidogrel hyporesponsiveness index is a quantitative assessment of the patient's responsiveness to clopidogrel, providing a scientific basis for the formulation of individualized treatment plans. By calculating the clopidogrel hyporesponsiveness index, researchers can more accurately understand the differences in patients' responsiveness to different drugs, thereby guiding clinical medication decisions.

[0013] 2. The present invention outputs the risk level of adverse reactions through the dual antibody treatment adverse reaction submodule. The risk level of adverse reactions can predict the risk of adverse reactions of dual antibody treatment, providing clinicians with an important risk assessment tool, so that the risk level of adverse reactions has significant progress in ensuring patient safety and optimizing treatment plans. The role of the risk level of adverse reactions is to predict the risk of adverse reactions of dual antibody treatment and provide clinicians with a risk assessment tool. Through the risk level of adverse reactions, clinicians can have a more comprehensive understanding of the adverse reaction risks that patients may face when receiving dual antibody treatment, and then formulate safer and more effective treatment plans.

[0014] 3. The present invention outputs a medication evaluation index through a medication evaluation submodule. The medication evaluation index comprehensively considers multiple factors, including therapeutic effect, adverse reaction risk, drug usage and treatment cost, etc., and provides a comprehensive evaluation system for personalized precision pharmaceutical services. The medication evaluation index has made significant progress in optimizing treatment plans, improving treatment effects and reducing treatment costs. The medication evaluation index is a comprehensive evaluation of the effect of personalized precision pharmaceutical services. Through the calculation of the medication evaluation index, researchers can have a more comprehensive understanding of the overall effect and economy of different treatment plans in patient groups, thereby guiding the formulation of clinical medication strategies.

[0015] 4. The present invention iterates the clinical score through the optimization submodule. Through iterative analysis, potential adverse drug reaction risks can be discovered and dealt with in a timely manner, which helps to reduce the occurrence of adverse drug reactions and improve patients' treatment experience and satisfaction. At the same time, it can also optimize the drug use strategy to improve the efficacy and safety of drugs. The iterative method of medication evaluation index can comprehensively consider multiple factors, including genotype, clinical characteristics and drug interactions, and thus provide more comprehensive and accurate evaluation results. Through iterative analysis, a more personalized treatment plan is provided for each patient, which helps to reduce unnecessary drug use, reduce treatment costs and improve patients' treatment effects and quality of life. With the continuous accumulation and updating of clinical data, the iterative method can be continuously optimized and improved, which helps to maintain the accuracy and reliability of the evaluation results and provide patients with better treatment effects and services. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the method steps of the clinical drug population response data collation and analysis system; Figure 2 This is a schematic diagram of the overall structure of the clinical drug population response data collation and analysis system; Figure 3 A structural diagram of the processing module. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] This clinical drug population response data collation and analysis system is different from existing data collation and analysis systems. Existing data collation and analysis often rely on the patient's clinical manifestations and the doctor's experience and judgment, making it difficult to accurately assess the patient's responsiveness to clopidogrel. Furthermore, existing dual antiplatelet therapy may cause adverse reactions such as bleeding and gastrointestinal discomfort, increasing the patient's risk. Traditional methods often have difficulty accurately predicting the risk of adverse reactions in patients. Furthermore, different patients have different needs for dual antiplatelet therapy. Providing personalized pharmaceutical services based on the individual characteristics of the patient has become a challenge in current clinical pharmaceutical services. The module of this clinical drug population response data collation and analysis system can achieve accurate assessment of patients' clopidogrel responsiveness. This requires us to comprehensively consider the patient's genotype, such as CYP2C19 gene polymorphism, clinical characteristics such as age, gender, liver and kidney function, drug interactions and other factors, and use mathematical formulas for quantitative evaluation to achieve reliable prediction of patients' adverse reaction risks. We also analyze the patient's clinical characteristics, drug use history, allergy history and other information, and use mathematical formulas for quantitative evaluation to predict the risk of adverse reactions that may occur when patients receive dual-antibody treatment, as well as to achieve scientific evaluation of the patient's personalized pharmaceutical service level. We comprehensively consider the patient's ROGZ and BPOD, and use mathematical formulas for quantitative evaluation to formulate personalized pharmaceutical service plans.

[0019] Example 1: Please refer to Figures 1 to 3 This embodiment provides a clinical drug population response data collation and analysis system, including: Data collection module: collects patients' age information, genetic information, drug use information and adverse reaction information from the hospital information system and electronic medical record system, and stores it in the database; Data processing module: cleans, removes duplicates and standardizes the collected patient age information, genetic information, drug use information and adverse reaction information; Arrangement module: inputs the patient's age information, genetic information, drug use information and adverse reaction information, and outputs genotype score, clinical score, impact score, bleeding risk score and respiratory risk score; Processing module: inputs genotype score, clinical score, impact score, bleeding risk score and respiratory risk score, and outputs clopidogrel hyporesponsiveness index, risk level of adverse reactions and medication assessment index; Analysis module: Conduct genetic testing on patients with a high clopidogrel hyporesponsiveness index, focus on monitoring patients at high risk of adverse reactions, provide medication recommendations for patients with a low medication assessment index, and write a research report based on the analysis results, proposing targeted clinical recommendations and improvement measures; The processing modules include: clopidogrel hyporesponsiveness submodule, dual-antibody therapy adverse reaction submodule, medication evaluation submodule and optimization submodule; The processing module's processing process is as follows: Ⅰ. Input the genetic information into the processing module; If the patient does not carry a loss-of-function allele, the genotype score output is 0; If the patient carries the CYP2C19 loss-of-function allele, the genotype score output is 1; II. Input age information into the processing module; The first item: if the patient's age is > 65 years old, output 1 point, otherwise output 0 points; The second item: if the patient has liver and kidney dysfunction, output 2 points, otherwise output 0 points; Item 3: If the patient has gastrointestinal bleeding and peptic ulcer, output 2 points, otherwise output 0 points; Item 4: If the patient has hypertension, diabetes, and kidney disease, output 2 points; otherwise, output 0 points; The sum of the outputs of the first, second, third, and fourth items is the clinical score CIS; III. Inputting drug use information into the processing module; Item 1: If the patient is using a proton pump inhibitor, output 2 points, otherwise output 0 points; Item 2: If the patient is taking other anticoagulant and antiplatelet drugs, output 2 points, otherwise output 0 points; The first item plus the second item is the impact score; IV. Inputting age information and drug use information into the processing module; Item 1: If the patient's age is > 65 years old, output 2 points, otherwise output 0 points; Item 2: If the patient has a history of gastrointestinal bleeding, cerebral hemorrhage, or other bleeding diseases, output 2 points; otherwise, output 0 points; Item 3: If the patient has hypertension, diabetes, or liver or kidney dysfunction, output 2 points; otherwise, output 0 points; Item 4: If the patient is also taking anticoagulants that cause bleeding, output 2 points; otherwise, output 0 points; Item 5: If the patient has a genetic variant associated with bleeding, the output is 2 points, otherwise the output is 0 points; The first, second, third, fourth, and fifth items are summed to form the bleeding risk score; V. Inputting age information, drug use information, and adverse reaction information into the processing module; Item 1: If the patient's age is > 65 years old, output 2 points, otherwise output 0 points; Item 2: If the patient has a history of asthma or chronic obstructive pulmonary disease, output 2 points, otherwise output 0 points; Item 3: If the patient has a history of smoking, output 2 points, otherwise output 0 points; Item 4: Patients who are taking other drugs that affect the respiratory system will receive a score of 2, otherwise a score of 0 will be received. The first, second, third, and fourth items are summed to form the respiratory risk score.

[0020] In this embodiment, the multiple submodules in the processing module together constitute the core part of the personalized precision pharmaceutical service model, which helps promote the widespread application and development of personalized treatment in clinical practice. Through quantitative analysis and evaluation, doctors can formulate and adjust treatment plans more scientifically, improve the accuracy and effectiveness of clinical decision-making, and provide valuable data support for clinical research, which helps further explore and optimize the personalized precision pharmaceutical service model. ROGZ is unique in that it comprehensively considers multiple factors influencing clopidogrel responsiveness, including genotype and clinical characteristics, thereby accurately quantifying patient responsiveness. This feature makes ROGZ a significant advancement in guiding the development of personalized treatment plans. ROGZ's role is to quantitatively assess a patient's responsiveness to clopidogrel, providing a scientific basis for the development of personalized treatment plans. By calculating ROGZ, researchers can more accurately understand the differences in patient responsiveness to different drugs, thereby guiding clinical medication decisions. A key feature of BPOD is its ability to predict the risk of adverse reactions to dual-antibody therapy, providing clinicians with an important risk assessment tool. This feature significantly advances BPOD in ensuring patient safety and optimizing treatment plans. BPOD's role is to predict the risk of adverse reactions to dual-antibody therapy and provide clinicians with a risk assessment tool. By calculating BPOD, clinicians can gain a more comprehensive understanding of the potential adverse reaction risks patients may face when receiving dual-antibody therapy, thereby formulating safer and more effective treatment plans. The IODS is characterized by its comprehensive consideration of multiple factors, including therapeutic efficacy, adverse reaction risks, drug usage, and treatment costs, providing a comprehensive evaluation system for personalized precision pharmaceutical services. This feature enables IODS to achieve significant progress in optimizing treatment plans, improving treatment efficacy, and reducing treatment costs. The role of IODS is to comprehensively evaluate the overall effectiveness of personalized precision pharmaceutical services, providing a scientific basis for optimizing treatment plans and reducing treatment costs. By calculating IODS, researchers can more comprehensively understand the overall effectiveness and economic efficiency of different treatment plans in patient groups, thereby guiding the formulation of clinical medication strategies. This system achieves accurate quantitative assessment of patient responsiveness by comprehensively considering multiple factors, such as genotype, clinical characteristics, and drug interactions, thereby improving the accuracy and effectiveness of clinical medication. Formulas such as BPOD and IODS in this method can predict key indicators such as adverse reaction risk and treatment cost, providing clinicians with important risk assessment and decision support tools. This method uses computer-assisted data collation and analysis equipment to improve the efficiency of data processing and analysis, while also helping to reduce treatment costs and improve the efficiency of medical resource utilization. The three sets of formulas comprehensively evaluate patients from different perspectives, such as responsiveness, adverse reaction risk, and comprehensive assessment, forming a systematic and complete evaluation system. This comprehensiveness and systematic approach helps clinicians gain a more comprehensive understanding of patients' conditions and treatment needs, thereby formulating more scientific and reasonable treatment plans. The three sets of formulas work together and complement each other during the evaluation process, together forming the core of personalized precision pharmaceutical services. ROGZ can quantitatively assess patients' responsiveness to clopidogrel; BPOD can predict the risk of adverse reactions; and IODS comprehensively considers multiple factors, providing a scientific basis for optimizing treatment plans and reducing treatment costs. This synergy and complementarity make this method more adaptable and flexible in clinical applications.

[0021] See also Figures 1 to 3 , the processing of the clopidogrel low response submodule is as follows: ; in: ROGZ indicates clopidogrel hyporesponsiveness index; GRS denotes genotype score, CIS denotes clinical score, DCS denotes impact score, and RAG denotes patient age; RBM stands for body mass index, which refers to the patient's obesity level. It is calculated by dividing weight by height. Specifically, RBM reflects the patient's body composition and obesity level. These factors may affect the distribution, metabolism, and excretion of drugs, thereby affecting the therapeutic effect and risk of adverse reactions of drugs. RA represents the genotype score weight coefficient, RB represents the clinical score weight coefficient, RC represents the impact score weight coefficient, RD represents the patient's physical condition weight coefficient, RA×GRS represents the impact degree of the genotype score, RB×CIS represents the impact degree of the clinical score, and RC×DCS represents the impact degree of the impact score; The processing process of the clopidogrel hyporesponsiveness submodule is as follows: the genotype score GRS, clinical score CIS, and impact score DCS are input into the clopidogrel hyporesponsiveness submodule, and the clopidogrel hyporesponsiveness index ROGZ is output based on the patient's age RAG and the patient's body mass index.

[0022] In this embodiment, ROGZ represents a patient's degree of hyporesponsiveness to clopidogrel treatment. This index is calculated using a specific algorithm by comprehensively considering multiple parameters, including the patient's genetic factors, such as CYP2C19 genotype, physiological status, liver and kidney function, concomitant medications, and age. Higher ROGZ values indicate a greater hyporesponsiveness to clopidogrel, potentially requiring treatment adjustments. Calculating ROGZ allows for earlier identification of patients with hyporesponsiveness to clopidogrel, enabling timely adjustments to treatment plans to avoid ineffective or inadequate treatment. ROGZ allows for personalized assessment of each patient's specific circumstances, improving assessment accuracy. Using ROGZ, physicians can identify patients at risk of hyporesponsiveness to clopidogrel early in treatment, allowing for timely adjustments to treatment plans. Prioritizing monitoring of high-risk patients can conserve medical resources and avoid unnecessary testing and treatment. ROGZ can help stratify data based on patient responsiveness, facilitating subsequent data analysis and mining. When evaluating the therapeutic efficacy of clopidogrel, ROGZ can serve as an important reference indicator, helping physicians determine treatment effectiveness. The relationship between the impact score DCS and the clopidogrel hyporesponsiveness index ROGZ in this submodule, that is, the relationship between the patient's use of proton pump inhibitors and other anticoagulant and antiplatelet drugs and the clopidogrel hyporesponsiveness index ROGZ is: The DCS appears as a subtracted item, meaning that the higher the score, the lower the ROGZ value. From a medical perspective, drug interactions may lead to reduced efficacy or increased adverse reactions of clopidogrel. Therefore, when there is a significant drug interaction, that is, the clopidogrel hyporesponsiveness index (ROGZ) of patients with a higher DCS should be reduced accordingly to reflect this potential risk. This design reflects the negative impact of drug interactions on the patient's clopidogrel responsiveness assessment, helping physicians to more accurately assess patients' treatment risks and develop personalized treatment plans. See also Figures 1 to 3 The processing process of the dual-antibody therapy adverse reaction submodule is as follows: ; in: BPOD indicates the risk level of adverse reactions; HRFC indicates bleeding risk score, DRFC indicates respiratory risk score; e represents the base of the natural logarithm. This submodule is presented in the exponential form of e. The exponential function is a nonlinear transformation that can map the independent risk factors of bleeding risk and dyspnea risk to a new output space. The output of the exponential function is always positive, regardless of the input value. In the risk prediction system, this is a welcome feature because the risk value, whether probability or score, is usually expected to be a positive number. Then the exponential function has an amplification effect, that is, when the input value is large, the output value grows faster, which may be beneficial in risk prediction because high-risk events are usually more worthy of attention than low-risk events. BA represents weight parameter 1, BB represents weight parameter 2, BC represents constant term, Bx represents weight parameter of ROGZ, BA×HRFC represents weight influence degree of bleeding risk score, and BB×DRFC represents weight influence degree of respiratory risk score; The processing process of the dual-antibody therapy adverse reaction submodule is as follows: the clopidogrel hyporesponsiveness index ROGZ is input into the dual-antibody therapy adverse reaction submodule, and the risk degree of adverse reaction BPOD is output based on the bleeding risk score HRFC and the respiratory risk score DRFC.

[0023] In this embodiment, BPOD is used to predict a patient's risk of adverse reactions while receiving dual antiplatelet therapy. This prediction system is based on independent risk factors for adverse reactions such as bleeding identified through logistic regression analysis, and is calculated in combination with the patient's specific circumstances, such as age, gender, and medical history. Higher BPOD values indicate a greater risk of adverse reactions, requiring closer monitoring and possible intervention. The BPOD model can predict a patient's risk of adverse reactions, enabling preventive measures to be taken in advance to reduce the occurrence of adverse reactions and improve patient safety. BPOD can accurately predict a patient's risk of adverse reactions while receiving dual antiplatelet therapy, helping physicians make more reasonable treatment decisions. For high-risk patients, physicians can adopt more cautious treatment plans, such as reducing drug dosages and strengthening monitoring, to reduce the incidence of adverse reactions. BPOD predictions allow physicians to take preventive measures in advance to improve treatment safety. BPOD can help stratify data according to patient risk levels, facilitating subsequent risk management and intervention. By mining and analyzing BPOD data, new risk factors or treatment strategies can be discovered, providing clues for future research.

[0024] See also Figures 1 to 3 ,The processing process of the medication evaluation submodule is as follows: ; in: IODS stands for Index of Drug Use Assessment; CST stands for cost of treatment. It appears as a subtraction item, meaning that when evaluating the comprehensive benefits of pharmaceutical services, the impact of treatment costs on the economic burden needs to be taken into account. This includes the cost of patients purchasing drugs such as clopidogrel and proton pump inhibitors, as well as the cost of other adjuvant treatment drugs that may be involved, as well as genetic testing, blood tests, etc., which all incur costs, registration fees, consultation fees, treatment fees, etc. These costs are usually related to the provision of medical services and the number of patients' visits; PPUS represents the score of proton pump inhibitor use, CA represents the weighting factor of ROGZ, CB represents the weighting factor of BPOD, CC represents the weighting factor of PPUS, and CD represents the weighting factor of CST; The processing process of the medication evaluation submodule is as follows: the clopidogrel hyporesponsiveness index ROGZ and the risk level of adverse reactions BPOD are input into the medication evaluation submodule, and the medication evaluation index IODS is output based on the treatment cost CST and the proton pump inhibitor usage score PPUS.

[0025] In this embodiment: the medication evaluation index IODS is a comprehensive evaluation index that takes into account the results of ROGZ and BPOD, and combines other relevant factors, such as the patient's medication compliance, economic status, medical resources, etc., to evaluate the necessity and effectiveness of patients receiving personalized precision pharmaceutical services. The IODS index comprehensively considers multiple aspects of the patient, which helps medical institutions to more reasonably allocate medical resources and provide priority support for patients who most need personalized precision pharmaceutical services. IODS provides a comprehensive evaluation system for personalized precision pharmaceutical services by comprehensively considering multiple factors such as ROGZ and BPOD, PPUS usage scores, and treatment costs. This method helps to optimize treatment plans and reasonably control costs, improve patients' treatment effects and quality of life. The introduction of IODS makes the collation and analysis of clinical drug population response data more comprehensive and in-depth. By calculating IODS, researchers can more comprehensively evaluate the overall effects of different treatment plans in patient groups, providing strong support for the formulation of more scientific and reasonable clinical medication strategies.

[0026] The scoring process for the use of proton pump inhibitors (PPUS) is as follows: If the patient has a history of peptic ulcers or a higher risk of peptic ulcer bleeding, such as advanced age or the use of other drugs that increase the risk of bleeding, the score is 0; Item 1: If the dosage, frequency, and course of PPUS use are in accordance with relevant guidelines and expert consensus recommendations, the score is 3; Item 2: If the patient has no clear risk of gastrointestinal bleeding or the risk is low, but PPUS is used, the score is -2 points; Item 3: If the dosage, frequency, and course of PPUS use do not comply with relevant guidelines or expert consensus recommendations, such as excessive dosage and long course of treatment, the score will be -2 points; Item 4: If the patient has a clear high risk of gastrointestinal bleeding but does not use PPUS for prophylactic treatment, the score is -1 point; Item 5: If the use of PPUS matches the patient's condition, treatment needs, and potential risks, the score is 1 point; In summary, the PPUS score is the sum of items one to five.

[0027] See also Figures 1 to 3 , the optimization submodule processing process is as follows: first: ; Next: Set the loop termination condition: Termination condition 1: The number of iterations i is 98; Termination condition 2: <0.0053; in: represents the updated clinical score, represents the clinical score before updating, β represents the adjustment coefficient, and IODSA represents the expected medication assessment index; Based on the difference between the medication assessment index IODS and the expected medication assessment index IODSA, the clinical score CIS before the update is adjusted by the adjustment coefficient β old The change range is used to output the updated clinical score CIS new , the updated clinical score CIS new Replace the clinical score CIS in the clopidogrel hyporesponsiveness submodule to continuously adjust the clopidogrel hyporesponsiveness index ROGZ, the risk level of adverse reactions BPOD, and the medication evaluation index IODS. Finally, when the CIS has not reached the termination condition 2 within 98 iterations, the iteration is automatically terminated. The setting of 98 iterations is to avoid infinite iteration loops. In this embodiment: <0.0053, 0.0053 is the convergence threshold of CIS iteration. When CIS is in the process of multiple iterations and When the difference is less than 0.0053, the stability of CIS is the best, and CIS reaches a stable state. At this time, the fluctuation received by CIS is the smallest and the most stable. Therefore, the normal iteration of CIS is guaranteed based on the iteration and the convergence threshold of 0.0053, and the purpose of iterative convergence stability is met. The clinical score CIS is a score based on the patient's clinical characteristics, which is used to assess the possibility of clopidogrel hyporesponsiveness in patients. The clinical score CIS of the clopidogrel hyporesponsiveness submodule iterated by the medication evaluation index IODS can comprehensively consider more factors, such as genotype, drug interaction and age, so as to more accurately predict the patient's drug response. In the iterative process, With the continuous accumulation and updating of clinical data, the weights and thresholds of the clinical score CIS can also be adjusted accordingly to reflect the latest clinical research results and practical experience, further improving the accuracy and reliability of predictions. Through the iteration of the Medication Evaluation Index IODS, individual differences of patients, such as genotype differences and drug metabolism ability, can be comprehensively considered to provide patients with more personalized treatment plans. For example, for patients with a higher risk of clopidogrel hyporesponsiveness, drug dosage adjustments, alternative drugs, or enhanced monitoring can be adopted. The iterative Medication Evaluation Index IODS score can also be used as a reference for clinical decision-making, helping doctors make more scientific and reasonable decisions in the process of formulating treatment plans. Moreover, CIS is not fixed. As the patient's treatment progresses, their clinical characteristics may change. By iterating CIS, we can more accurately assess the patient's responsiveness to dual antiplatelet therapy and the risk of adverse reactions at different stages of treatment, thereby developing a more personalized pharmaceutical service plan. Iteration also helps to promptly detect and address changes in the patient's condition and avoid potential adverse drug reactions and medical risks. The expected medication assessment index IODSA is the expected level value of the medication assessment index IODS. IODSA is a hyperparameter. The determination of the expected medication assessment index of IODSA is based on data analysis, that is, collecting a large amount of clinical data, including the patient's clinical characteristics, biological response predictions, PPI usage, treatment costs and other information, which is comprehensively determined by experts; Iterative analysis can promptly identify and address potential adverse drug reaction risks, such as bleeding and dyspnea. This helps reduce the occurrence of adverse drug reactions and improve patients' treatment experience and satisfaction. Iterative analysis can also optimize drug use strategies, improve drug efficacy and safety, and provide patients with better treatment outcomes and prognosis. Traditional drug response assessment methods often focus on a single factor or a few factors, while the IODS iterative method can comprehensively consider multiple factors, including genotype, clinical characteristics, and drug interactions, thereby providing more comprehensive and accurate assessment results. Iterative analysis can provide each patient with a more personalized treatment plan, which helps reduce unnecessary drug use, lower treatment costs, and improve patients' treatment outcomes and quality of life. As clinical data continues to accumulate and update, the iterative method can be continuously optimized and improved. This helps maintain the accuracy and reliability of assessment results and provide patients with better treatment outcomes and services.

[0028] In the specific implementation process, multiple submodules in this method are used to form a clinical drug population response data collation and analysis system. By inputting the genotype score GRS, clinical score CIS, and impact score DCS into the clopidogrel hyporesponsiveness submodule, the clopidogrel hyporesponsiveness index ROGZ is output. ROGZ comprehensively considers multiple factors that affect clopidogrel responsiveness, including genotype and clinical characteristics, thereby achieving accurate quantification of patient responsiveness, making ROGZ a significant improvement in guiding the formulation of individualized treatment plans. ROGZ is a quantitative assessment of patient responsiveness to clopidogrel, providing a scientific basis for the formulation of individualized treatment plans. By calculating ROGZ, researchers can more accurately understand the differences in patient responsiveness to different drugs, thereby guiding clinical medication decisions. By inputting the clopidogrel hyporesponsiveness index (ROGZ) into the dual-antibody therapy adverse reaction submodule, the adverse reaction risk level (BPOD) is output. BPOD can predict the adverse reaction risk of dual-antibody therapy, providing clinicians with an important risk assessment tool. This has made BPOD a significant advancement in ensuring patient safety and optimizing treatment plans. BPOD's role is to predict the adverse reaction risk of dual-antibody therapy and provide clinicians with a risk assessment tool. Through BPOD, clinicians can have a more comprehensive understanding of the adverse reaction risks that patients may face when receiving dual-antibody therapy, thereby formulating safer and more effective treatment plans. By inputting the clopidogrel hyporesponsiveness index (ROGZ) and the risk of adverse reactions (BPOD) into the medication evaluation submodule, the output is the medication evaluation index (IODS). IODS comprehensively considers multiple factors, including treatment efficacy, adverse reaction risk, drug usage, and treatment costs, providing a comprehensive evaluation system for personalized precision pharmaceutical services. IODS has made significant progress in optimizing treatment plans, improving treatment efficacy, and reducing treatment costs. The role of IODS is to comprehensively evaluate the effectiveness of personalized precision pharmaceutical services. By calculating IODS, researchers can more comprehensively understand the overall effectiveness and cost-effectiveness of different treatment plans in patient populations, thereby guiding the formulation of clinical medication strategies. The Medication Use Evaluation Index (IODS) iterative clinical score (CIS) allows for timely identification and resolution of potential adverse drug reaction risks, such as bleeding and dyspnea, through iterative analysis. This helps reduce the occurrence of adverse drug reactions and improves patient treatment experience and satisfaction. The analysis also optimizes drug use strategies, enhancing drug efficacy and safety, and ultimately providing better treatment outcomes for patients. Traditional drug response assessment methods focus on a single or a few factors, while the IODS iterative approach comprehensively considers multiple factors, including genotype, clinical characteristics, and drug interactions, providing more comprehensive and accurate assessment results. Iterative analysis allows for more personalized treatment plans for each patient, helping to reduce unnecessary drug use, lower treatment costs, and improve patient outcomes and quality of life. As clinical data continues to accumulate and update, the iterative approach can be continuously optimized and improved. This helps maintain the accuracy and reliability of assessment results, providing better treatment outcomes and services for patients.

[0029] This allows the various sub-modules to cooperate with each other in calculations, and to perform overall cycles and iterations, so that the overall system has the effect of automatic optimization and updating, and thus better adaptability.

[0030] Example 2: Please refer to Figure 1 、 Figure 2 and Figure 3 ,The data processing module is specifically used to clean the collected information data, remove ,duplicate, erroneous and invalid data, convert data from different units into ,a unified unit, and then group and classify various data.

[0031] In this embodiment: the collected information data is pre-processed by the data processing module, and the data is sorted and cleaned, thereby making the data more regular as a whole, making it easier to process later.

[0032] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A clinical drug population response data collation and analysis system, characterized by: include: Data collection module: collects patients' age information, genetic information, drug use information and adverse reaction information from the hospital information system and electronic medical record system, and stores it in the database; Data processing module: cleans, removes duplicates and standardizes the collected patient age information, genetic information, drug use information and adverse reaction information; Arrangement module: inputs the patient's age information, genetic information, drug use information and adverse reaction information, and outputs genotype score, clinical score, impact score, bleeding risk score and respiratory risk score; Processing module: inputs genotype score, clinical score, impact score, bleeding risk score and respiratory risk score, and outputs clopidogrel hyporesponsiveness index, risk level of adverse reactions and medication assessment index; Analysis module: Conduct genetic testing on patients with a high clopidogrel hyporesponsiveness index, focus on monitoring patients with a high risk of adverse reactions, provide medication recommendations for patients with a low medication assessment index, and write a research report based on the analysis results, proposing targeted clinical recommendations and improvement measures.

2. The clinical drug population response data collation and analysis system according to claim 1, characterized in that: The processing module includes: a clopidogrel hyporesponsiveness submodule, a dual-antibody therapy adverse reaction submodule, a medication evaluation submodule and an optimization submodule.

3. The clinical drug population response data collation and analysis system according to claim 2, characterized in that: The calculation formula of the clopidogrel low responsiveness submodule is as follows: ； in: ROGZ indicates clopidogrel hyporesponsiveness index; GRS represents genotype score, CIS represents clinical score, DCS represents impact score, RAG represents patient age, RBM represents patient body mass index, RA represents genotype score weight coefficient, RB represents clinical score weight coefficient, RC represents impact score weight coefficient, RD represents patient physical condition weight coefficient, Indicates the degree of influence of genotype score, Indicates the degree of influence of clinical scores, Indicates the degree of influence that affects the score; First: input genetic information based on the sorting module; If the patient does not carry a loss-of-function allele, the genotype score GRS output is 0; If the patient carries the CYP2C19 loss-of-function allele, the genotype score GRS output is 1; Second: input age information based on the sorting module; The first item: if the patient's age is > 65 years old, output 1 point, otherwise output 0 points; The second item: if the patient has liver and kidney dysfunction, output 2 points, otherwise output 0 points; Item 3: If the patient has gastrointestinal bleeding and peptic ulcer, output 2 points, otherwise output 0 points; Item 4: If the patient has hypertension, diabetes, and kidney disease, output 2 points; otherwise, output 0 points; The sum of the outputs of the first, second, third, and fourth items is the clinical score CIS; Third: input drug use information based on the sorting module; The first item is: if the patient is using a proton pump inhibitor, the output is 2 points; otherwise, the output is 0 points; The second item: if the patient is taking other anticoagulant and antiplatelet drugs, output 2 points, otherwise output 0 points; The sum of the outputs of the first and second items is the impact score DCS; The processing process of the clopidogrel hyporesponsiveness submodule is as follows: the genotype score GRS, clinical score CIS, and impact score DCS are input into the clopidogrel hyporesponsiveness submodule, and the clopidogrel hyporesponsiveness index ROGZ is output based on the patient's age RAG and the patient's body mass index.

4. The clinical drug population response data collation and analysis system according to claim 3, characterized in that: The calculation formula of the dual-antibody treatment adverse reaction submodule is as follows: ； in: BPOD indicates the risk level of adverse reactions; HRFC represents the bleeding risk score, DRFC represents the respiratory risk score, e represents the base of the natural logarithm, BA represents the weight parameter one, BB represents the weight parameter two, BC represents the constant term, Bx represents the weight parameter of ROGZ, BA×HRFC represents the weight influence degree of the bleeding risk score, and BB×DRFC represents the weight influence degree of the respiratory risk score; First: input age information and drug use information based on the sorting module; The first item: if the patient's age is > 65 years old, the output is 2 points, otherwise the output is 0 points; Second, if the patient has a history of gastrointestinal bleeding, cerebral hemorrhage, or other bleeding diseases, output 2 points; otherwise, output 0 points; Item 3: If the patient has hypertension, diabetes, or liver or kidney dysfunction, output 2 points; otherwise, output 0 points; Item 4: If the patient is taking anticoagulants that cause bleeding, output 2 points; otherwise, output 0 points; Item 5: If the patient has a genetic variant related to bleeding, the output is 2 points, otherwise the output is 0 points; The sum of the outputs of the above five items is the bleeding risk score HRFC; Second: input age information, drug use information and adverse reaction information based on the sorting module; The first item: if the patient's age is > 65 years old, the output is 2 points, otherwise the output is 0 points; Item 2: If the patient has a history of asthma or chronic obstructive pulmonary disease, output 2 points, otherwise output 0 points; Item 3: If the patient has a history of smoking, output 2 points, otherwise output 0 points; Item 4: Patients who are taking other drugs that affect the respiratory system will receive a score of 2, otherwise a score of 0 will be received. The sum of the outputs of the above four items is the respiratory risk score DRFC; The processing process of the dual-antibody therapy adverse reaction submodule is as follows: the clopidogrel hyporesponsiveness index ROGZ is input into the dual-antibody therapy adverse reaction submodule, and the risk degree of adverse reaction BPOD is output based on the bleeding risk score HRFC and the respiratory risk score DRFC.

5. The clinical drug population response data collation and analysis system according to claim 4, characterized in that: The calculation formula of the medication evaluation submodule is as follows: ； in: IODS stands for Index of Drug Use Assessment; CST denotes treatment cost, PPUS denotes the score of proton pump inhibitor usage, CA denotes the weighting factor of ROGZ, CB denotes the weighting factor of BPOD, CC denotes the weighting factor of PPUS, and CD denotes the weighting factor of CST; The processing process of the medication evaluation submodule is as follows: the clopidogrel hyporesponsiveness index ROGZ and the risk level of adverse reactions BPOD are input into the medication evaluation submodule, and the medication evaluation index IODS is output based on the treatment cost CST and the proton pump inhibitor usage score PPUS.

6. The clinical drug population response data collation and analysis system according to claim 5, characterized in that: The processing process of the optimization submodule is as follows: first: ; Next: Set the loop termination condition: Termination condition 1: The number of iterations i is 98; Termination condition 2: <0.0053; in: represents the updated clinical score, represents the clinical score before updating, β represents the adjustment coefficient, and IODSA represents the expected medication assessment index; Based on the difference between the medication assessment index IODS and the expected medication assessment index IODSA, the clinical score before the update is adjusted by the adjustment coefficient β The magnitude of change in the output of the updated clinical score The updated clinical score The clinical score CIS in the clopidogrel hyporesponsiveness submodule is replaced to continuously adjust the clopidogrel hyporesponsiveness index ROGZ, the risk level of adverse reactions BPOD, and the medication evaluation index IODS. Finally, when the CIS has not reached the termination condition 2 within 98 iterations, the iteration is automatically terminated. The setting of 98 iterations is to avoid an infinite iterative loop.

7. The clinical drug population response data collation and analysis system according to claim 1, characterized in that: The data processing module is specifically used to clean the collected information data, remove duplicate, erroneous and invalid data, convert data of different units into unified units, and then group and classify various data.

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