Opioid individualized analgesia evaluation method and system based on gene polymorphism

Abstract

The invention discloses an opioid individualized analgesia evaluation method and system based on gene polymorphism, relates to the technical field of digital medical treatment, and comprises the following steps: evaluating opioid analgesia related SNP sites according to evidence to establish a recommendation intensity standard; screening all detected SNP sites related to the analgesic effect of the opioid drugs in the database and the sample, and respectively setting pain influence parameters of SNP in different levels according to classification levels of recommended intensity standards; and establishing an administration dosage prediction scheme according to the pain influence parameters and the SNP variation number, and adjusting the dosage prediction scheme based on analgesia attribute characteristics corresponding to a to-be-evaluated patient determined by overall scores of the to-be-evaluated patient under different clinical variables. According to the method, individual information and pain assessment data of a patient are introduced, the administration dosage is adjusted according to SNP locus variation conditions, clinical medication decision-making of opioid analgesic drugs based on gene polymorphism is realized, and intelligent medication decision-making support of the administration dosage can be provided according to a detection result.

Description

Technical Field

[0001] The present application relates to the field of digital medical technology, and in particular to a method and system for evaluating individualized analgesia of opioid drugs based on gene polymorphism. Background Art

[0002] Opioids are the main analgesic drugs. However, clinical practice has found that due to differences in genetic polymorphism, the analgesic effect of opioids varies greatly from person to person. Some patients with the same basic conditions and pain assessments use the same dose of drugs, but some patients experience insufficient analgesia or adverse drug reactions such as nausea, vomiting, constipation, and respiratory depression. Clinical cases of opioid tolerance are also common due to excessively rapid increases in doses. The above problems complicate the clinical application of opioids.

[0003] Chinese patent CN113707263B discloses a method, device and computer equipment for evaluating drug effectiveness based on population division. By dividing the sample population into multiple subgroups and determining the target subgroup to which the patient to be evaluated belongs from the multiple subgroups, it is possible to evaluate whether a specific drug is effective for the patient to be evaluated based on the effectiveness parameters corresponding to the target subgroup. The above scheme can accurately evaluate the effectiveness of a specific drug for individual patients, but does not consider the influencing factors of individual gene polymorphism. Chinese patent CN115547513B discloses a kit for realizing a method for predicting the dosage of intrathecal opioid analgesics, by detecting the genotype of each SNP (single nucleotide polymorphism) site of the patient's ABCB1, and performing a combined analysis of the genotype of each site to determine the dosage of intrathecal opioid analgesics required by the patient. The above patent takes into account the influence of individual gene polymorphism on the dosage of opioid drugs, mainly based on the limited primer pairs in the kit to achieve the determination of the limited gene sites of the ABCB1 gene.

[0004] Intelligent medication decision support has been a research hotspot in the field of medical informationization in recent years. How to achieve intelligent medication decision support based on individual genetic polymorphism is a technical problem that needs to be solved urgently in the field of medical informationization. Summary of the invention

[0005] In order to solve the above technical problems, this application proposes the following technical solutions: In a first aspect, the present application provides a method for evaluating individualized analgesia of opioid drugs based on gene polymorphism, comprising: Evidence-based evaluation of opioid analgesia-related SNP sites to establish recommendation strength standards; Screen all the SNP sites related to the analgesic effect of opioids detected in the database and the samples, and set the pain impact parameters of SNPs in different levels according to the classification levels of the recommended strength standard; Establish a drug dosage prediction plan based on the pain impact parameters and the number of SNP variations, and adjust the drug dosage prediction plan based on the analgesic attribute characteristics corresponding to the patient to be evaluated determined by the overall score of the patient to be evaluated under different clinical variables.

[0006] In a possible implementation manner, the establishment of the recommended strength standard for SNP sites related to the evidence-based evaluation of opioid analgesia includes: Based on the sample data of each patient in the evidence-based materials under different clinical variables, evaluate the analgesic effectiveness parameters of opioids for the samples according to their opioid analgesic usage information and pain relief degree, and the clinical variables include: tumor type, pain score, and basic information; According to the analgesic effectiveness parameters corresponding to the samples, divide them into a 6-level scoring system of level 1A, level 1B, level 2A, level 2B, level 3, and level 4 according to the parameter values to evaluate the importance of different opioid analgesic SNP sites.

[0007] In a possible implementation manner, the screening of all the SNP sites related to the analgesic effect of opioids detected in the database and the samples, and setting the pain impact parameters of SNPs in different levels according to the classification levels of the recommended strength standard includes: Determine the increase or decrease multiple of drug demand, the number of allele types, and the frequency of alleles; Establish the pain impact parameter formula of the SNP , is the increase or decrease multiple of drug demand, where an increase is a positive value and a decrease is a negative value, is the number of allele types, is the frequency of the

[0008] In a possible implementation manner, the establishment of a drug dosage prediction plan based on the pain impact parameters and the number of SNP variations includes: First, select the target population for detection at the SNP sites with high correlation and strong recommended strength; For the target population, select cancer pain patients using morphine and oxycodone respectively. All patients need to be included in the same cancer type and the same stage of cancer pain patients with long-term medication and good pain control; Statistically analyze the pain relief situation before and after opioid use in patients, the dosage of opioids, and the occurrence of nausea, vomiting, and respiratory depression in patients. Use correlation statistical analysis to evaluate the correlation between the polymorphism of the above gene loci and drug dosage and drug adverse reactions; Perform multiple linear regression analysis on the dosage situation using the stepwise bidirectional selection method to establish a dose prediction formula based on the sensitivity of gene loci.

[0009] In a possible implementation, the dose prediction formula is: opioid analgesic demand = (1.356 + 0.530) * (number of SNP variations of gene locus classification 1 * PI + 0.283) * (number of SNP variations of gene locus classification 2 * PI + 0.164) *... * (number of SNP variations of gene locus classification m * PI + 0.155) * (pain sensation delay time * pain score for different tumor stage classifications - 0.006) * body weight, where the unit of opioid analgesic demand is μg / kg.

[0010] In a possible implementation, adjusting the drug dose prediction plan according to the analgesic attribute characteristics corresponding to the patient to be evaluated determined based on the overall score of the patient to be evaluated under different clinical variables includes: First, select the patient to be evaluated for grouping. The experimental group is first given genomic testing and then re-grouped according to the detected genotypes; For each group, use the dose prediction formula and the basic situation of the patient to formulate the initial dosing dose and dosing frequency of the analgesic drug according to different genotypes. At the same time, pre-treat patients with genes sensitive to adverse reactions or replace the drug; Finally, during the specific evaluation period, determine the titration duration, pain relief degree, and incidence and degree of adverse reactions of the analgesic drug in each group, judge the actual application effect of the gene locus polymorphism in the clinical application of opioids, and correct the dose prediction formula.

[0011] In a possible implementation, the correction value for correcting the dose prediction formula Is expressed as: where N is the total number of alleles, is the allele frequency adjacent to the

[0012] In a possible implementation, the gene loci with relevance after screening are comprehensively evaluated again, and the Delphi expert consultation method is used to assign values according to the metabolic-related genomic loci, pain relief effect-related gene loci, and drug adverse reaction-related loci. Finally, the gene locus with the greatest relevance and the highest score is selected as the basic detection combination; the detection criteria, clinical application consensus, and knowledge base rules are determined; and finally, an opioid analgesic clinical medication decision-making system that incorporates patient individual information, genomics information, and pain assessment data is formed.

[0013] In a second aspect, an embodiment of the present application provides an individualized analgesia evaluation system for opioid drugs based on gene polymorphism, including: A standard determination module for establishing a recommendation strength standard by evidence-based evaluation of opioid analgesia-related SNP loci; An influence parameter setting module for screening SNP loci related to the analgesic effect of opioid drugs detected in all databases and samples, and setting pain influence parameters for SNPs in different levels according to the classification levels of the recommendation strength standard; An analgesia evaluation module for establishing a drug dosage prediction plan based on the pain influence parameter and the number of SNP variations, and adjusting the drug dosage prediction plan based on the analgesic attribute characteristics corresponding to the patient to be evaluated determined by the overall score of the patient to be evaluated under different clinical variables.

[0014] In a third aspect, an embodiment of the present application provides an individualized analgesia evaluation device for opioid drugs based on gene polymorphism, including: A processor; A memory; And a computer program, where the computer program is stored in the memory, and the computer program includes instructions that, when executed by the processor, cause the individualized analgesia evaluation device for opioid drugs based on gene polymorphism to execute the individualized analgesia evaluation method for opioid drugs based on gene polymorphism according to any possible implementation manner of the first aspect.

[0015] In the embodiment of the present application, the relevance between different genotypes of cancer pain patients and the demand for opioid drugs is evaluated based on evidence, the patient individual information and pain assessment data are incorporated, and the drug dosage is adjusted according to the SNP locus variation situation, realizing the clinical medication decision of opioid analgesic drugs based on gene polymorphism, and providing intelligent medication decision support for the drug dosage according to the detection results. It can significantly improve the standardization degree of the use of opioid drugs in cancer pain patients, especially in patients with middle and late stage cancer pain, reduce the occurrence of non-standard use and abuse of opioid analgesic drugs, and benefit cancer patients. Description of the Drawings

[0016] Figure 1Schematic flowchart of an individualized analgesia evaluation method for opioid drugs based on gene polymorphism provided by an embodiment of the present application; Figure 2 Molecular schematic diagram related to the action of opioid analgesics provided by an embodiment of the present application; Figure 3 Schematic diagram of an individualized analgesia evaluation system for opioid drugs based on gene polymorphism provided by an embodiment of the present application; Figure 4 Schematic diagram of an individualized analgesia evaluation device for opioid drugs based on gene polymorphism provided by an embodiment of the present application. Detailed implementation manners

[0017] The present solution will be elaborated below in conjunction with the accompanying drawings and the detailed implementation manners.

[0018] Refer to Figure 1 , the individualized analgesia evaluation method for opioid drugs based on gene polymorphism provided by an embodiment of the present application includes: S101, performing evidence-based evaluation on SNP sites related to opioid analgesia to establish a recommended strength standard.

[0019] Based on the sample data of each patient under different clinical variables in the evidence-based materials, for the usage information and pain relief degree of their opioid analgesics, evaluate the analgesic effectiveness parameters of the opioid drugs for the samples. The clinical variables include: tumor type, pain score, and basic information.

[0020] According to the relevant pathways of the molecular mechanism of opioid analgesia, preliminary gene locus screening and analysis have been carried out on the associated molecules. The relevant loci with more research evidence included are preliminarily screened and evaluated, specifically divided into three categories of gene loci related to drug metabolism, transporters, and drug targets, such as Figure 2 and Table 1, Figure 2 where CYP is cytochrome P450 enzyme, UGT is UDP-glucuronosyltransferase, GIRK is G protein-activated inward rectifier potassium ion, cAMP is cyclic adenosine monophosphate, and PKA is protein kinase A. The next step will be a systematic evidence-based evaluation.

[0021] Table 1 Screening of research on loci related to opioid genomics At the same time, evidence-based evaluation of the relevance of opioid adverse reaction-related sites was performed, as shown in Table 2. For the above sites, the drug adverse reaction sentinel monitoring system was used to retrieve people who used opioids and had various adverse drug reactions for analysis and evaluation, and genetic testing was performed on the relevant associated sites. The detection screened out two statistically significant opioid adverse reaction-related genes, one of which was CYP2D6, which was related to constipation caused by opioids. Constipation is an intolerable adverse reaction of opioid analgesics, which generally persists during opioid treatment. One of the possible reasons is that after the concentration of opioids increases, the patient's bowel habits or defecation patterns change. By retrieving 30 patients who had severe constipation after using oxycodone from 2018 to 2021, the CYP2D6 gene typing was detected by XL-PCR. IM type and NM type were significantly more than PM type, and IM type showed a trend of more than NM type as shown in Table 3. The sample size needs to be increased in the future to evaluate the correlation by stratification.

[0022] Table 2 Summary of studies on association between gene loci and adverse reactions to opioids Table 3 CYP2D6 genotyping in 30 patients with opioid-induced constipation At the same time, the bowel function index questionnaire was used to evaluate the severity of constipation in different groups. Eight people in the IM group and six people in the NM group were selected with no difference in gender, age, KPS score, BMI, liver and kidney function, metastasis, tumor type, and oxycodone use to evaluate the correlation. The average BFI score of the IM group was significantly higher than that of the NM group, as shown in Table 4.

[0023] Table 4 CYP2D6 genotype and severity of opioid-induced constipation Using evidence-based medicine evaluation methods, we fully searched the clinical guidelines for cancer pain treatment and the pharmacogenomics research databases (PharmGKB, 1000Genomes, CYP database, SNPedia, WarfarinDosing, SNPs3D, ugt-pharmacogenomics, etc.), fully identified candidate genes related to pain, opioid efficacy, and toxicity, especially respiratory depression, and the analgesic effectiveness of different drugs and different gene loci. We established a recommendation strength standard and listed a 6-level scoring system: 1A, 1B, 2A, 2B, 3, and 4, to evaluate the importance of different opioid analgesic SNP loci.

[0024] The above are the key points of the embodiments of the present application. The focus is on comprehensively screening candidate genes, strictly following the evaluation methods of evidence-based medicine, collecting candidate genes related to pain and gene loci related to the efficacy and adverse reactions of opioid drugs, establishing a target gene group database, and evaluating the association strength.

[0025] S102, screen SNP loci related to the analgesic effect of opioid drugs detected in all databases and samples, and set pain influence parameters for SNPs in different levels according to the classification levels of the recommended strength standard.

[0026] Determine the increase or decrease multiple of drug demand, the number of allele types, and the frequency of alleles. Establish the pain influence parameter formula for the SNP , is the increase or decrease multiple of drug demand, where an increase is a positive value and a decrease is a negative value, is the number of allele types, is the frequency of the

[0027] S103, establish a drug dosage prediction plan according to the pain influence parameter and the number of SNP variations, and adjust the drug dosage prediction plan based on the analgesic attribute characteristics corresponding to the patient to be evaluated determined by the overall score of the patient to be evaluated under different clinical variables.

[0028] In this embodiment, first, select target populations for detection at loci with high correlation and strong recommended strength (1A level, 1B level), such as gene loci related to drug metabolism: CYP3A4*18B; gene loci related to drug efficacy: u opioid receptor gene locus (OPRMl A118G), dopamine transporter gene locus (DAT1 VNTR), ABCB1 gene, etc. For the target populations, 100 cancer pain patients using morphine and oxycodone respectively are selected. All patients need to be included in the same cancer type and the same stage of cancer pain patients who have been using drugs for a long time and have good pain control (intended to select patients with stage III lung cancer) for enrollment. Statistics are made on the pain relief situation before and after the use of opioid drugs by the patients, the dosage of opioid drugs, and the occurrence of nausea, vomiting, and respiratory depression in the patients. The correlation between the polymorphism of the above gene loci and drug dosage and drug adverse reactions is evaluated by correlation statistical analysis, and a dose prediction formula based on gene locus sensitivity is established by stepwise two-way selection method for the dosage situation for multiple linear regression analysis.

[0029] Specifically, the sample can be the patient's blood sample or oral exfoliated cells for genotype detection, and the detection method can be XL-PCR, immunofluorescence method or sequencing according to the genotype; SPSS 20.0 statistical software is used for data analysis, and the count data uses For the test, descriptive classification data comparison adopted Logistic regression analysis, and one-way ANOVA was used for comparison between different genotype groups.

[0030] The finally established dose prediction formula is: opioid analgesic demand = (1.356 + 0.530) * (number of SNP variations of gene locus classification 1 * PI + 0.283) * (number of SNP variations of gene locus classification 2 * PI + 0.164) *... * (number of SNP variations of gene locus classification m * PI + 0.155) * (pain sensation delay time * pain score for different tumor stages - 0.006) * body weight, where the unit of opioid analgesic demand is μg / kg.

[0031] In this embodiment, in order to further correct the dose prediction formula, lung cancer patients were first selected for grouping (100 people in each of the experimental group and the control group). The experimental group was first given genomics testing, and then re-grouped according to the detected genotypes. Each subgroup formulated the initial administration dose and administration frequency of analgesic drugs according to different genotypes using the prediction formula and the basic conditions of the patients. At the same time, patients with genes sensitive to adverse reactions were pre-treated or the drugs were replaced. Finally, the titration duration of analgesic drugs, the degree of pain relief (VAS score), and the incidence and degree of adverse reactions in each group during hospitalization were specifically evaluated to determine the practical application effect of gene locus polymorphism in the clinical application of opioid drugs and correct the dose prediction formula.

[0032] In this embodiment, the correction value for correcting the dose prediction formula is expressed as: where N is the total number of alleles, is the allele frequency adjacent to the th allele.

[0033] Comprehensive evaluation was carried out on the gene loci with strong correlation after screening. Using methods such as the Delphi expert consultation method, according to three categories: metabolism-related genomic loci, pain relief-related gene loci, and drug adverse reaction-related loci, assignments were made. Finally, the gene locus with the greatest correlation and the highest score was selected as the basic detection combination to determine the detection standard, clinical application consensus, and knowledge base rules. Finally, using information technology and artificial intelligence algorithms, an opioid analgesic clinical medication decision-making system incorporating patients' individualized information, genomics information, and pain assessment data was formed. This system can provide relevant warning information and initial recommended administration doses.

[0034] Corresponding to the method for individualized analgesia evaluation of opioid drugs based on gene polymorphism provided in the above embodiment, the present application also provides an embodiment of a system for individualized analgesia evaluation of opioid drugs based on gene polymorphism.

[0035] See Figure 3 , the individualized analgesia evaluation system 20 for opioid drugs based on gene polymorphism in this embodiment includes: A standard determination module 201, configured to establish a recommended strength standard by evidence-based evaluation of SNP sites related to opioid analgesia.

[0036] An influence parameter setting module 202, configured to screen SNP sites related to the analgesic effect of opioid drugs detected in all databases and samples, and set pain influence parameters of SNPs in different levels according to the classification levels of the recommended strength standard.

[0037] An analgesia evaluation module 203, configured to establish a drug dosage prediction plan according to the pain influence parameters and the number of SNP variations, and adjust the drug dosage prediction plan based on the analgesic attribute characteristics corresponding to the patient to be evaluated determined by the overall score of the patient to be evaluated under different clinical variables.

[0038] This application also provides an embodiment of an individualized analgesia evaluation device for opioid drugs based on gene polymorphism. See Figure 4 , the individualized analgesia evaluation device 30 for opioid drugs based on gene polymorphism in this embodiment includes: A processor 301, a memory 302, and a communication unit 303. These components communicate through one or more buses. Those skilled in the art can understand that the structure of the individualized analgesia evaluation device for opioid drugs based on gene polymorphism shown in the figure does not constitute a limitation on the embodiments of the present application. It can be a bus structure, a star structure, and can also include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0039] Among them, the communication unit 303 is configured to establish a communication channel, so that the individualized analgesia evaluation device for opioid drugs based on gene polymorphism can communicate with other electronic devices.

[0040] The processor 301 is the control center of the individualized analgesia evaluation device for opioid drugs based on gene polymorphism, connects various parts of the entire individualized analgesia evaluation device for opioid drugs based on gene polymorphism through various interfaces and lines, and executes various functions and / or processes data of the individualized analgesia evaluation device for opioid drugs based on gene polymorphism by running or executing software programs and / or modules stored in the memory 302, and calling data stored in the memory.

[0041] The processor may be composed of an integrated circuit (IC). For example, it may be composed of a single packaged IC, or may be composed of multiple packaged ICs with the same or different functions connected together. For example, the processor 301 may include only a central processing unit (CPU). In the embodiments of the present application, the CPU may be a single-core processor or may include multiple cores.

[0042] A memory 302, configured to store execution instructions of the processor 301. The memory 302 may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk, or an optical disk.

[0043] When the execution instructions in the memory 302 are executed by the processor 301, the opioid individualized analgesia evaluation device 30 based on gene polymorphism can execute some or all of the steps in the above method embodiments.

[0044] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may represent the cases of A existing alone, A and B existing simultaneously, and B existing alone. Where A and B may be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c may represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c may be single or multiple.

[0045] As described above, the foregoing are only specific embodiments of the present application. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all such changes or substitutions should be covered within the protection scope of the present application. The protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for evaluating individualized analgesia of opioid drugs based on gene polymorphism, characterized in that: include: Evidence-based evaluation of opioid analgesia-related SNP sites to establish recommendation strength standards; Screen all SNP sites related to the analgesic effect of opioid drugs that have been detected in the database and samples, and set the pain impact parameters of SNPs in different levels according to the classification levels of the recommendation intensity standard; A dosage prediction scheme is established according to the pain influencing parameters and the SNP variance, and the dosage prediction scheme is adjusted based on the analgesic attribute characteristics corresponding to the patient to be evaluated determined based on the overall score of the patient to be evaluated under different clinical variables.

2. The method for evaluating individualized analgesia of opioid drugs based on gene polymorphism according to claim 1, characterized in that: The evidence-based evaluation of opioid analgesia-related SNP sites established recommendation strength standards, including: Based on the sample data of each patient under different clinical variables in the evidence-based materials, the analgesic effectiveness parameters of opioids for the samples were evaluated according to their opioid analgesic usage information and pain relief degree. The clinical variables included: tumor type, pain score and basic information; According to the analgesic effectiveness parameters corresponding to the samples, a 6-level scoring system is formed according to the parameter values: 1A, 1B, 2A, 2B, 3, and 4, to evaluate the importance of different opioid analgesic SNP sites.

3. The method for evaluating individualized analgesia of opioid drugs based on gene polymorphism according to claim 1, characterized in that: The method comprises screening all SNP sites related to the analgesic effect of opioid drugs detected in the database and samples, and setting pain impact parameters of SNPs at different levels according to the classification levels of the recommendation intensity standard, including: Determine the fold increase or decrease in drug requirement, the number of allele species, and the allele frequency; Establishing the pain impact parameter formula of the SNP , is the multiple of drug demand increase or decrease, where increase is positive and decrease is negative. is the number of allele types, For the The frequency of an allele.

4. The method for evaluating individualized analgesia of opioid drugs based on gene polymorphism according to claim 3, characterized in that: The method of establishing a dosage prediction scheme according to the pain impact parameter and the SNP variation number comprises: SNP loci with high association and strong recommendation strength are preferred to select the target population for testing; The target population is selected from cancer pain patients who use morphine and oxycodone respectively. All patients with the same cancer type and the same stage who use long-term medication for cancer pain and have good pain control are included in the group; The pain relief before and after the use of opioids, the dosage of opioids, and the occurrence of nausea, vomiting, and respiratory depression in patients were recorded. The correlation between the polymorphisms of the above gene loci and drug dosage and adverse drug reactions was evaluated by correlation statistical analysis. The dosage was analyzed by multiple linear regression using stepwise two-way selection method to establish a dosage prediction formula based on gene locus sensitivity.

5. The method for evaluating individualized analgesia of opioid drugs based on gene polymorphism according to claim 4, characterized in that: The dosage prediction formula is: Opioid analgesic requirement = (1.356+0.530)*(SNP variation of gene locus classification 1*PI+0.283)*(SNP variation of gene locus classification 2*PI+0.164)*……*(SNP variation of gene locus classification m*PI+0.155)*(pain sensation delay time* pain score of different tumor types and stages-0.006)*body weight, where the unit of opioid analgesic requirement is μg / kg.

6. The method for evaluating individualized analgesia of opioids based on gene polymorphism according to claim 5, characterized in that: The step of adjusting the drug dosage prediction scheme based on the analgesic attribute characteristics corresponding to the patient to be evaluated determined based on the overall score of the patient to be evaluated under different clinical variables includes: First, the patients to be evaluated were selected and grouped. The experimental group was first given genomic testing, and then secondary grouping was performed based on the detected genotypes; Each group used the dosage prediction formula and the basic conditions of the patients to determine the initial dosage and frequency of analgesics according to different genotypes, and pre-treated or replaced the drugs for patients with genes that were sensitive to adverse reactions. Finally, the duration of analgesic titration, degree of pain relief, and incidence and extent of adverse reactions in each group were evaluated specifically to determine the actual application effect of gene locus polymorphism in the clinical application of opioids and to correct the dosage prediction formula.

7. The method for evaluating individualized analgesia of opioids based on gene polymorphism according to claim 6, characterized in that: Correction values ​​used in the corrected dose prediction formula It is expressed as: Where N is the total number of alleles, For the The allele frequencies of adjacent alleles.

8. The method for evaluating individualized analgesia of opioids based on gene polymorphism according to claim 7, characterized in that: The gene loci with correlation after screening were comprehensively evaluated again, and the Delph expert consultation method was used to assign values ​​according to the metabolism-related genomic loci, analgesic effect-related gene loci and adverse drug reaction-related loci. Finally, the gene loci with the greatest correlation and the highest score were selected as the basic detection combination. Determine testing standards, clinical application consensus and knowledge base rules; finally, form a clinical medication decision-making system for opioid analgesics that incorporates patient individual information, genomic information and pain assessment data.

9. An opioid personalized analgesia evaluation system based on gene polymorphism, characterized in that: include: The standard determination module is used to establish the recommendation strength standard for the evidence-based evaluation of opioid analgesia-related SNP sites; The impact parameter setting module is used to screen all SNP sites related to the analgesic effect of opioid drugs that have been detected in the database and samples, and set the pain impact parameters of SNPs in different levels according to the division levels of the recommendation intensity standard; The analgesic evaluation module is used to establish a drug dosage prediction scheme based on the pain influencing parameters and SNP variance, and adjust the drug dosage prediction scheme based on the analgesic attribute characteristics corresponding to the patient to be evaluated determined based on the overall score of the patient to be evaluated under different clinical variables.

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