{"title":"Development and validation of a risk-prediction model for adverse drug reactions in real-world cancer patients treated with anlotinib.","authors":"Jiajia Qian, Cong Ruan, Yunyun Cai, Weiyi Yi, Jiyong Liu, Rui Xu","doi":"10.1177/20420986251328687","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The risk factors related to the adverse drug reactions (ADRs) of anlotinib have been rarely investigated. In addition, a corresponding risk prediction model has not been established in China pertaining to anlotinib-related ADRs.</p><p><strong>Objectives: </strong>To manage ADRs more efficiently and improve the prognosis of patients administered anlotinib.</p><p><strong>Design: </strong>A retrospective analysis was conducted using the medical records of patients diagnosed with cancer who were administered anlotinib after hospitalization between January 1, 2020, and December 31, 2023.</p><p><strong>Methods: </strong>We performed a combination of univariate analysis and multivariate binary logistic regression analysis to identify significant factors that can accurately predict ADRs. Model fitting was performed using forward selection. The accuracy of the prediction model was expressed as the area under the receiver operating characteristic curve (AUC). The final ADR risk model was validated.</p><p><strong>Results: </strong>In this study, 300 patients who were administered anlotinib were included. Among them, 238 (79.33%) patients experienced at least one ADR. Diagnosis, combination treatment, distant metastasis, treatment lines, and cumulative dose were independent risk factors for the ADRs of anlotinib. The AUC and the concordance index of the nomogram constructed from the above five factors were 0.790 and 0.789, respectively. The results of the Hosmer-Lemeshow test showed that the model was a good fit (<i>p</i> = 0.811). In addition, the decision curve analysis demonstrated a significantly higher net benefit of the model. The external validation indicated that the prediction nomogram was reliable.</p><p><strong>Conclusion: </strong>We developed and validated a simple model to use the ADR risk score in patients who were administered anlotinib. This risk prediction model was well-calibrated and discriminative. It can be used as a reference for clinical decision-making. It has clinical significance for preventing ADRs, improving the prognosis of patients, and providing support for the rational use of drugs.</p>","PeriodicalId":23012,"journal":{"name":"Therapeutic Advances in Drug Safety","volume":"16 ","pages":"20420986251328687"},"PeriodicalIF":3.4000,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11946284/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Therapeutic Advances in Drug Safety","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/20420986251328687","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: The risk factors related to the adverse drug reactions (ADRs) of anlotinib have been rarely investigated. In addition, a corresponding risk prediction model has not been established in China pertaining to anlotinib-related ADRs.
Objectives: To manage ADRs more efficiently and improve the prognosis of patients administered anlotinib.
Design: A retrospective analysis was conducted using the medical records of patients diagnosed with cancer who were administered anlotinib after hospitalization between January 1, 2020, and December 31, 2023.
Methods: We performed a combination of univariate analysis and multivariate binary logistic regression analysis to identify significant factors that can accurately predict ADRs. Model fitting was performed using forward selection. The accuracy of the prediction model was expressed as the area under the receiver operating characteristic curve (AUC). The final ADR risk model was validated.
Results: In this study, 300 patients who were administered anlotinib were included. Among them, 238 (79.33%) patients experienced at least one ADR. Diagnosis, combination treatment, distant metastasis, treatment lines, and cumulative dose were independent risk factors for the ADRs of anlotinib. The AUC and the concordance index of the nomogram constructed from the above five factors were 0.790 and 0.789, respectively. The results of the Hosmer-Lemeshow test showed that the model was a good fit (p = 0.811). In addition, the decision curve analysis demonstrated a significantly higher net benefit of the model. The external validation indicated that the prediction nomogram was reliable.
Conclusion: We developed and validated a simple model to use the ADR risk score in patients who were administered anlotinib. This risk prediction model was well-calibrated and discriminative. It can be used as a reference for clinical decision-making. It has clinical significance for preventing ADRs, improving the prognosis of patients, and providing support for the rational use of drugs.
期刊介绍：
Therapeutic Advances in Drug Safety delivers the highest quality peer-reviewed articles, reviews, and scholarly comment on pioneering efforts and innovative studies pertaining to the safe use of drugs in patients.
The journal has a strong clinical and pharmacological focus and is aimed at clinicians and researchers in drug safety, providing a forum in print and online for publishing the highest quality articles in this area. The editors welcome articles of current interest on research across all areas of drug safety, including therapeutic drug monitoring, pharmacoepidemiology, adverse drug reactions, drug interactions, pharmacokinetics, pharmacovigilance, medication/prescribing errors, risk management, ethics and regulation.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
