Identifying Risk Factors for Poor Efficacy of Adalimumab Treatment in Patients With Crohn’s Disease: Insights From Machine Learning Models

IF 3.7 4区医学 Q1 DERMATOLOGY

Dermatologic Therapy Pub Date : 2025-06-12 DOI:10.1155/dth/1996661

Xiaojun Li, Maomao Tang, Jie Zhang, Yongjun Wang, Chunlian Wang, Chunhui Ouyang

{"title":"Identifying Risk Factors for Poor Efficacy of Adalimumab Treatment in Patients With Crohn’s Disease: Insights From Machine Learning Models","authors":"Xiaojun Li, Maomao Tang, Jie Zhang, Yongjun Wang, Chunlian Wang, Chunhui Ouyang","doi":"10.1155/dth/1996661","DOIUrl":null,"url":null,"abstract":"<div>\n Aim: Adalimumab (ADA) is an effective treatment for Crohn’s disease (CD); however, some patients still experience adverse reactions and nonresponse. This study aimed to explore the risk factors associated with ADA poor efficacy through machine learning algorithms, which provide promising guidance for the management of ADA in clinical practice.\n Methods: This single-center investigation included 114 CD patients treated with ADA in the Department of Gastroenterology from January 2020 to January 2023. Risk factors associated with each poor efficacy event were explored using logistic regression and machine learning algorithms. Shapley additive explanations (SHAP) and partial dependence plot methods were used to analyze the risk factors of each event.\n Results: The results showed 8 of these patients experienced primary non-response (PNR), 35 patients developed secondary loss of response (LOR), and 27.2% (31/114) of patients experienced at least one adverse events (AEs). After comparing the fit of the models established by 10 algorithms, the risk factors associated with PNR, LOR, and AEs were analyzed using the logistic regression algorithm, KNN algorithm, and Extra Tree algorithm, respectively. The most important variables related to the PNR, LOR, and AEs events were the history of corticosteroid use, baseline CDAI, and uric acid, respectively.\n Conclusions: This study confirmed the efficacy of ADA for clinical practice in the Chinese CD population, and that patients with a history of corticosteroid use, high levels of disease activity, and high inflammatory state before ADA treatment were associated with increased risks of poor efficacy.\n </div>","PeriodicalId":11045,"journal":{"name":"Dermatologic Therapy","volume":"2025 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/dth/1996661","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Dermatologic Therapy","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/dth/1996661","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"DERMATOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Aim: Adalimumab (ADA) is an effective treatment for Crohn’s disease (CD); however, some patients still experience adverse reactions and nonresponse. This study aimed to explore the risk factors associated with ADA poor efficacy through machine learning algorithms, which provide promising guidance for the management of ADA in clinical practice.

Methods: This single-center investigation included 114 CD patients treated with ADA in the Department of Gastroenterology from January 2020 to January 2023. Risk factors associated with each poor efficacy event were explored using logistic regression and machine learning algorithms. Shapley additive explanations (SHAP) and partial dependence plot methods were used to analyze the risk factors of each event.

Results: The results showed 8 of these patients experienced primary non-response (PNR), 35 patients developed secondary loss of response (LOR), and 27.2% (31/114) of patients experienced at least one adverse events (AEs). After comparing the fit of the models established by 10 algorithms, the risk factors associated with PNR, LOR, and AEs were analyzed using the logistic regression algorithm, KNN algorithm, and Extra Tree algorithm, respectively. The most important variables related to the PNR, LOR, and AEs events were the history of corticosteroid use, baseline CDAI, and uric acid, respectively.

Conclusions: This study confirmed the efficacy of ADA for clinical practice in the Chinese CD population, and that patients with a history of corticosteroid use, high levels of disease activity, and high inflammatory state before ADA treatment were associated with increased risks of poor efficacy.

查看原文本刊更多论文

确定阿达木单抗治疗克罗恩病疗效差的危险因素：来自机器学习模型的见解

目的：阿达木单抗（ADA）是治疗克罗恩病（CD）的有效药物；然而，一些患者仍然会出现不良反应和无反应。本研究旨在通过机器学习算法探索ADA疗效差的相关危险因素，为临床对ADA的管理提供有希望的指导。方法：该单中心调查纳入了2020年1月至2023年1月在消化内科接受ADA治疗的114例CD患者。使用逻辑回归和机器学习算法探索与每个不良疗效事件相关的风险因素。采用Shapley加性解释（SHAP）和部分依赖图方法分析各事件的危险因素。结果：8例患者出现原发性无反应（PNR）， 35例患者出现继发性反应丧失（LOR）， 27.2%（31/114）的患者出现至少一次不良事件（ae）。在比较10种算法建立的模型的拟合性后，分别使用logistic回归算法、KNN算法和Extra Tree算法对PNR、LOR和ae的相关危险因素进行分析。与PNR、LOR和ae事件相关的最重要变量分别是皮质类固醇使用史、基线CDAI和尿酸。结论：本研究证实了ADA对中国CD人群的临床疗效，ADA治疗前有皮质类固醇使用史、疾病活动度高、炎症状态高的患者疗效差的风险增加。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Dermatologic Therapy 医学-皮肤病学

CiteScore

7.00

自引率

8.30%

发文量

711

审稿时长

3 months

期刊介绍： Dermatologic Therapy has been created to fill an important void in the dermatologic literature: the lack of a readily available source of up-to-date information on the treatment of specific cutaneous diseases and the practical application of specific treatment modalities. Each issue of the journal consists of a series of scholarly review articles written by leaders in dermatology in which they describe, in very specific terms, how they treat particular cutaneous diseases and how they use specific therapeutic agents. The information contained in each issue is so practical and detailed that the reader should be able to directly apply various treatment approaches to daily clinical situations. Because of the specific and practical nature of this publication, Dermatologic Therapy not only serves as a readily available resource for the day-to-day treatment of patients, but also as an evolving therapeutic textbook for the treatment of dermatologic diseases.