使用抗肿瘤坏死因子α（抗TNF-α）药物实现克罗恩病（CD）的长期临床缓解仍然具有挑战性。本研究旨在根据患者的临床特征建立预测模型，利用机器学习方法进行预测英夫利昔单抗 (IFX) 的长期疗效。2013 年 6 月至 2022 年 1 月期间，来自 3 个炎症性肠病 (IBD) 中心的 3 个队列包括 746 名 CD 患者。收集了基线、14 岁、30 岁和IFX 治疗后 52 周。采用三种机器学习方法来开发基于 23 个基线预测因子的预测模型。使用 SHapley Additive exPlanations (SHAP) 算法来剖析潜在的预测因子，并应用潜在类混合模型 (LCMM) 对血常规测试随着长期 IFX 治疗的纵向变化进行轨迹分析。XGBoost 模型表现出最佳长期反应者和非反应者之间的歧视。在内部训练和测试集中，该模型的 AUC 分别为 0.91（95% CI，0.86-0.95）和 0.71（95% CI，0.66-0.87）。此外，它在独立外部队列中实现了中等预测性能，AUC 为 0.68（95% CI，0.59-0.77）。 SHAP 算法揭示了与疾病相关的实验室测量结果，特别是血红蛋白 (HB)、白细胞 (WBC)、红细胞沉降率 (ESR)、白蛋白 (ALB) 和血小板 (PLT)，以及诊断时的年龄和蒙特利尔分类，作为最有影响力的预测者。此外，基于动态实验室测试确定了 2 个不同的患者群，用于监测长期缓解情况。建立的预测模型在区分 IFX 治疗的长期反应者和无反应者方面表现出显着的区分能力。不同患者群的识别进一步强调了在 CD 管理中采用量身定制的治疗方法的必要性。© 作者 2024。由牛津大学出版社代表克罗恩病出版

Achieving long-term clinical remission in Crohn's disease (CD) with antitumor necrosis factor α (anti-TNF-α) agents remains challenging.This study aims to establish a prediction model based on patients' clinical characteristics using a machine-learning approach to predict the long-term efficacy of infliximab (IFX).Three cohorts comprising 746 patients with CD were included from 3 inflammatory bowel disease (IBD) centers between June 2013 and January 2022. Clinical records were collected from baseline, 14-, 30-, and 52-week post-IFX treatment. Three machine-learning approaches were employed to develop predictive models based on 23 baseline predictors. The SHapley Additive exPlanations (SHAP) algorithm was used to dissect underlying predictors, and latent class mixed model (LCMM) was applied for trajectory analysis of the longitudinal change of blood routine tests along with long-term IFX therapy.The XGBoost model exhibited the best discrimination between long-term responders and nonresponders. In the internal training and testing set, the model achieved an AUC of 0.91 (95% CI, 0.86-0.95) and 0.71 (95% CI, 0.66-0.87), respectively. Moreover, it achieved a moderate predictive performance in the independent external cohort, with an AUC of 0.68 (95% CI, 0.59-0.77). The SHAP algorithm revealed disease-relevant laboratory measurements, notably hemoglobin (HB), white blood cells (WBC), erythrocyte sedimentation rate (ESR), albumin (ALB), and platelets (PLT), alongside age at diagnosis and the Montreal classification, as the most influential predictors. Furthermore, 2 distinct patient clusters based on dynamic laboratory tests were identified for monitoring the long-term remission.The established prediction model demonstrated remarkable discriminatory power in distinguishing long-term responders from nonresponders to IFX therapy. The identification of distinct patient clusters further emphasizes the need for tailored therapeutic approaches in CD management.© The Author(s) 2024. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.