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性结肠炎中的风险分层生物有效性:一个多中心机器学习研究

Pingxin Zhang^1,2, Chuhan Zhang¹, Zishan Liu¹

¹Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.

Inflammatory bowel diseases

|December 22, 2025

概括

一个新的随机森林模型准确地预测了性结肠炎 (UC) 的进展,确定了从生物疗法中获益最多的患者. 该工具通过分层风险来改善治疗结果,有助于个性化UC管理.

科学领域:

机器学习在胃肠病学中的应用.
慢性疾病管理的预测建模.
在炎症性肠道疾病的个性化药物.

背景情况:

性结肠炎 (UC) 呈现出可变的临床过程,使得预后和治疗决策具有挑战性.
目前用于预测UC进展和对生物疗法的反应的现有方法不足.
需要先进的工具来分层UC患者,以优化治疗策略.

研究的目的:

开发和验证一种机器学习模型,以准确地对性结肠炎 (UC) 患者进行风险分层.
评估该模型在预测疾病进展和优化接受生物疗法患者的治疗结果方面的有用性.

主要方法:

一个多中心的回顾性研究,涉及481名UC患者进行培训和131人进行外部验证.
为疾病进展 (治疗升级,住院,手术) 开发四种预测模型 (Cox回归,物流回归,随机森林,XGBoost).
将235名用生物治疗的UC患者分为风险组,使用最佳模型来评估粘膜愈合和复发等结果.

主要成果:

随机森林模型显示出优异的预测性能 (AUC 0.959培训,0.759验证).
与低风险患者相比,接受生物制剂的高风险患者的粘膜愈合率显著降低,复发,住院和急性严重UC的风险更高.
在血清性缓解,手术率或需要在风险组之间进行生物切换方面没有发现显著差异.

结论:

开发的随机森林模型有效地对UC患者进行了分层,识别了对生物疗法的独特反应模式.
该模型区分了从及时生物制剂中受益的患者与需要加强治疗策略的患者.
这种预测框架支持个性化的UC管理,强调了未来验证的必要性.

关键词:

生物药物疗效疗效生物药物疾病的进展疾病的进展风险分层的分层化性结肠炎是一种

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