[可解释的基于机器学习的预测模型,用于评估CDAI0至1级克罗恩病患者的腹部手术风险和生物治疗疗效]
Kailing Xie1, Qi Sun2, Zhixian Jiang3
1Department of General Surgery, Second Xiangya Hospital, Central South University, Changsha 410011. 3212136489@qq.com.
这项研究开发了一种CoxBoost模型,用于预测低疾病活性克朗氏病 (CD) 患者的腹部手术风险. 该模型有助于个性化治疗决策,特别是在生物治疗方面.
科学领域:
- 胃肠病学和肝病学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 克朗氏病 (CD) 患者的疾病活性较低 (CDAI 0-1) 仍然面临着腹部手术的显著风险.
- 准确预测手术风险对于优化治疗策略和患者结果至关重要.
研究的目的:
- 开发和验证一种预测模型,用于估计CD患者的腹部手术风险,CD患者的CDAI为0-1.
- 确定手术风险的关键预测因素,并评估模型的临床实用性和性能.
主要方法:
- 615名CD患者 (2016-2022) 的回顾性招募.
- 开发和比较9个机器学习生存模型,包括CoxBoost,使用培训/验证分割 (5:5比).
- 模型评估使用C指数,时间依赖ROC曲线 (AUC),校准曲线,决策曲线分析 (DCA) 和SHAP进行解释性.
主要成果:
- 考克斯Boost模型显示出强大的预测性能 (C指数=0.746,验证AUCs在0.751-0.797之间).
- 确定了关键预测因素:C反应蛋白 (CRP),白蛋白 (ALB),纤维素 (Fg) 和蒙特利尔B行为分类.
- 该模型有效地将患者分为低,中等和高手术风险组,生物疗法显著降低了中高风险组的风险.
结论:
- 考克斯Boost模型准确地预测了CD患者的腹部手术风险,CDDAI为0-1.
- 该模型支持临床决策,特别是在高风险患者中启动生物疗法.
- 这有助于个性化治疗规划,以改善患者管理.
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