炎症性肠病 (IBD) 爆发的先进预测建模:使用远程监测平台的生活方式和心理社会因素进行数据驱动的方法
Y Okegunna1,2,3, M Sekar4, G M C Adriaans5
1Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands. yomi.okegunna@maastrichtuniversity.nl.
Digestive diseases and sciences
|March 12, 2026
概括
预测炎症性肠病 (IBD) 爆发是通过结合生活方式和心理社会数据来改进的. 像myIBDcoach这样的远程监测平台可以提高超出临床因素的爆发预测准确度.
科学领域:
- 胃肠道学和数字健康
背景情况:
- 经常出现的爆发会对炎症性肠病 (IBD) 的进展和患者的生活质量产生重大影响.
- 目前仅使用临床数据的模型提供了有限的爆发预测准确度,阻碍了临床应用.
- 远程监测平台,如myIBDcoach,收集广泛的现实世界数据,包括生活方式和心理社会因素,为改进爆发预测提供了潜力.
研究的目的:
- 开发和比较炎症性肠病 (IBD) 爆发的五种预测模型.
- 评估将与生活方式和心理社会因素相关的患者报告结果措施 (PROMs) 纳入爆发预测模型的有效性.
- 确定利用远程监测平台的综合数据的模型是否优于仅依赖基线临床特征的模型.
主要方法:
- 使用myIBDcoach远程医疗平台 (2022年11月 - 2024年6月) 的数据建立了一个由429名患者组成的前性观察队列.
- 使用逐步组-LASSO逻辑回归 (G-LASSO) 来估计爆发与临床变量和PROM (生活方式/心理社会因素) 之间的关联.
- 模型性能被严格评估,使用准确度,曲线下的面积 (AUC),灵敏度,特异性,正预测值 (PPV) 和负预测值 (NPV).
主要成果:
- 结合基线变量与myIBDcoach PROM (心理社会和生活方式) 结合在一起的G-LASSO模型显示出卓越的性能.
- 这种综合模型的精度 (71%) 和ROC-AUC (77%) 比仅使用基线数据的模型更高 (精度:63%,ROC-AUC:65%).
- 具体的绩效指标显示,随着PROM的纳入,显著改善:灵敏度 (59%对51%),特异性 (91%对81%),PPV (91%对81%) 和NPV (59%对52%).
结论:
- 与单独的临床特征相比,整合主观健康,可修改的生活方式和心理社会数据显著提高了炎症性肠病 (IBD) 爆发的预测.
- 该研究强调了生活方式选择 (饮食,运动,压力管理) 和心理社会因素在管理IBD中的关键作用,因为这些因素可以调节炎症反应.
- 开发的预测模型强调了考虑多因素影响的必要性,包括生活方式和心理压力因素,以有效管理IBD和预防爆发.
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