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预测克罗恩病肠切除的短期重大术后并发症:基于机器学习的研究
Fang-Tao Wang1, Yin Lin1, Xiao-Qi Yuan1
1Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China.
World journal of gastrointestinal surgery
|April 5, 2024
概括
机器学习模型可以预测克罗恩病 (CD) 肠切除后的重大术后并发症. 随机森林模型在识别高风险患者方面表现出卓越的准确性,以便更好地进行术后管理.
科学领域:
- 胃肠病学 胃肠病学
- 手术瘤学手术瘤学
- 医疗信息学 医疗信息学
背景情况:
- 经过肠切除 (IR) 的克罗恩氏病 (CD) 患者面临高风险的术后并发症.
- 预测这些并发症对于手术决策和患者的治疗结果至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测IR后CD患者的短期重大术后并发症.
- 确定这些并发症的关键预测因素.
主要方法:
- 对259名接受IR (2017-2022) 的CD患者进行了回顾性分析.
- 使用培训队伍开发后勤回归和随机森林 (RF) 模型.
- 在单独的队列中使用AUC验证模型性能.
主要成果:
- 5.0%的患者在30天内出现了严重的术后并发症 (Clavien-Dindo ≥ III).
- 与物流模型 (0.916) 相比,射频模型实现了更高的AUC (0.965).
- 关键预测因素包括手术前CD活性指数 (CDAI) ≥220,低血清白蛋白和延长的手术时间.
结论:
- 逻辑回归 (通过名图) 和射频模型都有效地预测了CD中IR后的短期重大并发症.
- 射频模型表现出卓越的预测性能.
- 手术前CDAI,血清白蛋白和手术时间是风险分层和个性化外科护理的关键因素.
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