使用机器学习方法的长期死亡率预测器在外周血管干预后患有慢性四肢威胁性缺血的患者中
Santiago Callegari1, Gaëlle Romain1, Jacob Cleman1
1Vascular Medicine Outcomes Program Yale University New Haven CT.
Journal of the American Heart Association
|May 18, 2024
概括
慢性肢体威胁性缺血症 (CLTI) 患者的死亡率很高. 一个机器学习模型确定了病和心力衰竭等关键预测因素,使得更好的风险评估和个性化护理计划能够改善CLTI患者的生存率.
科学领域:
- 血管外科 血管外科
- 生物统计学 生物统计学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 慢性四肢威胁性缺血症 (CLTI) 与显著的长期死亡率有关.
- 确定新的死亡率预测因子对于改善患者生存率和定制医疗保健计划至关重要.
- 机器学习提供了一种强大的方法来分析复杂的数据集,以预测风险.
研究的目的:
- 利用随机生存森林算法来确定CLTI患者长期全因死亡率的预测因素.
- 开发一种风险分层模型,用于为CLTI进行外周血管干预的患者.
主要方法:
- 分析了2017-2018年接受了外周血管干预的10,114名CLTI患者的队列.
- 使用66个程序前变量构建了一个随机生存森林模型,以预测3年全因死亡率.
- 模型性能使用连续排列概率得分,哈雷尔C指数和袋外错误率进行评估.
主要成果:
- 三年死亡率为39.1%,平均存活时间为1.4年.
- 死亡率的关键预测因素包括慢性病,年龄,充血性心力衰竭,痴呆症,心律失常,需要辅助护理,生活状况和体重指数.
- 随机生存森林模型在预测死亡率方面表现出卓越的准确性.
结论:
- 开发的随机生存森林模型准确地预测了CLTI患者的长期死亡率.
- 死亡风险受到人口,功能,行为和医疗因素的组合的影响.
- 将多维风险因素纳入医疗保健计划可以增强个性化护理,并可能改善CLTI患者的生存率.
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