评估医疗保险患者的短期死亡率,这些患者因慢性肢体威胁性缺血而接受内血管干预
Jacob Cleman1, Gaëlle Romain1, Santiago Callegari1
1Vascular Medicine Outcomes Program, Section of Cardiovascular Medicine, Yale University, New Haven, CT, USA.
Vascular medicine (London, England)
|February 9, 2024
概括
机器学习模型确定了经过外周血管干预 (PVI) 的慢性四肢威胁性缺血症 (CLTI) 患者30天和90天死亡的关键风险因素. 五期慢性病,痴呆和心力衰竭是最关键的预测因素之一.
科学领域:
- 血管外科 血管外科
- 医疗信息学 医疗信息学
- 预测分析 (Predictive Analytics) 是一种分析方法.
背景情况:
- 患有慢性肢体威胁性缺血症 (CLTI) 的患者在重血管化后面临高死亡率.
- 在外周血管干预 (PVI) 后,对短期结果进行准确的风险分层仍然是一个挑战.
研究的目的:
- 开发和评估机器学习模型,对接受PVI的CLTI患者30天和90天全因死亡率的预测变量进行排名.
- 确定影响短期死亡率的关键程序前因素.
主要方法:
- 利用随机生存森林 (RSF) 模型对来自医疗保险相关的血管质量倡议的10,114名患者的数据集.
- 包括66个程序前变量,并对其在预测死亡率方面的重要性进行排名.
- 评估模型性能使用布里尔分数,连续排列概率分数,出袋错误率和哈雷尔的C指数.
主要成果:
- 该研究确定了5期慢性病 (CKD),痴呆症,充血性心力衰竭 (CHF),年龄,紧急手术和需要辅助护理作为30天和90天死亡率最具预测性的变量.
- 在前10个预测因素中,有8个是医疗并发症或功能状态变量.
- RSF模型显示出良好的区分 (C-统计 0.72-0.73) 和校准 (Brier 分数 0.03-0.10).
结论:
- 机器学习模型有效地确定了接受PVI的CLTI患者死亡的关键风险因素.
- 关键预测因素包括CKD,痴呆症,心血管疾病,需要辅助护理,紧急手术和年龄.
- 这些发现可以为CLTI患者PVI的个性化风险-益处讨论提供信息.
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