后勤回归和机器学习算法用于预测老年患者手术前心血管不良事件的风险
Xiao Yan Li1, Guang You Duan2, Lin Li3
1Center for Health Quality National Research Institute for Family Planning Beijing China.
Aging medicine (Milton (N.S.W))
|November 3, 2025
概括
这项研究开发了准确的模型来预测经过非心脏手术的老年患者的外科手术期间心血管不良事件 (PACE). 这些模型识别高风险个体,帮助临床决策,并可能降低死亡率.
科学领域:
- 心脏病学 心脏病学
- 老年医学 老年医学
- 麻醉学 麻醉学
背景情况:
- 接受非心脏手术的老年患者面临手术前心血管不良事件 (PACE) 的风险增加,导致预后较差和死亡率较高.
- 早期识别有风险的个体对于有效的预防和管理战略至关重要.
研究的目的:
- 在接受非心脏手术的老年患者中开发和验证PACE的预测模型.
- 为了分层风险和改善临床决策,以获得更好的患者结果.
主要方法:
- 一项对8309名接受非心脏手术的老年患者的回顾性研究.
- 使用临床数据,生物标志物和风险因素开发后勤回归和机器学习模型.
- 使用ROC曲线,决策曲线,校准曲线,灵敏度,特异性和F1分数的模型评估.
主要成果:
- 一个后勤回归模型实现了0.895的AUC,确定了Pro-BNP,心脏功能和肌素作为关键预测因子 (PCC指标).
- 机器学习模型准确地预测了跨风险层的PACE (低风险精度0.86,高风险精度0.970) 具有高灵敏度 (0.736) 和特异性 (0.973).
- 这些模型在识别高风险患者方面表现出强大的预测性能和临床价值.
结论:
- 在接受非心脏手术的老年患者中,已建立的PACE风险预测模型表现出良好的预测准确性.
- 这些模型为及时的诊断和治疗调整提供了科学基础,有可能减少PACE相关的死亡率.
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