冠状动脉疾病与糖尿病结合的可解释机器学习模型:开发和验证研究研究
Xujie Wang1, Shipeng Wang2, Xuhui Liu3
1Department of Emergency ICU, The Affiliated Hospital of Qinghai University, Xining, Qinghai, China.
Frontiers in cardiovascular medicine
|January 21, 2026
概括
机器学习模型准确预测冠状动脉疾病和糖尿病患者的死亡率. 这些模型利用临床参数进行个性化风险分层,改善患者护理.
科学领域:
- 心脏病学 心脏病学
- 糖尿病学 糖尿病学
- 机器学习在医学中的应用
背景情况:
- 冠状动脉疾病 (CAD) 和糖尿病有着强烈的双向关系,增加心血管风险和恶化预后.
- 对于患有并发性CAD和糖尿病的患者,准确的预后对于有效的治疗决策至关重要.
- 使用可访问的临床参数预测不良结果对于这个患者群体具有显著的临床价值.
研究的目的:
- 开发和验证基于机器学习的预测模型,用于评估伴随性CAD和糖尿病患者的预后.
- 确定该人群中不良结果的独立预测因素.
- 为个性化风险分层和决策创建一个临床工具.
主要方法:
- 一项对389名患有并发性CAD和糖尿病的患者进行的回顾性队列研究.
- 使用LASSO回归的特征选择,其次是向后循序渐进的Cox回归.
- 为临床应用开发一个包含独立预测因子的名录.
- 模型性能评估使用歧视指标,校准图和决策曲线分析.
主要成果:
- 确定了八个独立预测因素:血红蛋白,INR,白蛋白,NT-proBNP,年龄,纤维素原,利尿剂使用和数字化治疗.
- 综合模型在培训和验证队列中都显示出强大的歧视性性能,用于死亡率预测 (AUC从0.798到0.846).
- 开发了一个名图,显示可靠的性能和临床实用性,用于个性化风险分层.
结论:
- 一种多式预后模型有效地预测了CAD和糖尿病并发症患者的全因死亡率.
- 开发的诺米图为个性化风险估计提供了潜在的实用性,有助于临床决策和患者分层.
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