开发和评估一种机器学习模型,用于手术后急性损伤的活跃感染性内心炎
XinPei Liu1, SanXi Ai2, RuiMing Yu1
1Department of Cardiac Surgery, Peking Union Medical College Hospital, Beijing, China.
Frontiers in cardiovascular medicine
|December 23, 2024
概括
这项研究开发了一种机器学习模型,用于预测感染性内心炎心脏手术后的急性损伤 (AKI). 在RBF-SVM模型中,AUC达到0.771,确定了早期干预的关键风险因素.
科学领域:
- 心脏病学 心脏病学
- 腎臟病學 (nephrology) 是一種醫學.
- 人工智能在医学中的应用
背景情况:
- 急性损伤 (AKI) 是心脏手术后的严重并发症,特别是在患有活跃感染性内心炎的患者中.
- 由于感染性内心炎而接受手术的患者代表了一个具有复杂病因的高风险群体,患有手术后的AKI.
研究的目的:
- 开发和验证一种机器学习模型,用于预测因活跃传染性内心炎而接受心脏手术的患者的AKI.
- 通过专注于内心炎相关手术的具体挑战,改进现有的AKI预测模型.
主要方法:
- 分析了527名因活跃传染性内心炎接受心脏手术的患者的病历.
- 使用LASSO回归进行特征选择,然后开发多种机器学习模型 (逻辑回归,SVM,XGBoost,决策树,随机森林).
- 模型性能评估使用ROC曲线AUC,区分,校准和SHAP值进行解释性.
主要成果:
- 手术后的AKI发生在49.53%的患者中.
- 射线基函数支向量机 (RBF-SVM) 模型表现出最高的预测性能,AUC为0.771.1.
- 确定的关键预测因素包括门置换,手术前高血压,大量植被,NYHA类,酒,年龄,低心输出综合征,TyG指数,手术前肌素清除,心肺绕道持续时间,手术内红细胞输血,手术内尿输出,手术前血红蛋白,以及手术时间.
结论:
- 机器学习模型可以有效地预测患有活跃感染性内心炎的患者手术后的AKI.
- 早期预测有助于在这种独特的患者群体中进行有针对性的术后优化和风险分层.
- 与一般心脏手术预测工具相比,开发的模型提供了改进的风险评估.
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