BAB指数的预测值和整合BAB指数的机器学习模型,用于预测急性ST段升高死亡率
Haonan Xu1, Tianshu Gu1, Shuo Zhang1
1Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease Department of Cardiology, Tianjin Institute of Cardiology, The Second Hospital of Tianjin Medical University, Tianjin, China.
一个新的BAB指数,使用常规血清生物标志物,有效预测ST段升高心肌梗塞 (STEMI) 患者的短期和长期死亡率. 该工具还增强了机器学习模型,以改善STEMI结果预测.
科学领域:
- 心脏病学 心脏病学
- 生物标志物 生物标志物
- 预测 预测 预测 预测 预测
背景情况:
- ST段升高心肌梗塞 (STEMI) 的死亡率很高,受器官功能障碍,并发症和营养状况的影响.
- 常规的血清生物标志物有可能预测STEMI结果.
研究的目的:
- 开发和验证使用血清生物标志物用于STEMI死亡率预测的简单预后工具 (BAB指数).
- 评估BAB指数在用于STEMI预后的机器学习 (ML) 模型中的作用.
主要方法:
- 来自天津冠状动脉病 (CAD) 数据库 (2010-2021) 的多中心观察数据的分析.
- 对BAB指数的计算:Log10 ((NT-proBNP × ALT × BUN) 的计算方法.
- 使用考克斯回归,受限立方脊柱分析,卡普兰-梅尔生存率和ML模型 (XGBoost) 的验证.
主要成果:
- 在8,002名STEMI患者中,BAB指数显示出1个月 (AUC=0.804) 和1年死亡率 (AUC=0.794) 的强烈歧视力.
- 较高的BAB指数值独立地与死亡率增加有关 (P < 0.001).
- 结合BAB指数的XGBoost模型显示出高性能 (AUC=0.873为1个月,AUC=0.871为1年死亡率),BAB指数作为顶级预测特征.
结论:
- BAB指数是一种简单而有效的工具,用于预测STEMI患者的短期和长期死亡率.
- 在用于STEMI结果预测的可解释机器学习模型中,BAB指数发挥着重要作用.
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