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基于机器学习的模型的开发和外部验证,用于预测2型糖尿病中心力衰竭风险

Yuqing Liu¹, Ping Wang², Min Wang³

¹Department of Endocrinology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, People's Republic of China.

Diabetes, metabolic syndrome and obesity : targets and therapy

|November 19, 2025

概括

一个新的机器学习名图准确地预测了2型糖尿病患者 (T2DM) 使用常规临床数据的心力衰竭风险. 这种可访问的工具有助于在缺乏先进诊断的初级保健机构中进行风险分层.

科学领域:

心脏病学心脏病学
内分泌学在内分泌学.
数据科学数据科学数据科学

背景情况:

2型糖尿病 (T2DM) 显著增加心力衰竭 (HF) 的风险,这是发病率和死亡率的主要原因.
生物标志物NT-proBNP是T2DM中可靠的高频预测器,但其测量通常无法在中国的初级保健中得到.
可访问的高频风险分层工具对于T2DM患者至关重要,特别是在资源有限的环境中.

研究的目的:

开发和外部验证一种基于机器学习的名谱,用于预测T2DM患者的NT-proBNP升高 (≥125 pg/mL).
为初级卫生保健机构提供HF风险评估的实用和可解释的工具.
确定T2DM中NT-proBNP升高的关键临床预测因素.

主要方法:

追溯招募564名T2DM患者进行模型开发和302人进行外部验证.
使用最小绝对收缩和选择操作员 (LASSO) 回归的特征选择.
构建和评估五个具有十倍交叉验证的机器学习模型;最佳模型以名ogram形式呈现.

主要成果:

确定了六个预测因素:估计的淋巴膜过率,年龄,血清白蛋白,血红蛋白,尿液白蛋白与肌素的比率,年龄≥65岁.
机器学习名录实现了0.806 (培训) 和0.861 (外部验证) 的AUC,证明了良好的校准和临床效用.
估计的膜过率被确定为通过夏普利添加式扩展 (SHAP) 最有影响力的预测因素.

结论:

一个可解释的,基于机器学习的名图有效地预测了T2DM患者使用常规临床变量增加的NT-proBNP.
开发的名图表显示了强大的性能和通用性,为高温风险分层提供了一个实用的解决方案.
这种工具对中国资源有限的初级保健机构特别有价值.

关键词:

这是一种N-终端的B型性性.心脏衰竭是因为心脏衰竭.机器学习是机器学习.预测模型预测模型 2型糖尿病是什么?

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