身体表现在现实老年糖尿病患者群体中强烈预测所有原因的死亡风险:用于死亡风险分层的机器学习方法
Alberto Montesanto1, Vincenzo Lagani2,3,4, Liana Spazzafumo5
1Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, Italy.
Frontiers in endocrinology
|May 15, 2024
概括
使用短物理性能电池 (SPPB) 的身体性能评估对于2型糖尿病 (T2D) 的老年人死亡风险分层至关重要. 较低的SPPB分数显著增加死亡风险,突出其在临床实践中的重要性.
科学领域:
- 老年病的医生 老年病的医生
- 糖尿病学 糖尿病学
- 生物统计学 生物统计学
背景情况:
- 预后风险分层对于患有2型糖尿病 (T2D) 的老年人至关重要.
- 这有助于做出明智的决策,提前为这个人口群体计划护理.
研究的目的:
- 确定老年T2D患者死亡风险的预测模型.
- 评估多维老年医疗评估,特别是身体表现在风险分层中的作用.
主要方法:
- 一项追溯的纵向研究,包括1001名70岁以上的T2D患者.
- 使用短物理性能电池 (SPPB) 进行物理性能评估.
- 使用自动机器学习 (AutoML) 通过JADBio平台进行模型开发.
主要成果:
- 最终的AutoML模型包括年龄,性别,SPPB和并发症 (CKD,心肌缺血症,PAD,神经病变,MI).
- SPPB是死亡率最重要的预测因素 (c指数:0.726).
- 低SPPB得分 (<5) 与高得分 (>8) (P<0.001) 相比,与3.35倍的死亡风险相关.
结论:
- 身体性能评估,特别是SPPB,应纳入常规的临床实践.
- 这种整合对于T2D老年患者的准确风险分层至关重要.
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