用常规体检数据预测老年人早期功能衰退，提前数月发现风险。
Predicting Early Functional Decline from Longitudinal Laboratory and Vital Sign Trajectories: A Large-Scale Study Using the All of Us Research Program
- 分析12项生物标志物的长期变化趋势，识别功能衰退前兆。
- 模型预测准确率达AUROC 0.797，显著优于传统静态指标。
- 仅需现有电子病历数据，可无负担集成至临床系统。
老年人功能衰退通常在跌倒或步态异常后才被察觉，已错过干预时机。本研究利用All of Us研究计划（N = 297,861；11.1%为病例），提取12项生物标志物在三年前窗期内的轨迹特征（斜率、波动性、变化量、均值），构建轻量级梯度提升模型。融合轨迹特征的LightGBM模型显著优于静态实验室指标（AUROC 0.797 vs. 0.755；DeLong p < 0.001；AUPRC 0.380 vs. 0.304）。年龄与性别匹配的1:1分析证实轨迹信号独立于人口学因素（AUROC 0.727 vs. 0.680）。前瞻性分析显示，模型在功能衰退发生前3-12个月仍具持续预测能力（AUROC 0.768–0.740）。因仅使用常规医疗中已有的检测数据，该方法支持无需额外负担的电子病历被动集成，实现对功能衰退前驱状态的早期预警。
原文摘要 · Abstract (English)
Functional decline in older adults is typically recognized only after falls or observable gait impairment, closing the window for prevention. We investigated whether temporal trajectories of routine biomarkers, already recorded but rarely analyzed longitudinally, can identify patients in the pre-clinical phase of mobility decline. Using the All of Us Research Program (N = 297,861; 11.1% cases), we derived trajectory features (slope, variability, delta, mean) for twelve biomarkers over a three-year pre-index window. LightGBM models incorporating trajectories significantly outperformed static laboratory summaries (AUROC 0.797 vs. 0.755; DeLong p < 0.001; AUPRC 0.380 vs. 0.304). A 1:1 age- and sex-matched analysis confirmed an independent trajectory signal (AUROC 0.727 vs. demographics-only 0.680). A horizon analysis demonstrated sustained prediction 3-12 months before decline onset (AUROC 0.768-0.740). Because the model uses only measurements already ordered in routine care, it supports passive, zero-burden EHR integration for early detection of pre-clinical functional decline.
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