发表机构
Indiana University（印第安纳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究基于All of Us计划的大规模数据，用LightGBM模型结合12种生物标志物的纵向轨迹，实现了对老年人临床前功能衰退的早期预测，性能优于静态指标，可零负担整合至电子健康记录。
AI 中文摘要
老年人的功能衰退通常在跌倒或出现明显步态障碍后才被发现，此时已错过预防窗口。本研究探究常规生物标志物的时间轨迹（已记录但极少进行纵向分析）是否能识别处于行动能力衰退临床前阶段的患者。研究使用All of Us研究计划的数据（N=297861，病例占比11.1%），在索引前三年窗口内提取12种生物标志物的轨迹特征（斜率、变异性、变化量、均值）。纳入轨迹特征的LightGBM模型显著优于静态实验室汇总指标（AUROC为0.797 vs 0.755，DeLong检验p<0.001；AUPRC为0.380 vs 0.304）。1:1年龄与性别匹配分析确认轨迹具有独立预测价值（AUROC为0.727 vs 仅人口统计学特征的0.680）。时间范围分析显示，模型可在衰退发作前3至12个月持续进行预测（AUROC为0.768至0.740）。由于该模型仅使用常规诊疗中已开具的测量数据，支持零负担被动整合至电子健康记录（EHR），以早期检测临床前功能衰退。
英文摘要
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.
CommentsAccepted for presentation at American Medical Informatics Association (AMIA) Annual Symposium 2026
