用患者病史提升糖尿病肾病预测准确率，最高提升4%。
Effect of Clinical History on Predictive Model Performance for Renal Complications of Diabetes
- 基于多类临床数据构建逻辑回归模型预测肾功能恶化
- 加入病史信息后模型性能最高提升4%，AUROC达0.98
- 适合临床风险分层与个性化治疗决策支持
糖尿病是慢性病，易引发糖尿病肾病，进而成为终末期肾病的主要原因。早期识别高危人群对正确治疗至关重要。本文基于全国多中心回顾性真实世界研究DARWIN-Renal的数据，构建一系列逻辑回归模型，利用人口统计、体格测量、实验室检查、病理及治疗相关变量，在不同预测时长下预测糖尿病患者跨过关键肾小球滤过率（eGFR）阈值的风险。通过分析患者既往就诊信息对模型性能的影响，并结合Boruta算法进行特征重要性分析，发现引入既往病史可使模型性能提升最高达4%，且各模型表现优异（最高AUROC为0.98）。结果证实既往临床信息对预测具有显著价值。
原文摘要 · Abstract (English)
Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy, which, in turn, is the leading cause of end-stage chronic kidney disease. The early identification of individuals at heightened risk of such complications or their exacerbation can be of paramount importance to set a correct course of treatment. In the present work, from the data collected in the DARWIN-Renal (DApagliflozin Real-World evIdeNce-Renal) study, a nationwide multicentre retrospective real-world study, we develop an array of logistic regression models to predict, over different prediction horizons, the crossing of clinically relevant glomerular filtration rate (eGFR) thresholds for patients with diabetes by means of variables associated with demographic, anthropometric, laboratory, pathology, and therapeutic data. In doing so, we investigate the impact of information coming from patient's past visits on the model's predictive performance, coupled with an analysis of feature importance through the Boruta algorithm. Our models yield very good performance (AUROC as high as 0.98). We also show that the introduction of information from patient's past visits leads to improved model performance of up to 4%. The usefulness of past information is further corroborated by a feature importance analysis.
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