AI 中文总结
研究针对慢性肾病肾衰竭风险评估问题，引入可解释的纵向扩展方法CLARK，利用540万个体数据创建大型队列，经实验表明该方法在区分能力上优于静态模型，能更好识别高风险患者，增强肾衰竭风险评估。
AI 中文摘要
准确的肾衰竭风险评估对慢性肾病（CKD）的及时干预至关重要。现有方程（如肾衰竭风险方程；KFRE）依赖单一实验室测量来估计短期和长期肾衰竭风险，未利用纵向实验室模式。本文引入Clalit肾衰竭风险纵向评估（CLARK），这是一种可解释的纵向扩展的最新值方法，纳入常规收集的重复实验室测量。我们使用540万个体的数据开发CLARK，识别出270,009例CKD患者，创建了迄今为止最大的纵向CKD队列之一，有12,087例肾脏替代治疗起始事件，中位随访10.4年。在各种实验室配置和预测期内，CLARK在区分能力上优于静态模型（如在仅eGFR设置中，2年平均精度0.541对0.516）。在干预阈值下，基于轨迹的模型改善了高风险患者的识别，特别是对于长期预测，表明可解释的纵向实验室特征可能通过更好地识别最可能从及时干预中受益的患者来增强肾衰竭风险评估。
英文摘要
Accurate kidney failure risk assessment is critical to timely intervention in chronic kidney disease (CKD). Existing equations (e.g. Kidney Failure Risk Equation; KFRE) rely on single laboratory measurements to estimate short- and long-term kidney failure risk, leaving longitudinal laboratory patterns unused. Here we introduce Clalit Longitudinal Assessment of Risk of Kidney Failure (CLARK), an interpretable longitudinal extension of latest-value methods which incorporates routinely collected repeat laboratory measures. We develop CLARK using data from 5.4 million individuals, identifying 270,009 patients with CKD to create one of the largest longitudinal CKD cohorts to date, with 12,087 kidney replacement therapy initiation events and a median follow-up of 10.4 years. Across laboratory configurations and prediction horizons, CLARK demonstrated improved discrimination over static models (e.g., 2-year average precision 0.541 vs 0.516 in the eGFR-only setting). At intervention thresholds, trajectory-based models improved identification of high-risk patients, especially for longer-term prediction, suggesting that interpretable longitudinal laboratory features may enhance kidney failure risk assessment through improved identification of patients most likely to benefit from timely intervention.
