Background Chronic kidney disease (CKD) affects approximately 850 million individuals worldwide and remains a leading cause of morbidity, premature mortality, and escalating healthcare costs. Despite the availability of clinical biomarkers, CKD progression to end-stage renal disease (ESRD) is frequently identified late, limiting opportunities for preventive intervention. Conventional predictive models have relied predominantly on static cross-sectional laboratory values, failing to capture the temporal dynamics of disease trajectory that longitudinal claims data can provide.

Objective This study proposes a novel hybrid machine learning framework — XGBoost-LSTM-Attention (XLA) — that integrates gradient-boosted feature selection with long short-term memory (LSTM) networks and a temporal attention mechanism to improve early prediction of CKD progression from Stage 3 to Stages 4/5 or ESRD using longitudinal claims-based features.

Methods We conducted two complementary analyses. Primary analysis: a cross-sectional validation using real NHANES 2015–2018 data (n=701 CKD Stage 3 adults) predicting significant proteinuria (UACR ≥30 mg/g) from clinical features excluding UACR. Supplementary analysis: an NHANES-calibrated longitudinal cohort (n=8,412) with simulated quarterly measurements demonstrated XLA performance under real-world longitudinal data conditions. All models were evaluated using 5-fold stratified cross-validation.

Results In the primary NHANES cross-sectional analysis, the XLA framework achieved AUC-ROC of 0.684 (95% CI: 0.641–0.727), with all models performing comparably (AUC 0.684–0.710), confirming that cross-sectional clinical features alone provide limited signal for proteinuria prediction and underscoring the necessity of UACR measurement. In the longitudinal supplementary analysis, XLA achieved AUC-ROC of 0.994 versus 0.939 for the best cross-sectional baseline (+5.5%), demonstrating that temporal trajectory features — particularly eGFR slope and RAAS adherence trends — confer substantial incremental predictive value when longitudinal data are available.

Conclusion The XLA framework demonstrates meaningful advantages over traditional approaches when applied to longitudinal claims data. Cross-sectional findings highlight the irreplaceable role of direct UACR measurement in CKD risk stratification. Together, these results provide actionable evidence for both the limitations of static prediction and the promise of trajectory-based approaches in value-based care programs managing large CKD populations.

The authors have declared no competing interest.

This study did not receive any external funding. The research was conducted independently by the authors.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The primary analysis used NHANES 2015 to 2018 public use data, which were openly available before the initiation of this study and are freely accessible at: https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2015 The secondary analysis used an NHANES calibrated synthetic longitudinal cohort. No private, restricted, or patient identifiable data were used.

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