Objectives
Monitoring kidney function after acute kidney injury (AKI) hospitalisation is essential for identifying patients at risk of rapid progression. This study developed a machine learning (ML) model to predict membership in a high-risk longitudinal estimated glomerular filtration rate (eGFR) trajectory associated with subsequent kidney replacement therapy (KRT) initiation.
Methods
We conducted a cohort study of 88 632 adults with ≥2 post-discharge eGFR measurements between 2010 and 2017. Joint latent class mixed models were used to identify dynamic eGFR trajectories from 665 499 measurements. Patient-level data splitting (80/20) was strictly enforced. Extreme gradient boosting (XGBoost) and random forest models were trained using clinical features within 3 months after discharge to predict membership in the highest-risk eGFR trajectory class.
Results
Three eGFR trajectories were identified: Steady Low (high-risk phenotype), Slow Decline and Recovery. The Steady Low group showed the strongest association with KRT. The 10-variable XGBoost model demonstrated excellent discrimination (AUROC 0.974, sensitivity 0.913, specificity 0.919). The eGFR slope from discharge to 3 months was the most influential predictor of high-risk progression.
Discussion
Although the study was limited to a single healthcare system, this ML framework identified patients at risk of rapid kidney function decline using routinely available clinical data. The model supports early risk stratification after AKI and may enable more timely kidney-protective interventions. External validation, calibration assessment and benchmarking against simpler eGFR-based rules remain important next steps.
Conclusion
A 10-variable XGBoost model accurately identifies patients with deteriorative eGFR trajectories (Steady Low) early after discharge, providing a practical framework for personalised post-AKI kidney care.
