Bayesian Joint Modelling of Longitudinal Creatinine Trajectories in Children with Auto-Immune Disorders to Predict Paediatric Kidney Disease Risk in a Single Centre Study

TL;DR

This study develops a joint modelling framework to analyze longitudinal creatinine data and predict kidney disease risk in children with autoimmune disorders, aiding personalized clinical decisions.

Contribution

The paper introduces a novel joint modelling approach that combines creatinine trajectories with time-to-event data for improved risk prediction in paediatric nephrology.

Findings

Strong association between creatinine profiles and adverse kidney events.

Treatment with corticosteroids and calcium channel blockers increases risk.

Immunosuppressive therapy reduces risk.

Abstract

This study investigates the relationship between longitudinal serum creatinine measurements and the risk of adverse kidney outcomes in paediatric patients with auto-immune disorders at Great Ormond Street Hospital for Children NHS Foundation Trust, London. To jointly analyse repeated biomarker measurements and time-to-event outcomes, we employed a joint modelling framework that combines the creatinine trajectories with the time to death or diagnosis of acute kidney injury or chronic kidney disease. Covariates considered in analysis included demographic and clinical characteristics. The results demonstrate a strong association between evolving creatinine profiles and the risk of the composite event. Specifically, treatment with corticosteroids and calcium channel blockers was associated with an increased event risk, whereas immunosuppressive therapy was associated with a reduced risk. The longitudinal component showed that creatinine trajectories were significantly influenced by age and BMI z-score. To demonstrate the practical utility of the proposed framework, dynamic risk predictions were generated using patients' observed creatinine trajectories. Model performance was compared using model selection criteria, alongside area under the curve and Brier score to evaluate the accuracy of dynamic risk predictions. These predictions illustrate the potential of joint models to support personalised medicine and clinical decision making in paediatric nephrology through real-time risk assessment.