Dynamic prediction of end stage kidney disease for patients with IgA nephropathy
Objective:To develop a personalized dynamically model to predict the risk of kidney failure for patients with IgA nephropathy(IgAN)using updates of longitudinal data at each follow-up visit.Methodology:Three joint models were fitted to analyze the longitudinal data at each visit.We defined the baseline as the time of the kidney biopsy and fitted a clinical joint Model A,which included variables such as sex,age,eGFR,ALB,and proteinuria.We also constructed a clinical-pathological Model B,which incorporated both clinical and histological MEST features.Model C was fitted using parameters identical to those in Model A,however,the baseline was defined as the time of the clinical visit instead of the biopsy.Results:A total of 866 patients were included(650 in the development cohort and 216 in the validation cohort)and contributed 10 565 patient-years of data,and 22 533 eGFR and proteinuria measurements.Models A and B performed similarly with high predictive ability.However,the inclusion or exclusion of pathological variables did not significantly increase or decrease the accuracy of the joint models for predicting kidney failure risk.As follow-up time increased,model A's predictive performance continued to improve,reaching optimal performance around 5 years after kidney biopsy.The AUC value increased from 0.864 at kidney biopsy to 0.956 at 5 years after biopsy,and the Brier score decreased from 0.124 at the time of biopsy to 0.058 at 5 years after biopsy.The model C achieved similar results.All predictive performances were confirmed in the validation cohort.To facilitate clinical practice,we utilized the Shiny package to implement dynamic prediction.The R objects and source code have been made publicly available online.Conclusion:The Joint model can be utilized for the longitudinal data generated from visits of IgAN patients,providing a high-performance dynamic prediction of kidney failure for IgAN patients.This helps achieve the objective of individualized dynamic prediction.
IgA nephropathyjoint modeldynamic predictionend stage kidney disease
NETL
NSTL
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