#1584 Development and validation of risk prediction models according to different clinical settings of IgA nephropathy: nationwide multicenter cohort study

Abstract Background and Aims IgA nephropathy (IgAN) is the most common glomerulonephritis with a high risk of progression to end-stage renal disease (ESRD). Because machine learning models developed for research have limited use in clinical settings, this study developed and validated prediction models for IgAN according to different clinical settings. Method In this study, we analyzed data from 1,174 patients in the Japanese Nationwide Retrospective Cohort Study in IgAN, focusing on a 10-year observation period. We defined the derivation data from January 2002 to April 2004, and temporal validation data from May 2004 to December 2004. The composite renal outcome was defined as a 1.5-fold increase in creatinine or progression to ESRD. We developed and evaluated the three prediction models according to the different clinical settings: the primary care model for general physicians in primary care settings, the tertiary care model for specialists in tertiary care hospitals, and the research institute model, a machine learning-based approach for academic research institute settings. Results After excluding 82 patients for missing data, 114 and 14 patients experienced the composite renal outcome in the derivation (n = 874) and validation (n = 218) cohorts, respectively. The primary care model identified three variables—eGFR <45 mL/min/1.73 m², proteinuria ≥0.5 g/day, and non-use of corticosteroids—as significant predictors (C-statistic: 0.796; 95% CI: 0.686-0.895). The tertiary care model composed of three variables including glomerular number, histological severity and tubular/interstitial severity showed a slightly higher C-statistic (0.807; 95% CI: 0.713-0.886). When stratifying patients into risk groups, the primary care model showed a higher incidence of composite renal events in high-risk patients, and the tertiary care model effectively discriminated outcomes in all groups. The research institute model, which included 38 variables including proteinuria level, hypertension, eGFR, and non-use of corticosteroids as key predictors, showed comparable performance (C-statistic: 0.808; 95% CI: 0.689-0.886). Conclusion The primary care and tertiary care models provide simple yet effective predictive tools, competitive with the machine learning-based research institution model. These models are critical for predicting renal outcomes in patients with IgA nephropathy and for patient management according to different clinical settings.