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Войти в исследования Revista de investigacion clinica; organo del Hospital de Enfermedades de la Nutricion · 2026
Development and validation of a predictive model for the lifetime risk of end-stage kidney disease in autosomal dominant polycystic kidney disease.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Aguilar-Lugo-Gerez Juan José, Rivero-García Pamela, Carrillo-Pérez Diego Luis, Correa-Rotter Ricardo
Аннотация
BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is the leading monogenic cause of end-stage kidney disease (ESKD). Because disease-modifying therapy is costly and can cause adverse effects, identifying patients most likely to benefit is essential. We developed and internally validated a model to predict ESKD by age 80 in class 1 ADPKD, hypothesizing that combining baseline eGFR with an exponential estimate of kidney growth would improve forecasting.
METHODS: We retrospectively analyzed 142 adults with class 1 ADPKD followed at a tertiary center in Mexico City (2012-2023). Total kidney volume (TKV) was measured by the ellipsoid method and eGFR by CKD-EPI 2021. Data were split 50/50 into derivation and validation sets. Future eGFR was modeled using baseline eGFR plus an exponential TKV growth surrogate; ESKD risk (eGFR <15ml/min/1.73m2) was derived from the standard normal cumulative distribution. Performance was compared with the Mayo Clinic Predictive Model (MCPM) and a Cox model.
RESULTS: In validation, the model achieved adjusted R2=0.858 (vs. 0.830 for MCPM). ESKD prediction showed excellent discrimination (C-index=0.94) and calibration, comparable to Cox (C-index=0.95).
CONCLUSIONS: This approach supports individualized lifetime ESKD risk prediction in class 1 ADPKD to guide targeted therapy and resource allocation.
METHODS: We retrospectively analyzed 142 adults with class 1 ADPKD followed at a tertiary center in Mexico City (2012-2023). Total kidney volume (TKV) was measured by the ellipsoid method and eGFR by CKD-EPI 2021. Data were split 50/50 into derivation and validation sets. Future eGFR was modeled using baseline eGFR plus an exponential TKV growth surrogate; ESKD risk (eGFR <15ml/min/1.73m2) was derived from the standard normal cumulative distribution. Performance was compared with the Mayo Clinic Predictive Model (MCPM) and a Cox model.
RESULTS: In validation, the model achieved adjusted R2=0.858 (vs. 0.830 for MCPM). ESKD prediction showed excellent discrimination (C-index=0.94) and calibration, comparable to Cox (C-index=0.95).
CONCLUSIONS: This approach supports individualized lifetime ESKD risk prediction in class 1 ADPKD to guide targeted therapy and resource allocation.