Prognostication in Autosomal Dominant Polycystic Kidney Disease: Current Tools, Clinical Gaps, and Future Directions.
TL;DR
Future directions in ADPKD prognostication point toward multimodal frameworks that integrate AI-derived imaging features, genomic risk measures, clinical risk factors, and molecular biomarkers, which could help translate complex disease information into more consistent, clinically actionable guidance for treatment decisions and shared decision-making.
Abstract
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most prevalent hereditary kidney disorder. While the disease is well-defined, its clinical course is highly variable, making the prediction of individual patient outcomes a significant challenge for clinicians and a source of psychological distress for those affected. Current prognostic tools fall into two primary categories: imaging-based models and genetic scoring systems. However, these unimodal approaches have inherent limitations. Imaging-based metrics often lose prognostic resolution in advanced disease stages as fibrosis begins to outweigh cyst expansion. Meanwhile, genetic scoring often fails to account for intrafamilial variability and may underestimate risk in a significant percentage of rapid progressors. Upcoming tools seek to fill these gaps by exploring new dimensions of the disease, including genome-wide polygenic scores (GPS) to account for background genetic influences and imaging approaches that use artificial intelligence that can capture cyst architecture and parenchymal changes beyond conventional volumetric measures. Future directions in ADPKD prognostication point toward multimodal frameworks that integrate AI-derived imaging features, genomic risk measures, clinical risk factors, and molecular biomarkers. Integrated prognostic tools could help translate complex disease information into more consistent, clinically actionable guidance for treatment decisions and shared decision-making.