P184: Streamlining drug repurposing: Optimizing candidate prioritization and evaluating candidates in polycystic kidney disease and SETBP1-associated disorders

Results: Of the 645 patients, genetic testing revealed positive results in 226 (35%) patients, negative results in 177 (27.4%) patients, variants of uncertain significance (VUS) in 133 (20.6%) patients, heterozygotes of autosomal recessive conditions (carriers) in 98 (15.2%) patients, and APOL1 risk genotypes in 11 (1.7%) patients.Only patients with positive or negative results (n=403) were included in model building.Patients with positive family history (q=0.017),diagnosis of a glomerular disease (q<0.001), or biopsy findings of focal segmental glomerulosclerosis (FSGS) (q=0.017) or thin glomerular basement membrane (GBM) disease (q=0.025) were significantly more likely to receive positive genetic testing results after multiple testing correction.Conversely, patients with diagnosis of atypical hemolytic uremic syndrome (aHUS) or thrombotic microangiopathy (TMA) (q=0.047),congenital anomalies of the kidneys and urinary tracts (CAKUT) (q=0.049), or electrolytes disorders (q=0.022) were significantly more likely to receive negative genetic testing results after multiple testing correction.XGBoost predictive modeling performed with a ROC-AUC of 0.76.The variables eGFR, glomerular disease, age at index visit, family history, and urine protein to creatinine ratio (UPCR) were 5 most important variables in model performance as determined by mean Shapley Additive Explanations (SHAP) values.Conclusion: This is the first study to report a model capable of reliably predicting genetic testing outcomes for CKD patients.XGBoost is unique in its ability to incorporate missing values, which was important for integrating diverse clinical data.An ROC-AUC of 0.76 represents robust predictive accuracy, setting the stage for a future web-based tool to guide nephrologists in genetic testing decisions.Importantly, as this model was trained on CKD patients referred specifically for genetics evaluation, validating model performance in a broader CKD cohort will be an essential next step.