Abstract 16761: Refining Individualized Risk Prediction for Apparent Treatment-Resistant Hypertension in a Large Multiethnic Biobank: The Million Veteran Program

Introduction: Approximately 10-20% of treated hypertensive patients have apparent treatment-resistant hypertension (ATRH), which is associated with adverse cardiovascular outcomes. Our goal was to develop a prediction model for ATRH that combines clinical data and a genetic risk score (GRS) for ATRH. Methods: Study participants were 232,925 patients with hypertension (HTN) enrolled in the Million Veteran Program receiving care at the VA from 2004 to 2017. 21% were black and 6% were women. Incident ATRH was defined as failure to achieve outpatient blood pressure (BP) <140/90 mmHg with 3 anti-HTN drugs including a thiazide or the use of ≥4 drugs. In the training data (n = 207,375), a logistic regression model was fit with age, sex, race, smoking history, body mass index, diabetes, anti-HTN drug classes, baseline systolic BP (SBP), diastolic BP, eGFR, serum potassium, serum bicarbonate and serum lipids. A weighted GRS was developed in the training data using 21 significant SNPs from a transethnic ATRH GWAS. An independent test data (n = 25,550) was used to validate the clinical model and fit a GRS-enriched (clinical model + GRS) model. Results: Over a 13.5-year follow-up, 20,536 and 2,247 incident ATRH cases were observed in the training and test data. In the training data, the clinical model had reasonable discriminant ability (C=0.706) and good calibration (Brier score = 0.085). Age, diabetes, and SBP were the strongest predictors of ATRH. In the test dataset, a 1-IQR increase in the GRS was associated with a 10% (95%CI: 1.04-1.17) higher odds of ATRH. In the test dataset, the LR χ 2 , C-statistic and Brier score for the clinical model were 1075, 0.705 and 0.077 respectively. In the GRS-enriched model, these values were 1107, 0.708 and 0.077. The net reclassification improvement was 1.5% (95% CI: 0.3-2.8, p=0.02). The GRS-enriched model had excellent calibration with an optimism-corrected calibration slope of 0.95. Conclusion: Augmenting a clinical prediction model for ATRH with a GRS comprising GWAS-significant SNPs improved model fit but not discriminant ability. Future work will evaluate whether models incorporating polygenic risk scores with a larger number of variants may refine ATRH risk prediction and stratification and evaluate race-specific performance.