Development and validation of a population-based prediction model for prevalent hypertension: evidence from Qatar biobank

Background Hypertension is a major cause of cardiovascular morbidity and mortality globally. Despite its high burden, validated population-based models and scores for prevalent hypertension remain limited. This study aimed to develop and internally validate a diagnostic model and scores to predict prevalent hypertension among adults in Qatar. Methods This population-based study included 6,927 adults aged ≥18 years with complete baseline data for hypertension status from the Qatar Biobank (QBB). A multivariable logistic regression model was developed using a randomly selected training cohort (70%; n = 4,835) and internally validated in an independent testing cohort (30%; n = 2,092). Model performance was evaluated for discrimination using the area under the receiver operating characteristic curve (AUC), calibration using calibration plots and the Hosmer–Lemeshow goodness-of-fit test, and stability via bootstrap resampling (1,000 replications). Regression coefficients were converted into simplified point-based scores to facilitate practical application. Results Overall, 1,097 (15.8%) participants had prevalent hypertension. The final model included predictors such as age, gender, nationality, body mass index, smoking, physical activity, sleep duration, diabetes, hyperlipidemia, and cardiovascular diseases. The model demonstrated good discrimination, with AUCs of 0.803 (95% CI: 0.78–0.83) and 0.797 (95% CI: 0.76–0.84) in the training and testing cohorts, respectively. Calibration was satisfactory, with close agreement between predicted and observed outcomes and non-significant Hosmer–Lemeshow tests in both the training ( p = 0.102) and testing ( p = 0.393) cohorts. Bootstrap bias across all predictors was negligible, further supporting the model's stability. A simplified 100-point predictive score was derived from the final model to support practical classification of hypertension. Conclusion We developed and internally validated a population-based prediction model and simplified predictive score for prevalent hypertension in Qatar. The model demonstrated good discrimination, calibration, and stability. The model may support population-level hypertension stratification, targeted screening, and complication-preventive interventions.