BACKGROUND: This study aimed to validate the Korean Genome Epidemiology Study (KoGES) risk score to predict the 4-year risk of hypertension (HT) in a large nationwide sample, and compare its discrimination and calibration with the Framingham and blood pressure (BP)-only models. METHODS AND RESULTS: This study analyzed 69,918 subjects without HT at baseline from the National Sample Cohort in the National Health Insurance Service database. We compared the Framingham, KoGES, and BP-only models for discrimination using area under the receiver-operating characteristic curves (AROC), calibration using goodness-of-fit tests, and reclassification ability using the continuous net reclassification improvement (NRI) and integrated discrimination improvement. Of 69,918 subjects, 18.6% developed HT during the follow-up. AROC was significantly higher for the KoGES (0.733) than for the Framingham (0.729) or BP-only (0.707) model. Recalibrated Framingham model underestimated HT incidence in all deciles (P<0.001). BP-only model overestimated risk in the lower deciles (P<0.001). KoGES model accurately predicted risk in all except the highest decile (χ(2)=14.85, P=0.062). The KoGES model led to a significant improvement in risk reclassification compared with the Framingham and BP-only models (NRI, 0.354; 95% confidence interval [CI], 0.343-0.365 and 0.542; 95% CI, 0.523-0.561, respectively). CONCLUSIONS: In this validation study, the KoGES model demonstrated better discrimination, calibration, and reclassification ability than either the Framingham or BP-only model. The KoGES model may help identify Korean individuals at high risk for HT. (Circ J 2016; 80: 1578-1582).
BACKGROUND: This study aimed to validate the Korean Genome Epidemiology Study (KoGES) risk score to predict the 4-year risk of hypertension (HT) in a large nationwide sample, and compare its discrimination and calibration with the Framingham and blood pressure (BP)-only models. METHODS AND RESULTS: This study analyzed 69,918 subjects without HT at baseline from the National Sample Cohort in the National Health Insurance Service database. We compared the Framingham, KoGES, and BP-only models for discrimination using area under the receiver-operating characteristic curves (AROC), calibration using goodness-of-fit tests, and reclassification ability using the continuous net reclassification improvement (NRI) and integrated discrimination improvement. Of 69,918 subjects, 18.6% developed HT during the follow-up. AROC was significantly higher for the KoGES (0.733) than for the Framingham (0.729) or BP-only (0.707) model. Recalibrated Framingham model underestimated HT incidence in all deciles (P<0.001). BP-only model overestimated risk in the lower deciles (P<0.001). KoGES model accurately predicted risk in all except the highest decile (χ(2)=14.85, P=0.062). The KoGES model led to a significant improvement in risk reclassification compared with the Framingham and BP-only models (NRI, 0.354; 95% confidence interval [CI], 0.343-0.365 and 0.542; 95% CI, 0.523-0.561, respectively). CONCLUSIONS: In this validation study, the KoGES model demonstrated better discrimination, calibration, and reclassification ability than either the Framingham or BP-only model. The KoGES model may help identify Korean individuals at high risk for HT. (Circ J 2016; 80: 1578-1582).