Prediction of incident hypertension risk in women with currently normal blood pressure.

BACKGROUND: We examined whether a hypertension risk prediction model based on clinical characteristics and blood biomarkers might improve on risk prediction based on current blood pressure alone.
METHODS: A prospective cohort of 14,822 normotensive women aged 45 years and older were followed over 8 years beginning in 1992 for the development of hypertension. Among a randomly selected two-thirds sample (N=9427), hypertension prediction models were developed using 52 potential predictors and compared with a model based on blood pressure alone. Each prediction model was validated in the remaining one third (N=5395).
RESULTS: In the development cohort, the best prediction model for incident hypertension included age, blood pressure, ethnicity, body mass index, total grain intake, apolipoprotein B, lipoprotein(a), and C-reactive protein (Bayes Information Criteria [BIC]=8788). Although this model was superior to a model based on blood pressure alone (BIC=8957), it was only marginally better than a simplified model including age, blood pressure, ethnicity, and body mass index (BIC=8820). In the validation cohort, the simplified model demonstrated adequate calibration, a c-index similar to that of the best model (0.703 vs 0.705), and when compared with the model based on blood pressure alone, reclassified 1499 participants to hypertension risk categories that proved to be closer to observed risk in all but one instance.
CONCLUSION: In this prospective cohort of initially normotensive women, a model based on readily available clinical information predicted incident hypertension better than a model based on blood pressure alone.