Predicting the risk of incident hypertension and increase in blood pressure: A systematic review of existing prediction models for adults

Abstract

Background and aims: Models that can predict the risk of developing essential hypertension or increase in blood pressure (BP) can be used to identify high risk individuals. We aimed to summarize and assess prediction models developed in the general adult population using longitudinal data, as well as any external validation of such models. Methods: For this systematic review, we searched the literature on Medline and Embase for studies published between database inception and February 5, 2021. We conducted a narrative synthesis of all models and assessed the risk of bias (ROB) in included studies using PROBAST. We also performed a meta-analysis of all external validation studies validating the Framingham hypertension risk model. We excluded models based on cross-sectional data, or those based on specific patient populations. Results: Our review includes 29 articles which contain 42 prediction models and 11 external validation studies of existing prediction models. Among model development studies, only five models performed both internal and external validation. Among the validation studies, only two existing models were externally validated by researchers other than the ones who developed the model. Most models had low ROB in the predictors and outcomes domains, and half had low ROB in the participants domain. However, all had high ROB in the analysis domain due to inappropriate handling of missing data and/or lack of adequate performance measures, which resulted in high overall ROB for all models. Conclusions: All current risk prediction models predicting hypertension or increased blood pressure have high ROB and most have not been externally validated. New studies should aim to reduce their ROB using standard reporting guidelines and externally validated existing models.