Key result
Machine learning models outperform traditional models for predicting incident hypertension, achieving an AUC of 0.82.
Why the study?
What is the predictive performance, validation status, and clinical utility of multivariable prognostic models for incident hypertension in initially normotensive individuals?
Systematic Review
What is the predictive performance, validation status, and clinical utility of multivariable prognostic models for incident hypertension in initially normotensive individuals?
Effect estimate: AUC 0.78 (95% CI 0.76-0.80)
There is a significant gap between the development of prognostic models for incident hypertension and their clinical implementation, highlighting a need to prioritize external validation and clinical-impact evaluation over de novo model derivation.
Models for incident hypertension show limited clinical implementation; confirms persistent development-application gap and supports targeted implementation research.
Hypertension represents a major modifiable cardiovascular risk factor with increasing global prevalence. While numerous prognostic models for incident hypertension have been developed, their clinical implementation remains limited, suggesting a gap between model development and practical application. This study aimed to synthesize evidence from systematic reviews on multivariable prognostic models estimating the future risk of incident hypertension among initially normotensive individuals, evaluating methodological quality, predictive performance, validation status, calibration reporting, and clinical utility. We conducted an umbrella review, searching six databases from January 2000 to March 2026. Systematic reviews (SRs) evaluating multivariable prognostic models for incident hypertension with documented external validation evidence were included; reviews mixing prognostic and cross-sectional diagnostic screening models were retained only when eligible prognostic evidence could be distinguished and were examined in sensitivity synthesis. Methodological quality was assessed using ROBIS, and primary-study risk of bias was extracted from PROBAST/CHARMS assessments reported by the included reviews. Six SRs (three with meta-analyses) identified 162 unique models reported as prognostic. Models were categorized as traditional statistical (60%), machine learning (30%), and genetic/polygenic risk score-based (10%). Reported pooled AUCs were 0.78 (95% CI 0.76–0.80) for traditional models and 0.82 (95% CI 0.77–0.86) for machine learning models, but all quantitative summaries showed extreme heterogeneity (I2 > 99%) and should therefore be interpreted descriptively rather than as stable estimates of clinical performance. Only 21 of 94 models with extractable validation information (22%) underwent at least one external validation, and only seven of 162 unique models were externally validated more than once. Calibration was reported in a minority of models, and no published clinical-impact studies evaluating the use of these models in routine practice were identified. The field should avoid prioritizing additional de novo model derivation in already well-represented settings and should redirect resources toward external validation, recalibration, clinical-impact evaluation, and implementation of the most robust existing models in primary care.
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Vera-Ponce et al. (2026) conducted a systematic review in Incident hypertension. Multivariable prognostic models was evaluated on Predictive performance (AUC) (AUC 0.78, 95% CI 0.76-0.80). Multivariable prognostic models for incident hypertension yielded pooled AUCs of 0.78 (95% CI 0.76-0.80) for traditional and 0.82 (95% CI 0.77-0.86) for machine learning models, with high heterogeneity.
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