Key result
Nonlaboratory eight-variable model identifies undiagnosed hypertension phenotypes with an AUC of ~0.78.
Why the study?
Early diagnosis of hypertension is critical, but most screening models do not simultaneously distinguish phenotypes based on systolic or diastolic patterns.
Does a multinomial prediction model using nonlaboratory predictors accurately identify phenotypes of undiagnosed hypertension in adults without self-reported hypertension?
Observational (n=139,463)
Yes
Does a multinomial prediction model using nonlaboratory predictors accurately identify phenotypes of undiagnosed hypertension in adults without self-reported hypertension?
A multinomial prediction model using eight nonlaboratory predictors demonstrated good discrimination for identifying phenotypes of undiagnosed hypertension, which could aid screening in resource-limited settings.
Underscores need for improved hypertension detection strategies; leaves open clinical utility of this model pending validation in diverse cohorts.
Background/Objectives: Early diagnosis of hypertension (HTN) is critical, but most screening models do not simultaneously distinguish phenotypes based on systolic or diastolic patterns. We developed and temporally validated a multinomial model to predict normotension and three phenotypes of undiagnosed hypertension in Peru. Methods: We used ENDES 2017–2019 for development (final analytic n = 62,091) and ENDES 2021–2024 for temporal validation (final analytic n = 77,372), excluding 2020 due to COVID-19 disruptions. We included adults aged ≥18 years without self-reported HTN. The outcome was classified as normotension, isolated diastolic hypertension (IDH), isolated systolic hypertension (ISH), or systolic–diastolic hypertension (SDH). Eight nonlaboratory predictors were used: age, BMI, sex, residential altitude, smoking, alcohol consumption, vegetable intake, and fruit intake. Results: The model achieved an AUC of 0.789 (95% CI: 0.783–0.795) in training and 0.776 (95% CI: 0.770–0.781) in temporal validation. The prevalence of undiagnosed hypertension was 11.6% in the training set and 12.6% in the validation set. At a prespecified cutoff of 0.1004, sensitivity and specificity were 79.0% and 63.2% in training and 78.7% and 60.9% in validation, respectively (NPV 95.8% and 95.2%). Decision curve and clinical impact analyses suggested a positive net benefit and plausible referral volumes across a range of thresholds. Conclusions: This model could help prioritize confirmatory blood pressure measurements in resource-limited settings.
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Vera-Ponce et al. (2026) conducted an observational in Undiagnosed hypertension (n=139,463). Multinomial prediction model was evaluated on Prediction of normotension, isolated diastolic hypertension, isolated systolic hypertension, or systolic-diastolic hypertension (AUC 0.776, 95% CI 0.770-0.781). A multinomial prediction model using eight nonlaboratory predictors achieved an AUC of 0.776 (95% CI: 0.770-0.781) for identifying phenotypes of undiagnosed hypertension in temporal validation.
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