Key result
Cognitive prediction models in hypertension show moderate discrimination but suffer from high bias and poor reproducibility.
Why the study?
The methodological quality, validation status, and reproducibility of existing models to identify or predict cognitive impairment in patients with hypertension had not been systematically assessed.
Do multivariable clinical prediction models accurately identify cognitive impairment in adults with hypertension?
Systematic Review
Do multivariable clinical prediction models accurately identify cognitive impairment in adults with hypertension?
While existing models show some ability to identify cognitive impairment in hypertensive patients, their clinical utility is currently limited by high risk of bias, poor reproducibility, and a lack of external validation.
Existing models warrant clinical caution in hypertension; high bias and minimal external validation leave reliability open.
Background Hypertension is strongly associated with cognitive impairment. Existing tools include models designed to identify prevalent cognitive impairment and a smaller number developed to predict future risk. However, their methodological quality, validation status, and reproducibility have not been systematically assessed. We conducted a systematic review and critical appraisal of existing models designed to identify cognitive impairment in patients with hypertension. Methods Chinese and English databases were searched from inception to December 23, 2025. Studies that developed, updated, or validated a multivariable model, nomogram, score, or machine-learning tool for cognitive impairment, mild cognitive impairment, cognitive decline, subjective cognitive decline, or dementia among adults with hypertension were included. Data were extracted using CHARMS, and risk of bias was assessed with PROBAST. The synthesis emphasized model purpose, temporality, outcome definition, discrimination, calibration, validation, risk of bias, and reproducibility. Reported c-statistics were summarized descriptively because of substantial heterogeneity in study design, outcome definitions, cognitive assessment tools, and repeated model estimates from the same datasets. Results Seventeen studies reporting 25 models were included. Most models were developed in China using cross-sectional data, with cognitive outcomes assessed using varied instruments. Reported AUCs ranged from 0.344 to 0.938; most multivariable models exceeded 0.70. Internal validation was reported in 15 studies, whereas only four conducted external validation and 11 assessed calibration. Sixteen studies were judged at high risk of bias. Reproducibility was limited: four studies provided sufficient information for independent calculation, eight were partially reproducible, and five were insufficiently reported. Common predictors included older age, lower education, depression, obesity, longer hypertension duration, greater hypertension severity, and adverse metabolic indicators. Discussion Existing models show some ability to distinguish hypertensive patients with and without cognitive impairment, while evidence supporting future-risk prediction remains limited. Future work should prioritize prospective cohorts, standardized outcome definitions, external validation, calibration, and transparent reporting of equations, intercepts, scoring rules, code, and model objects. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420251157136 , identifier CRD420251157136.
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Sun et al. (2026) conducted a systematic review in Hypertension and cognitive impairment. Prediction models for cognitive impairment was evaluated on Model discrimination (AUC) and reproducibility. Existing prediction models for cognitive impairment in hypertensive patients demonstrate moderate-to-good discrimination with AUCs mostly exceeding 0.70, but suffer from high risk of bias, limited external validation, and poor reproducibility.
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