SynthesisPloS one2017
Recent development of risk-prediction models for incident hypertension: An updated systematic review.
Synthesis in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
48 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Prognostic risk models for incident hypertension: A PRISMA systematic review and meta-analysis.PloS one · 2024Pooled it
- Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis.PloS one · 2022Pooled it
- Pooled it
- Heritability of blood pressure traits in diverse populations: a systematic review and meta-analysis.Journal of human hypertension · 2019Pooled it
- Vasorelaxant Effects of Wild Jujube Leaf Aqueous Extract: Endothelial NO/cGMP/SERCA Signaling, Antioxidant Activity, and AI-Based Molecular Docking.Molecules (Basel, Switzerland) · 2026Article
- Multi-horizon machine learning for population-level hypertension risk stratification in the UK Biobank.European heart journal. Digital health · 2026Article
- Development and validation of a hypertension risk prediction model using electronic health records data from general practices in Hampshire and Isle of Wight integrated care system.BMC cardiovascular disorders · 2026Article
- Development and Internal Validation of Machine-Learning Models for Short-Term Prediction of Day-to-Day Home Blood Pressure Variability Using IoT-Based Environmental and Activity Data: Protocol for the AURA-BPV Study.Circulation reports · 2026Article
- Development of a Sex-Specific Prevalent Hypertension Discrimination Model in Korean Adults Using Genetic Risk Scores and Clinical Biomarkers: A Cross-Sectional Study.Current issues in molecular biology · 2026Article
- Remote photoplethysmography for health assessment: a review informed by IntelliProve technology.Frontiers in digital health · 2025Review
- Remnant cholesterol, not LDL cholesterol, is associated with the risk of hypertension in the middle-aged and elderly population: Bushehr Elderly Health cohort.Frontiers in cardiovascular medicine · 2025Article
- Review
- Development of risk models of incident hypertension using machine learning on the HUNT study data.Scientific reports · 2024Article
- The association of ideal cardiovascular health metrics and incident hypertension among an urban population of Iran: a decade follow-up in Tehran Lipid and Glucose Study.Journal of human hypertension · 2024Article
- Non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and hypertension in American adults: a NHANES cross-sectional study.Frontiers in physiology · 2024Article
- Vasoactive mediators of hypertension in obesity.American journal of physiology. Endocrinology and metabolism · 2023Article
- Feasibility and Clinical Utility of Prediction Models for Breast Cancer-Related Lymphedema Incorporating Racial Differences in Disease Incidence.JAMA surgery · 2023Article
- Predicting the onset of hypertension for workers: does including work characteristics improve risk predictive accuracy?Journal of human hypertension · 2023Article
- Social and Metabolic Determinants of Prevalent Hypertension in Men and Women: A Cluster Analysis from a Population-Based Study.International journal of environmental research and public health · 2023Article
- Factors Associated with Hypertension Among Adults: A Cross-Sectional Analysis of the Indonesian Family Life Survey.Vascular health and risk management · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundHypertension is a leading global health threat and a major cardiovascular disease. Since clinical interventions are effective in delaying the disease progression from prehypertension to hypertension, diagnostic prediction models to identify patient populations at high risk for hypertension are imperative.
methodsBoth PubMed and Embase databases were searched for eligible reports of either prediction models or risk scores of hypertension. The study data were collected, including risk factors, statistic methods, characteristics of study design and participants, performance measurement, etc.
resultsFrom the searched literature, 26 studies reporting 48 prediction models were selected. Among them, 20 reports studied the established models using traditional risk factors, such as body mass index (BMI), age, smoking, blood pressure (BP) level, parental history of hypertension, and biochemical factors, whereas 6 reports used genetic risk score (GRS) as the prediction factor. AUC ranged from 0.64 to 0.97, and C-statistic ranged from 60% to 90%.
conclusionsThe traditional models are still the predominant risk prediction models for hypertension, but recently, more models have begun to incorporate genetic factors as part of their model predictors. However, these genetic predictors need to be well selected. The current reported models have acceptable to good discrimination and calibration ability, but whether the models can be applied in clinical practice still needs more validation and adjustment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.