Evidence map›Paper›PMID 40654225›Full record

ArticleJournal of the American Heart Association2025

Development and Validation of an Incident Hypertension Risk Prediction Model for Young Adults.

Jaejin An, Heidi Fischer, Liang Ni, Mengying Xia, Soon Kyu Choi, Kerresa L Morrissette, Rong Wei, Kristi Reynolds, Paul Muntner, Lisandro D Colantonio and 9 more

Abstract readValidation Study
In one paragraph

Article in Journal of the American Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Association between PMBMC public health · 2026
    Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Jaejin AnDepartment of Research & Evaluation Kaiser Permanente Southern California Pasadena CA USA.ORCID 0000-0002-3161-5732
Heidi FischerDepartment of Research & Evaluation Kaiser Permanente Southern California Pasadena CA USA.ORCID 0000-0001-5343-0002
Liang NiDepartment of Research & Evaluation Kaiser Permanente Southern California Pasadena CA USA.
Mengying XiaDivision of General Medicine Columbia University Irving Medical Center New York NY USA.ORCID 0009-0009-8866-0057
Soon Kyu ChoiDepartment of Research & Evaluation Kaiser Permanente Southern California Pasadena CA USA.ORCID 0000-0001-9160-662X
Kerresa L MorrissetteDepartment of Research & Evaluation Kaiser Permanente Southern California Pasadena CA USA.ORCID 0000-0002-8333-0405
Rong WeiDepartment of Research & Evaluation Kaiser Permanente Southern California Pasadena CA USA.ORCID 0000-0003-0204-4982
Kristi ReynoldsDepartment of Research & Evaluation Kaiser Permanente Southern California Pasadena CA USA.ORCID 0000-0001-7619-1649
Paul MuntnerDepartment of Epidemiology University of Alabama at Birmingham Birmingham AL USA.ORCID 0000-0002-4711-5492
Lisandro D ColantonioDepartment of Epidemiology University of Alabama at Birmingham Birmingham AL USA.ORCID 0000-0001-8742-1788
Andrew E MoranDivision of General Medicine Columbia University Irving Medical Center New York NY USA.ORCID 0000-0003-3554-0085
Brandon K BellowsDivision of General Medicine Columbia University Irving Medical Center New York NY USA.ORCID 0000-0003-1395-6047
Monika M SaffordDivision of General Internal Medicine Weill Cornell Medicine New York NY USA.ORCID 0000-0002-3060-0563
Norrina B AllenDivision of Epidemiology Northwestern University Chicago IL USA.ORCID 0000-0002-6993-4384
Vanessa XanthakisDepartment of Medicine Boston University Chobanian and Avedisian School of Medicine Boston MA USA.ORCID 0000-0002-7352-621X
Carmen R IsasiDepartment of Epidemiology and Population Health Albert Einstein College of Medicine Bronx NY USA.ORCID 0000-0003-2700-9593
Linda C GalloDepartment of Psychology San Diego State University San Diego CA USA.ORCID 0000-0002-3678-5888
Krista M PerreiraDepartment of Social Medicine University of North Carolina School of Medicine Chapel Hill NC USA.ORCID 0000-0003-2906-0261
Yiyi ZhangDivision of General Medicine Columbia University Irving Medical Center New York NY USA.ORCID 0000-0002-8663-5001

Funding

NCI NIH HHS HHSN261201300004INHLBI NIH HHS HHSN268201000001INHLBI NIH HHS HHSN268201500001INHLBI NIH HHS HHSN268201800005INHLBI NIH HHS HHSN268201800007I
6 · The paper itself

Abstract

backgroundIdentifying young adults at high risk of hypertension can improve blood pressure screening recommendations.

methodsWe developed models to predict incident hypertension using diverse contemporary cohorts of young adults aged 18 to 39 years from Kaiser Permanente Southern California (derivation and internal validation) and 3 cohort studies (CARDIA [Coronary Artery Risk Development in Young Adults], FHS [Framingham Heart Study], HCHS/SOL [Hispanic Community Health Study/Study of Latinos]; external validation). Predictors included age, systolic and diastolic blood pressure, body mass index, smoking, social determinants of health, comorbidities, high- and low-density lipoprotein cholesterol, and pregnancy-related hypertensive disorders. We used Cox elastic net and random survival forests to develop sex-specific models and compared their performance to 2021 US Preventive Services Task Force blood pressure screening recommendations.

resultsAmong 355 524 adults from Kaiser Permanente Southern California (mean age, 29 years; mean systolic/diastolic blood pressure, 115/70 mm Hg), 11.7% developed hypertension in a median of 5.4 years. External validation showed good discrimination and calibration (Harrell's C-statistic, 0.76 and 0.82; Integrated Brier Score, 0.04 and 0.02 for men and women, respectively). Compared with US Preventive Services Task Force, a 10-year risk for hypertension of ≥15% in the external cohort using the new model showed similar sensitivity (men, 0.90 versus 0.89; women, 0.81 versus 0.81) and moderate improvement in specificity (men, 0.35 versus 0.25; women, 0.66 versus 0.44). Using the National Health and Nutrition Examination Survey, the prediction model estimated 28.5 million US young adults being at high risk of hypertension compared with 45.3 million by the US Preventive Services Task Force.

conclusionsCompared with the US Preventive Services Task Force, the hypertension risk prediction model may be a more efficient tool to identify high-risk young adults for early intervention.

Indexed as

Blood PressureHypertensionAdolescentAdultAge FactorsCaliforniaFemaleHumansIncidenceMalePredictive Value of TestsReproducibility of ResultsRisk AssessmentRisk FactorsUnited StatesYoung Adulthypertensionprediction modelyoung adults

Identifiers

PMID40654225
PMCPMC12533609

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.