Evidence map›Paper›PMID 38833617›Full record

ArticleCardiovascular research2024

Sex inequalities in cardiovascular risk prediction.

Joshua Elliott, Barbara Bodinier, Matthew Whitaker, Rin Wada, Graham Cooke, Helen Ward, Ioanna Tzoulaki, Paul Elliott, Marc Chadeau-Hyam

Abstract readComparative Study
In one paragraph

Article in Cardiovascular research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Joshua ElliottDepartment of Infectious Diseases, Faculty of Medicine, Imperial College London, London, UK.
Barbara BodinierDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, 90 Wood Ln, London W12 0BZ, UK.ORCID 0000-0002-0781-3624
Matthew WhitakerDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, 90 Wood Ln, London W12 0BZ, UK.
Rin WadaDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, 90 Wood Ln, London W12 0BZ, UK.
Graham CookeDepartment of Infectious Diseases, Faculty of Medicine, Imperial College London, London, UK.
Helen WardDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, 90 Wood Ln, London W12 0BZ, UK.
Ioanna TzoulakiDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, 90 Wood Ln, London W12 0BZ, UK.ORCID 0000-0002-4275-9328
Paul ElliottDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, 90 Wood Ln, London W12 0BZ, UK.ORCID 0000-0002-7511-5684
Marc Chadeau-HyamDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, 90 Wood Ln, London W12 0BZ, UK.ORCID 0000-0001-8341-5436

Funding

BRC PA6381_WPEABritish Heart Foundation RE/18/4/34215DRIEconomic and Social Research CouncilEngineering and Physical Sciences Research CouncilHorizon 2020 874627Medical Research Council MR/L01341X/1National Institute for Health ResearchWellcome TrustWellcome Trust 205456/Z/16/Z
6 · The paper itself

Abstract

aimsEvaluate sex differences in cardiovascular disease (CVD) risk prediction, including use of (i) optimal sex-specific risk predictors and (ii) sex-specific risk thresholds. METHODS AND

resultsProspective cohort study using UK Biobank, including 121 724 and 182 632 healthy men and women, respectively, aged 38-73 years at baseline. There were 11 899 (men) and 9110 (women) incident CVD cases (hospitalization or mortality) with a median of 12.1 years of follow-up. We used recalibrated pooled cohort equations (PCEs; 7.5% 10-year risk threshold as per US guidelines), QRISK3 (10% 10-year risk threshold as per UK guidelines), and Cox survival models using sparse sex-specific variable sets (via LASSO stability selection) to predict CVD risk separately in men and women. LASSO stability selection included 12 variables in common between men and women, with 3 additional variables selected for men and 1 for women. C-statistics were slightly lower for PCE than QRISK3 and models using stably selected variables, but were similar between men and women: 0.67 (0.66-0.68), 0.70 (0.69-0.71), and 0.71 (0.70-0.72) in men and 0.69 (0.68-0.70), 0.72 (0.71-0.73), and 0.72 (0.71-0.73) in women for PCE, QRISK3, and models using stably selected variables, respectively. At current clinically implemented risk thresholds, test sensitivity was markedly lower in women than men for all models: at 7.5% 10-year risk, sensitivity was 65.1 and 68.2% in men and 24.0 and 33.4% in women for PCE and models using stably selected variables, respectively; at 10% 10-year risk, sensitivity was 53.7 and 52.3% in men and 16.8 and 20.2% in women for QRISK3 and models using stably selected variables, respectively. Specificity was correspondingly higher in women than men. However, the sensitivity in women at 5% 10-year risk threshold increased to 50.1, 58.5, and 55.7% for PCE, QRISK3, and models using stably selected variables, respectively.

conclusionUse of sparse sex-specific variables improved CVD risk prediction compared with PCE but not QRISK3. At current risk thresholds, PCE and QRISK3 work less well for women than men, but sensitivity was improved in women using a 5% 10-year risk threshold. Use of sex-specific risk thresholds should be considered in any re-evaluation of CVD risk calculators.

Indexed as

Cardiovascular DiseasesHeart Disease Risk FactorsAdultAgedFemaleHealth Status DisparitiesHumansIncidenceMaleMiddle AgedPredictive Value of TestsPrognosisProspective StudiesRisk AssessmentSex FactorsTime FactorsBiomarkersCVD risk predictionPooled cohort equationsQRISK3Sparse variable selection

Identifiers

PMID38833617
PMCPMC11416056

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.