Sex inequalities in cardiovascular risk prediction

Author:

Elliott Joshua1234,Bodinier Barbara35ORCID,Whitaker Matthew35,Wada Rin35,Cooke Graham124,Ward Helen34,Tzoulaki Ioanna3456789ORCID,Elliott Paul345678ORCID,Chadeau-Hyam Marc35ORCID

Affiliation:

1. Department of Infectious Diseases, Faculty of Medicine, Imperial College London , London , UK

2. Imperial College Healthcare NHS Trust , London , UK

3. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London , 90 Wood Ln, London W12 0BZ , UK

4. National Institute for Health Research Imperial Biomedical Research Centre, Imperial College London , The Bays, Entrance, 2 S Wharf Rd, London W2 1NY , UK

5. MRC Centre for Environment and Health, School of Public Health, Imperial College London , Praed Street, London W2 1NY , UK

6. British Heart Foundation Centre for Research Excellence, Imperial College London , South Kensington Campus, London SW7 2AZ , UK

7. Dementia Research Institute at Imperial College London , 86 Wood Ln, London W12 0BZ , UK

8. Health Data Research UK, Imperial College London , Exhibition Rd, South Kensington, London SW7 2AZ , UK

9. Department of Hygiene and Epidemiology, University of Ioannina Medical School , Ioannina , Greece

Abstract

Abstract Aims Evaluate 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 results Prospective 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. Conclusion Use 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.

Funder

National Institute for Health Research

BRC

British Heart Foundation

Medical Research Council

Horizon 2020

Wellcome Trust

DRI

Engineering and Physical Sciences Research Council

Economic and Social Research Council

Publisher

Oxford University Press (OUP)

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