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Performance of cardiovascular risk prediction equations in Indigenous Australians

Abstract

Objective To assess the performance of cardiovascular disease (CVD) risk equations in Indigenous Australians.

Methods We conducted an individual participant meta-analysis using longitudinal data of 3618 Indigenous Australians (55% women) aged 30–74 years without CVD from population-based cohorts of the Cardiovascular Risk in IndigenouS People(CRISP) consortium. Predicted risk was calculated using: 1991 and 2008 Framingham Heart Study (FHS), the Pooled Cohorts (PC), GloboRisk and the Central Australian Rural Practitioners Association (CARPA) modification of the FHS equation. Calibration, discrimination and diagnostic accuracy were evaluated. Risks were calculated with and without the use of clinical criteria to identify high-risk individuals.

Results When applied without clinical criteria, all equations, except the CARPA-adjusted FHS, underestimated CVD risk (range of percentage difference between observed and predicted CVD risks: −55% to −14%), with underestimation greater in women (−63% to −13%) than men (−47% to −18%) and in younger age groups. Discrimination ranged from 0.66 to 0.72. The CARPA-adjusted FHS equation showed good calibration but overestimated risk in younger people, those without diabetes and those not at high clinical risk. When clinical criteria were used with risk equations, the CARPA-adjusted FHS algorithm scored 64% of those who had CVD events as high risk; corresponding figures for the 1991-FHS were 58% and were 87% for the PC equation for non-Hispanic whites. However, specificity fell.

Conclusion The CARPA-adjusted FHS CVD risk equation and clinical criteria performed the best, achieving higher combined sensitivity and specificity than other equations. However, future research should investigate whether modifications to this algorithm combination might lead to improved risk prediction.

  • epidemiology
  • cardiac risk factors and prevention
  • diabetes
  • global health
  • coronary artery disease
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