What does this research mean for the field?

There is no justification for developing separate coronary risk prediction models for individuals with and without diabetes; instead, diabetes status should be used as a risk variable in an overall population equation. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

January 31, 2006

Coronary risk prediction for those with and without diabetes

Structured PICO

How should a prior diagnosis of diabetes be incorporated into coronary risk prediction models?

Population

364,566 subjects from 33 cohort studies

Intervention

Risk prediction equations incorporating diabetes status (stepped model, interaction model, fixed model)

Comparator

Comparison between the three risk prediction models

Outcome

Model performance (area under the receiver operating characteristic curve [AUC] and Hosmer-Lemeshow statistics) for predicting fatal coronary events

There is no justification for developing separate coronary risk prediction models for patients with diabetes or assuming a uniform high risk; diabetes should instead be included as a risk variable in overall population equations.

Abstract

BACKGROUND: Coronary risk prediction 'engines' are now in common use, and their worth is well proven. There remains the question of how to deal with a prior diagnosis of diabetes. DESIGN: An individual participant meta-analysis of 33 cohort studies involving 364 566 subjects. METHODS: Fatal coronary hazard ratios for age, smoking, systolic blood pressure and cholesterol, were computed from Cox models, comparing those with and without diabetes. Three risk prediction equations were compared: a 'stepped model', which included the risk factors and diabetes status; an 'interaction model', which included interactions between diabetes and the risk factors; and a 'fixed model', which fixed the 10-year rate of coronary death amongst those with diabetes to be 7%. These were compared through the area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow statistics. RESULTS: The hazard ratio for age was greater for those without diabetes than those with, for men (P=0.005) and women (P=0.02); for men only, systolic blood pressure showed a similar differential (P=0.011). Nevertheless, AUCs were only 0.001 different for the stepped and interaction models for each sex. The AUC for the fixed model was lower and, unlike the other two, showed significant lack of fit for both sexes (P<0.001). CONCLUSIONS: There is no justification for developing separate risk prediction models for those with and without diabetes, nor for assuming that everyone with diabetes should be considered as being at a common high level of risk. Diabetes status might, instead, be used as a risk variable in an overall population equation.

Mark Helpful

Bookmark

Relay