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Cardiovascular risk prediction in diabetic men and women using hemoglobin A1c vs diabetes as a high-risk equivalent

Nina P Paynter1, Norman A Mazer, Aruna D Pradhan

  • 1Brigham and Women's Hospital, Division of Preventive Medicine, 900 Commonwealth Ave., Boston, MA 02215, USA. npaynter@partners.org

Archives of Internal Medicine
|July 27, 2011
PubMed

Insights

Adding hemoglobin A1c (HbA1c) to cardiovascular disease (CVD) risk models significantly improves predictions for diabetic individuals. This approach offers better risk assessment than classifying diabetes as a CVD risk equivalent.

Area of Science:

  • Cardiovascular disease risk prediction
  • Diabetes mellitus management
  • Clinical risk modeling

Background:

  • Current cardiovascular disease (CVD) risk classification treats diabetes as a risk equivalent.
  • The added value of incorporating hemoglobin A1c (HbA1c) levels for diabetic patients in CVD risk prediction models remains unclear.

Purpose of the Study:

  • To evaluate if including HbA1c levels in CVD risk models enhances prediction accuracy for diabetic individuals compared to the current risk-equivalent classification.

Main Methods:

  • Prospective follow-up of 24,674 women and 11,280 men for CVD events.
  • Development of CVD risk models with and without HbA1c for diabetic participants.
  • Comparison of model performance using C statistic and net reclassification improvement (NRI).

Main Results:

  • Models with HbA1c significantly improved CVD risk prediction in women (C statistic change: 0.177, NRI: 26.7%) and men (C statistic change: 0.039, NRI: 9.2%).
  • HbA1c inclusion also improved prediction over a dichotomous diabetes term in women (NRI: 11.8%).

Conclusions:

  • Incorporating HbA1c levels into CVD risk prediction models significantly enhances accuracy for both women and men with diabetes.
  • This refined approach offers superior risk stratification compared to the diabetes risk-equivalent strategy.
Abstract

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