Predictive power of the grace score in population with diabetes

Anna Baeza-Román1, Eva de Miguel-Balsa1, Jaime Latour-Pérez1

  • 1Intensive Care Unit, Hospital General Universitario de Elche, Elche, Spain.

Insights

The GRACE score effectively predicts hospital mortality in acute coronary syndrome (ACS) patients, including those with diabetes mellitus (DM). Adding DM to the GRACE score did not improve its predictive accuracy.

Area of Science:

  • Cardiology
  • Clinical Risk Stratification
  • Diabetes Mellitus Research

Background:

  • Clinical guidelines advocate risk stratification for acute coronary syndrome (ACS) patients upon hospital admission.
  • Diabetes mellitus (DM) is a known independent predictor of mortality in ACS patients but is not currently incorporated into the GRACE risk score.

Purpose of the Study:

  • To validate the GRACE risk score in a contemporary ACS population.
  • To specifically evaluate the GRACE score's performance in diabetic patients.
  • To assess the impact of including diabetes mellitus as a variable in the GRACE risk score model.

Main Methods:

  • Retrospective cohort study utilizing the ARIAM-SEMICYUC registry.
  • Analysis of in-hospital mortality data for ACS patients.
  • Evaluation of GRACE score predictive power using ROC curve analysis and calibration assessment.
  • Assessment of the effect of DM inclusion via net reclassification improvement.

Main Results:

  • The GRACE score demonstrated good predictive power for hospital mortality in the study cohort.
  • Moderate calibration was observed for the GRACE score, with no significant variations across ACS types or DM status.
  • Incorporating diabetes mellitus as a variable did not enhance the predictive value of the GRACE model.

Conclusions:

  • The GRACE risk score exhibits appropriate predictive power and good calibration for ACS patients.
  • The GRACE score is clinically applicable to the subgroup of patients with diabetes mellitus.
  • Adding diabetes mellitus to the GRACE score did not provide additional predictive benefit.
Abstract

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