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Does admission hyperglycaemia add prognostic value to the GRACE score?
1Centro Hospitalar do Baixo Vouga, Cardiology - Aveiro - Portugal
Introduction: Hyperglycaemia at admission (AH) is a well documented major risk factor for mortality in the acute coronary syndrome (ACS). However, this parameter is not included in risk prediction scores, including Global Registry of Acute Coronary Events - GRACE (GS). Methods: We studied 431 consecutive patients admitted to our coronary care unit with an ACS [age: 67 ± 13 years, 26% female, 30,6% STEMI]. Our primary endpoint was the occurrence of all-cause mortality at mean follow-up of 22 ± 10 months. Patients were categorized in 6 groups according to their AG combined with GS. The ability of the two logistic regression models (GS categorized alone and in combination with AG as continuous variable or using the cutoff >140) to predict death was asseded by binary logistic regression, calculating ROC curves and the corresponding areas under the curve (AUC). Comparative analysis between different AUC was performed by non-parametric method described by DeLong. Continuous net reclassification improvement (NRI) were also calculated. Results: The best cut-point for AG was 140 mg/dl (sensitivity 63% and specificity 60%), and 42,9% of the patients had increased levels. This group was elderly, had more prevalence of hypertension, hyperlipidemia and worse renal function. GRACE score and troponin levels were also more elevated and had more frequent Killip class =2 and FEVE =35%. In multivariate Cox analysis adjusted to potential confounding factors (gender, obesity, hemoglobin level, pro-BNP, prior events, hyperlipidemia, grace score and AG), only AG (HR 1,004; IC 95% 1,000-1,007; p = 0,035), and GS (HR 1,02; IC 95% 1,003-1,02; p = 0,006) were sustained as independent predictors of mortality. AG = 140 was associated to lower survival at follow up (32,6±0,9 vs 35,5±0,6 months, log rank test p=0,006). The inclusion of AG, as a continuous variable, in a logistic regression model with GS, increased the area under the ROC curve from 0.647 to 0.705 (p=0.01) and was associated with an improvement in the NRI (30,6% [ 0.0091 - 0.6023 ]; p-value: 0.04), suggesting effective reclassification. Nevertheless when used as categorical variable in a logistic regression model with GS, the increased area under the ROC curve was only 0,02 without statistical signification (p=0,57). Conclusion: In our study AG had a predictive cardiovascular prognosis when added to GS. The addition of AG to the GS may further improve risk stratification and prediction of mortality in patients with ACS.
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