GWTG-HF score and acute coronary syndrome. (26th April 2021)

• Record Type: Journal Article
• Authors: Santos, M; Santos, H; Almeida, I; Miranda, H; Paula, S; Sa, C; Chin, J; Almeida, S; Sousa, C; Tavares, J; Santos, L; Almeida, ML
• Is Part Of: European heart journal. Volume 10(2021)Supplement 1
• Publication Date: 2021-04-26
• Subjects: 616.1205
• DOI: 10.1093/ehjacc/zuab020.078
• Languages: English
• ISSNs: 2048-8726

Abstract

Abstract: Funding Acknowledgements: Type of funding sources: None. OnBehalf: on behalf of the Investigators of " Portuguese Registry of ACS " Introduction: Regarding prognosis, acute coronary syndromes (ACS) are heterogeneous. Post-hospitalization (PH) risk stratification is crucial. The Get With The Guidelines Heart Failure score (GWTG‐HFS) predicts in‐hospital mortality (M) of patients (P) admitted with acute heart failure. Objective: To validate GWTG-HFS as predictor of PH early and late M and readmission (RA) rates, in our center population, using real-life data. Methods: Based on a single-center retrospective study, data collected from admissions between 1/01/20168 and 11/12/2019. Patients who survived the ACS and were discharged from the hospital were included. Concerning prognosis, we assessed 1-month M and RA (1mM and 1mRA), 6-month M and RA (6mM and 6mRA), 1-year M and RA (1yM and 1yRA). Statistical analysis used non-parametric tests, logistic regression and ROC curve analysis. Results: 268 patients with ACS, mean age was 66.4 ± 12.5 years old and 59.7% were male. The diagnosis was unstable angina in 2.6%, non-ST elevation myocardial infarction (NSTEMI) in 66.4% and ST elevation myocardial infarction (STEMI) in 31%. 41.8% of the P were or had been smokers, 68.5% had hypertension, 34.5% were diabetic and 50.9% had dyslipidaemia. Concerning coronary artery disease, 250 were submitted to coronary angiography – 18.8% had no lesions or non-significant lesions (stenosis <50%), 34.8% had one significant lesion, 23.2% had 2 significant lesions and 23.2% had 3 or more. Regarding left ventricle (LV) function, 70.5% of the P had no LV dysfunction, 15.7% had mild LV impairment (LVI), 9.3% moderate LVI and 4.5% had severe LVI. 1mM rate was 1.9% and 1yM rate was 7.8%. Age (p = 0.034), diabetes (p = 0.031), KKC (p < 0.001), BUN (p = 0.003) and LV function (p < 0.001) were predictors of 1mM. Age (p < 0.001), HR (p = 0.009), KKC (p = 0.032), BUN (p < 0.001), sodium (p < 0.001), creatinine (p < 0.001), Hb (p < 0.001), LV function (p < 0.001), de novo AF (p < 0.001) and number of arteries with significant disease (p = 0.044) were predictors of 1yM. Logistic regression and ROC curve analysis showed that GWTG-HFS was able to predict 1mM (Odds ratio (OR) 1.18, p = 0.005, confidence interval (CI) 1.05-1.33; area under curve (AUC) 0.872) and 1yM (OR 1.16, p = 0.001, CI 1.09-1.24, AUC 0.838) with excellent accuracy, and 1mRA (OR 1.10, p = 0.006, CI 1.03-1.18, AUC 0.677) and 1yRA (OR 1.04, p = 0.024, CI 1.01-1.08, AUC 0.580) with poor accuracy. A sub-analysis regarding NSTEMI P showed that GWTG-HFS was able to predict 1mM (OR 1.20, p = 0.010, CI 1.05-1.39, AUC 0.902) and 1yM (OR 1.15, p < 0.001, CI 1.07-1.23, AUC 0.817) with excellent accuracy. On the other hand, sub-analysis regarding STEMI showed that GWTG-HFS was not able to predict 1mM (p = 0.495) but was accurate at predicting 1yM (OR 1.18, p = 0.048, CI 1.00-1.39, AUC 0.881). Conclusion: This study confirms that, in our population, GWTG-HFS is a valuable tool in PH risk score stratification in ACS, particularly NSTEMI.