In:
European Heart Journal. Acute Cardiovascular Care, Oxford University Press (OUP), Vol. 10, No. Supplement_1 ( 2021-04-26)
Abstract:
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 & lt;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 & lt; 0.001), BUN (p = 0.003) and LV function (p & lt; 0.001) were predictors of 1mM. Age (p & lt; 0.001), HR (p = 0.009), KKC (p = 0.032), BUN (p & lt; 0.001), sodium (p & lt; 0.001), creatinine (p & lt; 0.001), Hb (p & lt; 0.001), LV function (p & lt; 0.001), de novo AF (p & lt; 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 & lt; 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.
Type of Medium:
Online Resource
ISSN:
2048-8726
,
2048-8734
DOI:
10.1093/ehjacc/zuab020.078
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2021
detail.hit.zdb_id:
2663340-1
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