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Funding Acknowledgements Type of funding sources: None. OnBehalf Portuguese Registry of Acute Coronary Syndromes (ProACS) Introduction Brain natriuretic peptide (BNP) is a highly sensitive and specific biomarker on the extension of myocardial infarction, strongly related to short and long-term prognosis in patients with ST elevation myocardial infarction (STEMI). Objective To evaluate diagnostic and prognostic value of BNP levels in a Portuguese cohort of STEMI patients. Material and methods Retrospective analysis of patients admitted with STEMI included in the Portuguese Registry of Acute Coronary Syndromes between 2010-19. Patients were divided in three groups regarding BNP: Group 1 if BNP <100 pg/ml; Group 2 100 ≤ BNP < 400 pg/ml and Group 3 ≥400 pg/ml. Independent predictors of a composite of all-cause mortality and rehospitalization for cardiovascular causes were assessed by multivariate logistic regression. Results 1650 patients were included, mean age 64 ± 13 years, 75.4% male. 39.0% (n = 643) integrated group 1, 39.5% (n = 652) group 2 and 21.5% (n = 355) group 3. Group 3 patients were significantly older (58 ± 11 vs 66 ± 13 vs 72 ± 12 years, p < 0.001), had more classic cardiovascular risk factors, except for smoker status, and more previous history of cardiovascular disease, chronic kidney disease, chronic obstructive pulmonary disease and cancer. Anterior STEMI was the most frequent location (51.1%), however group 3 patients presented with lower systolic blood pressure (136 ± 30 vs 140 ± 31 vs 131 ± 28 mmHg, p < 0.001) and higher heart rate (76 ± 17 vs 77 ± 19 vs 84 ± 26 bpm, p < 0.001) and KK class (KK class > I 5.6 vs 9.5 vs 28.5%, p < 0.001). They also presented higher levels of creatinine (1 ± 0.5 vs 1.2 ± 0.9 vs 1.5 ± 1 mg/dl, p < 0.001) and the lowest levels of hemoglobin (13.5 ± 1.6 vs 12.6 ± 1.9 vs 11.7 ± 2.1 g/dl, p < 0.001). Mean ejection fraction (EF) was lower in group 3 (58 ± 11 vs 53 ± 12 vs 44 ± 13%, p < 0.001). Multivessel disease was more common in group 3 (34.8 vs 44.8 vs 51.3%, p < 0.001), where a higher percentage was proposed to medical therapy (2.8 vs 3.5 vs 8.5%, p < 0.001). In the patients proposed to revascularization, although not statistically significant, there was a trend towards surgical revascularization or hybrid approach. In-hospital complications were more frequent in group 3, especially heart failure (HF) (18.9% mean vs 45.4%, p < 0.001), and mortality was seven times superior in group 3 versus group 1 (1.2% vs. 8.5%, p < 0.001). The composite endpoint of 1-year mortality and cardiovascular rehospitalization occurred in 12%. After propensity score application, the 1-year endpoint total mortality rate and cardiovascular readmission was 20.3%, and higher BNP was associated with higher rates (p < 0.001). Predictor factors for the composite endpoint, evaluated through Cox multivariate regression were previous HF, multivessel disease, EF < 30% and the use of nitrates and aldosterone antagonists. The use of aspirin was a protector factor. Conclusion BNP levels during index hospitalization were a powerful prognostic biomarker for all-cause mortality MACE in patients admitted with STEMI.

Funding Acknowledgements Type of funding sources: None. Introduction Patients (P) with acute heart failure (AHF) are a heterogeneous population. Therefore, early risk stratification at admission is essential. The Get With The Guidelines Heart Failure score (GWTG-HF) predicts in-hospital mortality (IHM) of patients admitted with AHF. GRACE score estimates risk of death, including IHM and long-term mortality (M), in non-ST elevation acute coronary syndromes. Objective To validate GRACE score in AHF and to compare GRACE and GWTG-HF scores as predictors of IHM, post discharge early and late M [1-month mortality (1mM) and 1-year M (1yM)], 1-month readmission (1mRA) and 1-year readmission (1yRA), in our center population, using real-life data. Methods Based on a single-center retrospective study, data collected from P admitted in the Cardiology department with AHF between 2010 and 2017. P without data on previous cardiovascular history or uncompleted clinical data were excluded. Statistical analysis used chi-square, non-parametric tests, logistic regression analysis and ROC curve analysis. Results 35.3% were admitted in Killip-Kimball class (KKC) 4. Mean GRACE was 147.9 ± 30.2 and mean GWTG-HF was 41.7 ± 9.6. Inotropes’ usage was necessary in 32.7% of the P, 11.3% of the patients needed non-invasive ventilation, 8% needed invasive ventilation. IHM rate was 5%, 1mM was 8% and 1yM 27%. 6.3% of the patients were readmitted 1 month after discharge and 52.7% had at least one more admission in the year following discharge. Older age (p < 0.001), lower SBP (p = 0,005), higher urea (p = 0,001), lower sodium (p = 0.005), previous history of percutaneous coronary intervention (p = 0,017), lower GFR (p < 0.001) and need of inotropes (0.001) were predictors of 1yM after discharge in our population. As expected, patients presenting in KKC 4 had higher IHM (OR 8.13, p < 0.001), higher 1mM (OR 4.13, p = 0.001) and higher 1yM (OR 1.96, p = 0.011). On the other hand, KKC at admission did not predict readmission (either 1mRA or 1yRA, respectively p = 0.887 and p = 0.695). Logistic regression confirmed that GWTG-HF was a good predictor of IHM (OR 1.12, p < 0.001, CI 1.05-1.19) but also 1mM (OR 1.1, p = 0.001, CI 1.04-1.16) and 1yM (OR 1.08, p < 0.001, CI 1.04-1.11). GRACE also showed the ability to predict IHM (OR 1.06, p < 0.001, CI 1.03-1.10), 1mM (OR 1.04, p < 0.001, CI 1.02-1.06) and 1yM (OR 1.03, p < 0.001, CI 1.01-1.03). ROC curve analysis revealed that GRACE and GWTG-HF were accurate at predicting IHM (AUC 0.866 and 0.774, respectively), 1mM (AUC 0.779 and 0.727, respectively) and 1yM (AUC 0.676 and 0.672, respectively). Both scores failed at predicting 1mRA (GRACE p = 0.463; GWTG-HF p = 0.841) and 1yRA (GRACE p = 0.244; GWTG-HF p = 0.806). Conclusion This study confirms that, in our population, both scores were excellent at predicting IHM, with GRACE performing better. Although both scores were able to predict post-discharge mortality outcomes, their performance was poorer.

Funding Acknowledgements Type of funding sources: None. Introduction Patients (pts) with acute heart failure (AHF) are a heterogeneous population. Risk stratification at admission may help predict in-hospital complications and needs. ACTION ICU score is validated to estimate the risk of complications requiring ICU care in non-ST elevation acute coronary syndromes. Objective To validate ACTION-ICU score in AHF as predictor of in-hospital M (IHM), post discharge early M [1-month mortality (1mM)] and 1-month readmission (1mRA), in our center population, using real-life data. Methods Based on a single-center retrospective study, data collected from pts admitted in the Cardiology department with AHF between 2010 and 2017. Pts without data on previous cardiovascular history or uncompleted clinical data were excluded. Statistical analysis used non-parametric tests, logistic regression analysis and ROC curve analysis. Results We included 300 pts admitted with AHF. Mean age was 67.4 ± 12.6 years old and 72.7% were male. 37.7% had previous history of revascularization procedures, 66.9% had hypertension, 41% were diabetic and 38% had dyslipidaemia. Mean heart rate was 95.5 ± 27.5bpm, mean systolic blood pressure (SBP) was 131.2 ± 37.0mmHg, mean urea level at admission was 68.8 ± 40.7mg/dL, mean sodium was 137.6 ± 4.7mmol/L, mean glomerular filtration rate (GFR) was 57.1 ± 23.5ml/min. 35.3% were admitted in Killip-Kimball class (KKC) 4. Mean ACTION-ICU score was 10.4 ± 2.3. Inotropes’ usage was necessary in 32.7% of the pts, 11.3% of the pts needed non-invasive ventilation (NIV), 8% needed invasive ventilation (IV). IHM rate was 5% and 1mM was 8%. 6.3% of the pts were readmitted 1 month after discharge. Older age (p < 0.001), lower SBP (p = 0,035), presenting in KKC 4 (p < 0.001, OR 8.13) and need of inotropes (p < 0.001) were predictors of IHM in our population. Older age (OR 1.06, p = 0.002, CI 1.02-1.10), lower SBP (OR 1.01, p = 0.05, CI 1.00-1.02) and lower left ventricle ejection fraction (LVEF) (OR 1.06, p < 0.001, CI 1.03-1.09) were predictors of need of NIV. None of the studied variables were predictive of need of IV. LVEF (OR 0.924, p < 0.001, CI 0.899-0.949), lower SBP (OR 0.80, p < 0.001, CI 0.971-0.988), higher urea (OR 1.01, p < 0.001, CI 1.005-1.018) and lower sodium (OR 0.92, p = 0.002, CI 0.873-0.971) were predictors inotropes’ usage. ACTION-ICU was able to predict IHM (OR 1.51, p = 0.02, CI 1.158-1.977), 1mM (OR 1.45, p = 0.002, CI 1.15-1.81) and inotropes’ usage (OR 1.22, p = 0.002, CI 1.08-1.39), but not 1mRA, the need of IV or NIV. ROC curve analysis revealed ACTION-ICU performs well when predicting IHM (Area under curve (AUC) 0.729, confidence interval (CI) 0.59-0.87), inotropes’ usage (AUC 0.619, CI 0.54-0.70) and 1mM (AUC 0.705, CI 0.58-0.84). Conclusion In our population, ACTION-ICU score was able to predict IHM, 1mM and inotropes’s usage.

Funding Acknowledgements Type of funding sources: None. OnBehalf on behalf of the Investigators of " Portuguese Registry of ACS " Introduction Stroke is a potential complication of acute coronary syndromes (ACS) and it is, therefore, important to access its impact on prognosis and identify patients with higher risk of stroke in the setting of ACS. Objective To evaluate predictors and prognosis of stroke in the setting of ACS. Methods Based on a multicenter retrospective study, data collected from admissions between 1/10/2010 and 4/09/2019. Patients (pts) without data on previous cardiovascular history or uncompleted clinical data were excluded. Pts were divided in 2 groups (G): GA – pts without stroke; GB - pts with stroke during hospitalization. Logistic regression was performed to assess predictors of stroke in ACS. Survival analysis was evaluated through Kaplan Meier curve. Results Population – 25711 pts with ACS, CA occurred in 154 (0.6%). Regarding epidemiological factors and past history, GB was older (72 ± 12 vs 67 ± 14, p < 0.001), had higher rates of females (53.2% vs 27.5%, p < 0.001), diabetes (43.9% vs 31.5%, p < 0.001), previous stroke (13.3% vs 7.2%, p = 0.004), peripheric arterial disease (9.2% vs 5.5%, p = 0.044) and dementia (6.8% vs 1.7%, p < 0.001), and had lower rates of smoking (16.6% vs 26.7%, p = 0.005), dyslipidaemia (53.5% vs 61.6%, p = 0.047) and previous ACS (12.7% vs 20.6%, p = 0.017. GB had longer times from first symptoms to admission (340min vs 240min, p = 0.011). The groups were similar regarding diagnosis, namely non-ST-elevation myocardial infarction (MI) (p = 0.345) and ST-elevation MI (p = 0.541). GB had higher heart rate (HR) (84 ± 24 vs 77 ± 19, p = 0.001), presented more frequently in Killip-Kimball class (KKC) ≥2 (28.0% vs 15.1%, p < 0.001), in atrial fibrillation (AF) (16.4% vs 7.1%, p < 0.001) and with higher brain-natriuretic peptide levels (545 vs 180, p < 0.001). The groups were similar regarding culprit lesion and number of lesions. GB had more left ventricle (<50%) dysfunction (51.4% vs 39.1%, p < 0.001) and needed more frequently mechanical ventilation (10.4% vs 1.9%, p < 0.001) and provisory pacemaker (8.4% vs 1.5%, p < 0.001). Logistic regression confirmed that older age (p = 0.018, OR 1.69, CI 1.10-2.60), female gender (p < 0.001, OR 2.09, CI 1.38-3.15), diabetes (p = 0.002, OR 1.91, CI 1.27-2.86), dementia (p = 0.047, OR 2.13, CI 1.01-4.50), AF (p = 0.024, OR 1.87, CI 1.09-3.21) and lower left ventricle function (p = 0.002, OR 2.01, CI 1.29-3.15) were predictors of stroke in the setting of ACS. Event-free survival was higher in GA than GB (79.9% vs 70.5%, OR 1.58, p < 0.001, CI 1.36-1.83). Conclusion As expected, stroke in the setting of ACS is associated with poorer prognosis. Several characteristics of the pts may help to predict the occurrence of stroke during hospitalizations, therefore allowing an earlier identification and prompt treatment.