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In-hospital and long-term mortality for acute heart failure: analysis at the time of admission to the emergency department.

ABSTRACT: AIMS:Acute heart failure (AHF) leads to a drastic increase in mortality and rehospitalization. The aim of the study was to identify prognostic variables in a real-life population of AHF patients admitted to the emergency department with acute shortness of breath. METHODS AND RESULTS:We evaluated potential predictors of mortality in 728 consecutive patients admitted to the emergency department with AHF. Possible predictors of all-cause and cardiovascular (CV) mortality were investigated by Cox and Fine and Gray models at multivariable analysis. Among the 728 patients, 256 died during the entire follow-up, 142 of these due to CV cause. The 1 year mortality rate was 20%, with the highest risk of death during the index hospitalization (with 8% estimate in-hospital mortality at 30 days). A higher risk of events during the index hospitalization was more evident for the CV deaths, for which we found a cumulative 1 year incidence of 12% with a cumulative incidence in the first 30 days of hospitalization of about 5%. At multivariable analysis, age (P < 0.001), New York Heart Association (NYHA) class IV vs. I-II-III (P = 0.001), systolic blood pressure (P < 0.001), non-cardiac co-morbidities (?3 vs. 0, P = 0.05), oxygen saturation (P = 0.03), serum creatinine (P < 0.001), and left ventricular ejection fraction (LVEF) (40-49% vs. <40%, P = 0.004; ?50% vs. <40%, P = 0.003) were independent predictors of all-cause mortality during the entire follow-up. Age (P = 0.03), systolic blood pressure (P = 0.01), oxygen saturation (P = 0.03), serum creatinine (P = 0.02), and LVEF (40-49% vs. <40%, P = 0.03; ?50% vs. <40%, P = 0.004) were independent predictors of CV mortality during the entire follow-up. NYHA class IV vs. I-II-III (P < 0.001), serum creatinine (P = 0.01), and LVEF (40-49% vs. <40%, P = 0.02; ?50% vs. <40%, P < 0.001) remained independent predictors for in-hospital death, while only serum creatinine (P = 0.04), LVEF (40-49% vs. <40%: 0.32, P = 0.04; ?50% vs. <40%, P < 0.001), and NYHA class vs. I-II-III (P = 0.02) remained predictors for in-hospital CV mortality. CONCLUSIONS:In this real-life cohort of patients with AHF, the results showed a similar mortality rate comparing with other analysis and with the most important registries. Age, NYHA class IV, systolic blood pressure, creatinine levels, sodium levels, and ejection fraction were independent predictors of 1 year mortality, while LVEF <40% was the only predictor of both all-cause mortality and CV mortality.

SUBMITTER: Lombardi C

PROVIDER: S-EPMC7524058 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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In-hospital and long-term mortality for acute heart failure: analysis at the time of admission to the emergency department.

Lombardi Carlo C Peveri Giulia G Cani Dario D Latta Federica F Bonelli Andrea A Tomasoni Daniela D Sbolli Marco M Ravera Alice A Carubelli Valentina V Saccani Nicola N Specchia Claudia C Metra Marco M

ESC heart failure 20200626 5

<h4>Aims</h4>Acute heart failure (AHF) leads to a drastic increase in mortality and rehospitalization. The aim of the study was to identify prognostic variables in a real-life population of AHF patients admitted to the emergency department with acute shortness of breath.<h4>Methods and results</h4>We evaluated potential predictors of mortality in 728 consecutive patients admitted to the emergency department with AHF. Possible predictors of all-cause and cardiovascular (CV) mortality were investi ...[more]

PMID: 32588981

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Project description:ObjectiveTo predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.MethodsThis retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.ResultsA total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).ConclusionMachine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

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Dataset Information

In-hospital and long-term mortality for acute heart failure: analysis at the time of admission to the emergency department.

Publications

In-hospital and long-term mortality for acute heart failure: analysis at the time of admission to the emergency department.

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