Abstract
BackgroundPatients with acute heart failure (AHF) show various clinical courses during hospitalization. We aimed to identify time course predictors of in-hospital mortality and to establish a sequentially assessable risk model.Methods and resultsWe enrolled 1,035 consecutive AHF patients into derivation (n = 597) and validation (n = 438) cohorts. For risk assessments at admission, we utilized Get With the Guidelines-Heart Failure (GWTG-HF) risk scores. We examined significant predictors of in-hospital mortality from 11 variables obtained during hospitalization and developed a risk stratification model using multiple logistic regression analysis. Across both cohorts, 86 patients (8.3%) died during hospitalization. Using backward stepwise selection, we identified five time-course predictors: catecholamine administration, minimum platelet concentration, maximum blood urea nitrogen, total bilirubin, and C-reactive protein levels; and established a time course risk score that could sequentially assess a patient's risk status. The addition of a time course risk score improved the discriminative ability of the GWTG-HF risk score (c-statistics in derivation and validation cohorts: 0.776 to 0.888 [p = 0.002] and 0.806 to 0.902 [p<0.001], respectively). A calibration plot revealed a good relationship between observed and predicted in-hospital mortalities in both cohorts (Hosmer-Lemeshow chi-square statistics: 6.049 [p = 0.642] and 5.993 [p = 0.648], respectively). In each group of initial low-intermediate risk (GWTG-HF risk score <47) and initial high risk (GWTG-HF risk score ≥47), in-hospital mortality was about 6- to 9-fold higher in the high time course risk score group than in the low-intermediate time course risk score group (initial low-intermediate risk group: 20.3% versus 2.2% [p<0.001], initial high risk group: 57.6% versus 8.5% [p<0.001]).ConclusionsA time course assessment related to in-hospital mortality during the hospitalization of AHF patients can clearly categorize a patient's on-going status, and may assist patients and clinicians in deciding treatment options.
Highlights
Acute heart failure (AHF) is a common and occasionally life-threatening disease
For risk assessments at admission, we utilized Get With the Guidelines-Heart Failure (GWTG-HF) risk scores
We examined significant predictors of in-hospital mortality from 11 variables obtained during hospitalization and developed a risk stratification model using multiple logistic regression analysis
Summary
Acute heart failure (AHF) is a common and occasionally life-threatening disease. In aging societies, the number of patients diagnosed with AHF has been growing, with AHF expected to become a serious public health problem and heavy social burden [1,2,3]. Several models of risk stratification for in-hospital mortality of AHF patients have been described [4,5,6,7,8,9] Most of these are well-validated and easy to use; their predictive abilities are not strong enough to be relied on in clinical settings, so that the estimation of a patient’s prognosis is still mainly based on a clinician’s subjective view [9]. One reason for their modest abilities in the prediction of in-hospital mortality may be that such risk stratification models are based solely on the initial assessments performed at admission. We aimed to identify time course predictors of in-hospital mortality and to establish a sequentially assessable risk model
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