Key result
Adding a time course risk score to GWTG-HF improves in-hospital mortality prediction in AHF.
Why the study?
Patients with acute heart failure show various clinical courses during hospitalization, and time course predictors of in-hospital mortality were not established.
Does a time course risk score improve the prediction of in-hospital mortality in patients with acute heart failure compared to the GWTG-HF risk score alone?
Observational (n=1,035)
No
Does a time course risk score improve the prediction of in-hospital mortality in patients with acute heart failure compared to the GWTG-HF risk score alone?
Effect estimate: c-statistic 0.902 (95% CI 0.858-0.945)
Absolute Event Rate: 0.902% vs 0.806%
p-value: p=<0.001
A novel time course risk score incorporating clinical and laboratory variables during hospitalization significantly improves the prediction of in-hospital mortality in acute heart failure patients when added to the baseline GWTG-HF risk score.
Supports dynamic risk modeling in acute HF; hypothesis-generating and should not yet change practice.
BACKGROUND: Patients 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 RESULTS: We 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]). CONCLUSIONS: A 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.
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Yagyu et al. (2017) conducted an observational in Acute heart failure (n=1,035). Time course risk score addition to GWTG-HF risk score vs. GWTG-HF risk score alone was evaluated on Discriminative ability (c-statistic) for in-hospital mortality (validation cohort) (c-statistic 0.902, 95% CI 0.858-0.945, p=<0.001). The addition of a time course risk score to the GWTG-HF risk score significantly improved the discriminative ability for predicting in-hospital mortality in patients with acute heart failure.
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