A registry-based algorithm using routine clinical characteristics accurately predicted EF ≥50% (C-statistic 0.78; 95% CI 0.77-0.78) and EF ≥40% (C-statistic 0.76; 95% CI 0.75-0.76).
Observational (n=52,688)
Yes
Can routine clinical characteristics accurately predict ejection fraction subphenotypes in heart failure patients?
Routine clinical characteristics can accurately predict HFrEF and HFpEF, but not HFmrEF, enabling EF subphenotyping in large databases lacking echocardiographic data.
Effect estimate: C-statistic 0.78 (95% CI 0.77-0.78)
AIMS: Left ventricular ejection fraction (EF) is required to categorize heart failure (HF) i.e. HF with preserved (HFpEF), mid-range (HFmrEF), and reduced (HFrEF) EF but is often not captured in population-based cohorts or non-HF registries. The aim was to create an algorithm that identifies EF subphenotypes for research purposes. METHODS AND RESULTS: We included 42 061 HF patients from the Swedish Heart Failure Registry. As primary analysis, we performed two logistic regression models including 22 variables to predict (i) EF≥ vs. <50% and (ii) EF≥ vs. <40%. In the secondary analysis, we performed a multivariable multinomial analysis with 22 variables to create a model for all three separate EF subphenotypes: HFrEF vs. HFmrEF vs. HFpEF. The models were validated in the database from the CHECK-HF study, a cross-sectional survey of 10 627 patients from the Netherlands. The C-statistic (discrimination) was 0.78 95% confidence interval (CI) 0.77-0.78 for EF ≥50% and 0.76 (95% CI 0.75-0.76) for EF ≥40%. Similar results were achieved for HFrEF and HFpEF in the multinomial model, but the C-statistic for HFmrEF was lower: 0.63 (95% CI 0.63-0.64). The external validation showed similar discriminative ability to the development cohort. CONCLUSIONS: Routine clinical characteristics could potentially be used to identify different EF subphenotypes in databases where EF is not readily available. Accuracy was good for the prediction of HFpEF and HFrEF but lower for HFmrEF. The proposed algorithm enables more effective research on HF in the big data setting.
Uijl et al. (Wed,) conducted a observational in Heart failure (n=52,688). Registry-based algorithm (logistic regression models including 22 variables) was evaluated on Prediction of EF ≥50% (C-statistic 0.78, 95% CI 0.77-0.78). A registry-based algorithm using routine clinical characteristics accurately predicted EF ≥50% (C-statistic 0.78; 95% CI 0.77-0.78) and EF ≥40% (C-statistic 0.76; 95% CI 0.75-0.76).
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