Abstract
BackgroundPatients with heart failure (HF) with recovered ejection fraction (HFrecEF) are a recently identified cohort that are phenotypically and biologically different from HFrEF and HFpEF patients. Whether there are unique phenotypes among HFrecEF patients is not known.MethodsWe studied all patients at a large medical center, who had an improvement in LVEF from ≤ 35% to ≥ 50% (LVrecEF) between January 1, 2005 and December 31, 2013. We identified a set of 11 clinical variables and then performed unsupervised clustering analyses to identify unique clinical phenotypes among patients with LVrecEF, followed by a Kaplan-Meier analysis to identify differences in survival and the proportion of LVrecEF patients who maintained an LVEF ≥ 50% during the study period.ResultsWe identified 889 patients with LVrecEF who clustered into 7 unique phenotypes ranging in size from 37 to 420 patients. Kaplan-Meier analysis demonstrated significant differences in mortality across clusters (logrank p<0.0001), with survival ranging from 14% to 87% at 1000 days, as well as significant differences in the proportion of LVrecEF patients who maintained an LVEF ≥ 50%.ConclusionThere is significant clinical heterogeneity among patients with LVrecEF. Clinical outcomes are distinct across phenotype clusters as defined by clinical cardiac characteristics and co-morbidities. Clustering algorithms may identify patients who are at high risk for recurrent HF, and thus be useful for guiding treatment strategies for patients with LVrecEF.
Highlights
Heart failure (HF) with a recovered left ventricular ejection fraction (HFrecEF) refers to a recently identified sub-group of HF patients with a reduced ejection fraction (HFrEF) whose left ventricular (LV) ejection fraction (LVEF) improves in response to implementation of guideline directed medical therapy (GDMT) or device therapy [1]
Recovery of LV function is associated with improved clinical outcomes in HFrecEF patients when compared to HFrEF, there is a growing body of evidence suggesting that even among patients who experience a complete normalization of LV structure and function after implementation of GDMT, a significant proportion will develop recurrent LV dysfunction accompanied by recurrent HF events [3, 4]
We prospectively identified a set of 11 clinical variables that were previously shown to predict clinical outcomes in patients with HFrEF or HFrecEF: age, weight, LVEF, history of atrial fibrillation, history of diabetes, ischemic heart disease, cardiac resynchronization therapy (CRT), moderate to severe mitral regurgitation, QRS 120ms, and time to LVEF recovery [1, 5, 10,11,12,13,14,15,16,17]
Summary
Heart failure (HF) with a recovered left ventricular ejection fraction (HFrecEF) refers to a recently identified sub-group of HF patients with a reduced ejection fraction (HFrEF) whose left ventricular (LV) ejection fraction (LVEF) improves in response to implementation of guideline directed medical therapy (GDMT) or device therapy [1]. Given the complexity and heterogeneity of HFrecEF patients, we sought to use unsupervised machine learning to identify unique subsets (clusters) of patients with recovered LVEF (LVrecEF), with the goal of identifying low- and high-risk subsets of LVrecEF patients, analogous to the approach that was taken to identify clinical phenotypes in patients with HFpEF [7]. Patients with heart failure (HF) with recovered ejection fraction (HFrecEF) are a recently identified cohort that are phenotypically and biologically different from HFrEF and HFpEF patients. Whether there are unique phenotypes among HFrecEF patients is not known
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