MRA for phenotype 2 (wHR 0.40; 95% CI 0.21-0.75) and ACEi/ARB or statins for phenotype 3 significantly reduced all-cause death and heart failure hospitalization in specific HFpEF phenotypes.
Observational (n=1,100)
Yes
Do specific medications (ACEi/ARB, beta blockers, MRA, statins) reduce the composite of all-cause death and heart failure hospitalisation in specific machine learning-derived phenotypes of HFpEF?
Machine learning-based clustering identifies specific HFpEF phenotypes that may benefit from targeted medical therapies such as MRAs, ACEi/ARBs, and statins.
Effect estimate: wHR 0.40 (95% CI 0.21-0.75)
p-value: p=0.005
OBJECTIVE: Our previously established machine learning-based clustering model classified heart failure with preserved ejection fraction (HFpEF) into four distinct phenotypes. Given the heterogeneous pathophysiology of HFpEF, specific medications may have favourable effects in specific phenotypes of HFpEF. We aimed to assess effectiveness of medications on clinical outcomes of the four phenotypes using a real-world HFpEF registry dataset. METHODS: This study is a posthoc analysis of the PURSUIT-HFpEF registry, a prospective, multicentre, observational study. We evaluated the clinical effectiveness of the following four types of postdischarge medication in the four different phenotypes: angiotensin-converting enzyme inhibitors (ACEi) or angiotensin-receptor blockers (ARB), beta blockers, mineralocorticoid-receptor antagonists (MRA) and statins. The primary endpoint of this study was a composite of all-cause death and heart failure hospitalisation. RESULTS: Of 1231 patients, 1100 (83 (IQR 77, 87) years, 604 females) were eligible for analysis. Median follow-up duration was 734 (398, 1108) days. The primary endpoint occurred in 528 patients (48.0%). Cox proportional hazard models with inverse-probability-of-treatment weighting showed the following significant effectiveness of medication on the primary endpoint: MRA for phenotype 2 (weighted HR (wHR) 0.40, 95% CI 0.21 to 0.75, p=0.005); ACEi or ARB for phenotype 3 (wHR 0.66 0.48 to 0.92, p=0.014) and statin therapy for phenotype 3 (wHR 0.43 (0.21 to 0.88), p=0.020). No other medications had significant treatment effects in the four phenotypes. CONCLUSIONS: Machine learning-based clustering may have the potential to identify populations in which specific medications may be effective. This study suggests the effectiveness of MRA, ACEi or ARB and statin for specific phenotypes of HFpEF. TRIAL REGISTRATION NUMBER: UMIN000021831.
Sotomi et al. (Thu,) conducted a observational in Heart failure with preserved ejection fraction (HFpEF) (n=1,100). ACEi or ARB, beta blockers, MRA, and statins vs. Non-users of the respective medications was evaluated on Composite of all-cause death and heart failure hospitalisation (wHR 0.40, 95% CI 0.21-0.75, p=0.005). MRA for phenotype 2 (wHR 0.40; 95% CI 0.21-0.75) and ACEi/ARB or statins for phenotype 3 significantly reduced all-cause death and heart failure hospitalization in specific HFpEF phenotypes.