Penalized Model‐Based Unsupervised Phenomapping Unravels Distinctive HFrEF Phenotypes With Improved Outcomes Discrimination From Sacubitril/Valsartan Treatment Independent of MAGGIC Score

Author:

Sung Kuo‐Tzu123,Chang Hung‐Yu4,Hsu Nai‐Wei5ORCID,Huang Wen‐Hung16,Lin Yueh‐Hung123ORCID,Yun Chun‐Ho67ORCID,Hsiao Chih‐Chung12ORCID,Hsu Chien‐Yi8,Tsai Shin‐Yi9ORCID,Chen Ying‐Ju10,Tsai Cheng‐Ting16ORCID,Su Cheng‐Huang12,Hung Ta‐Chuan16,Hou Charles Jia‐Yin126ORCID,Yeh Hung‐I12ORCID,Hung Chung‐Lieh11011ORCID

Affiliation:

1. Division of Cardiology, Department of Internal Medicine MacKay Memorial Hospital Taipei Taiwan

2. Department of Medicine MacKay Medical College New Taipei Taiwan

3. Institute of Clinical Medicine National Yang Ming Chiao Tung University Taipei Taiwan

4. Heart Center Cheng Hsin General Hospital Taipei Taiwan

5. Department of Medical Education Veterans General Hospital Taipei Taiwan

6. Mackay Junior College of Medicine Nursing and Management New Taipei City Taiwan

7. Division of Radiology MacKay Memorial Hospital Taipei Taiwan

8. Division of Cardiology and Cardiovascular Research Center, Department of Internal Medicine Taipei Medical University Hospital Taipei Taiwan

9. Johns Hopkins University Bloomberg School of Public Health Baltimore MD

10. Department of Telehealth MacKay Memorial Hospital Taipei Taiwan

11. Institute of Biomedical Sciences MacKay Medical College New Taipei Taiwan

Abstract

Background The angiotensin receptor–neprilysin inhibitor (LCZ696) has emerged as a promising pharmacological intervention against renin–angiotensin system inhibitor in reduced ejection fraction heart failure (HFrEF). Whether the therapeutic benefits may vary among heterogeneous HFrEF subgroups remains unknown. Methods and Results This study comprised a pooled 2‐center analysis including 1103 patients with symptomatic HFrEF with LCZ696 use and another 1103 independent HFrEF control cohort (with renin–angiotensin system inhibitor use) matched for age, sex, left ventricular ejection fraction, and comorbidity conditions. Three main distinct phenogroup clusterings were identified from unsupervised machine learning using 29 clinical variables: phenogroup 1 (youngest, relatively lower diabetes prevalence, highest glomerular filtration rate with largest left ventricular size and left ventricular wall stress); phenogroup 2 (oldest, lean, highest diabetes and vascular diseases prevalence, lowest highest glomerular filtration rate with smallest left ventricular size and mass), and phenogroup 3 (lowest clinical comorbidity with largest left ventricular mass and highest hypertrophy prevalence). During the median 1.74‐year follow‐up, phenogroup assignment provided improved prognostic discrimination beyond Meta‐Analysis Global Group in Chronic Heart Failure risk score risk score (all net reclassification index P <0.05) with overall good calibrations. While phenogroup 1 showed overall best clinical outcomes, phenogroup 2 demonstrated highest cardiovascular death and worst renal end point, with phenogroup 3 having the highest all‐cause death rate and HF hospitalization among groups, respectively. These findings were broadly consistent when compared with the renin–angiotensin system inhibitor control as reference group. Conclusions Phenomapping provided novel insights on unique characteristics and cardiac features among patients with HFrEF with sacubitril/valsartan treatment. These findings further showed potentiality in identifying potential sacubitril/valsartan responders and nonresponders with improved outcome discrimination among patients with HFrEF beyond clinical scoring.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cardiology and Cardiovascular Medicine

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