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

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 rema...

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Autores principales: Sung, Kuo‐Tzu, Chang, Hung‐Yu, Hsu, Nai‐Wei, Huang, Wen‐Hung, Lin, Yueh‐Hung, Yun, Chun‐Ho, Hsiao, Chih‐Chung, Hsu, Chien‐Yi, Tsai, Shin‐Yi, Chen, Ying‐Ju, Tsai, Cheng‐Ting, Su, Cheng‐Huang, Hung, Ta‐Chuan, Hou, Charles Jia‐Yin, Yeh, Hung‐I, Hung, Chung‐Lieh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10547272/
https://www.ncbi.nlm.nih.gov/pubmed/37681571
http://dx.doi.org/10.1161/JAHA.122.028860
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author Sung, Kuo‐Tzu
Chang, Hung‐Yu
Hsu, Nai‐Wei
Huang, Wen‐Hung
Lin, Yueh‐Hung
Yun, Chun‐Ho
Hsiao, Chih‐Chung
Hsu, Chien‐Yi
Tsai, Shin‐Yi
Chen, Ying‐Ju
Tsai, Cheng‐Ting
Su, Cheng‐Huang
Hung, Ta‐Chuan
Hou, Charles Jia‐Yin
Yeh, Hung‐I
Hung, Chung‐Lieh
author_facet Sung, Kuo‐Tzu
Chang, Hung‐Yu
Hsu, Nai‐Wei
Huang, Wen‐Hung
Lin, Yueh‐Hung
Yun, Chun‐Ho
Hsiao, Chih‐Chung
Hsu, Chien‐Yi
Tsai, Shin‐Yi
Chen, Ying‐Ju
Tsai, Cheng‐Ting
Su, Cheng‐Huang
Hung, Ta‐Chuan
Hou, Charles Jia‐Yin
Yeh, Hung‐I
Hung, Chung‐Lieh
author_sort Sung, Kuo‐Tzu
collection PubMed
description 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.
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spelling pubmed-105472722023-10-04 Penalized Model‐Based Unsupervised Phenomapping Unravels Distinctive HFrEF Phenotypes With Improved Outcomes Discrimination From Sacubitril/Valsartan Treatment Independent of MAGGIC Score Sung, Kuo‐Tzu Chang, Hung‐Yu Hsu, Nai‐Wei Huang, Wen‐Hung Lin, Yueh‐Hung Yun, Chun‐Ho Hsiao, Chih‐Chung Hsu, Chien‐Yi Tsai, Shin‐Yi Chen, Ying‐Ju Tsai, Cheng‐Ting Su, Cheng‐Huang Hung, Ta‐Chuan Hou, Charles Jia‐Yin Yeh, Hung‐I Hung, Chung‐Lieh J Am Heart Assoc Original Research 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. John Wiley and Sons Inc. 2023-09-08 /pmc/articles/PMC10547272/ /pubmed/37681571 http://dx.doi.org/10.1161/JAHA.122.028860 Text en © 2023 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Research
Sung, Kuo‐Tzu
Chang, Hung‐Yu
Hsu, Nai‐Wei
Huang, Wen‐Hung
Lin, Yueh‐Hung
Yun, Chun‐Ho
Hsiao, Chih‐Chung
Hsu, Chien‐Yi
Tsai, Shin‐Yi
Chen, Ying‐Ju
Tsai, Cheng‐Ting
Su, Cheng‐Huang
Hung, Ta‐Chuan
Hou, Charles Jia‐Yin
Yeh, Hung‐I
Hung, Chung‐Lieh
Penalized Model‐Based Unsupervised Phenomapping Unravels Distinctive HFrEF Phenotypes With Improved Outcomes Discrimination From Sacubitril/Valsartan Treatment Independent of MAGGIC Score
title Penalized Model‐Based Unsupervised Phenomapping Unravels Distinctive HFrEF Phenotypes With Improved Outcomes Discrimination From Sacubitril/Valsartan Treatment Independent of MAGGIC Score
title_full Penalized Model‐Based Unsupervised Phenomapping Unravels Distinctive HFrEF Phenotypes With Improved Outcomes Discrimination From Sacubitril/Valsartan Treatment Independent of MAGGIC Score
title_fullStr Penalized Model‐Based Unsupervised Phenomapping Unravels Distinctive HFrEF Phenotypes With Improved Outcomes Discrimination From Sacubitril/Valsartan Treatment Independent of MAGGIC Score
title_full_unstemmed Penalized Model‐Based Unsupervised Phenomapping Unravels Distinctive HFrEF Phenotypes With Improved Outcomes Discrimination From Sacubitril/Valsartan Treatment Independent of MAGGIC Score
title_short Penalized Model‐Based Unsupervised Phenomapping Unravels Distinctive HFrEF Phenotypes With Improved Outcomes Discrimination From Sacubitril/Valsartan Treatment Independent of MAGGIC Score
title_sort penalized model‐based unsupervised phenomapping unravels distinctive hfref phenotypes with improved outcomes discrimination from sacubitril/valsartan treatment independent of maggic score
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10547272/
https://www.ncbi.nlm.nih.gov/pubmed/37681571
http://dx.doi.org/10.1161/JAHA.122.028860
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