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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...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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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. |
format | Online Article Text |
id | pubmed-10547272 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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