Cargando…

Incorporating inflammatory biomarkers into a prognostic risk score in patients with non-ischemic heart failure: a machine learning approach

OBJECTIVES: Inflammation is involved in the mechanisms of non-ischemic heart failure (NIHF). We aimed to investigate the prognostic value of 21 inflammatory biomarkers and construct a biomarker risk score to improve risk prediction for patients with NIHF. METHODS: Patients diagnosed with NIHF withou...

Descripción completa

Detalles Bibliográficos
Autores principales: Feng, Jiayu, Zhao, Xuemei, Huang, Boping, Huang, Liyan, Wu, Yihang, Wang, Jing, Guan, Jingyuan, Li, Xinqing, Zhang, Yuhui, Zhang, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463734/
https://www.ncbi.nlm.nih.gov/pubmed/37649485
http://dx.doi.org/10.3389/fimmu.2023.1228018
_version_ 1785098301641588736
author Feng, Jiayu
Zhao, Xuemei
Huang, Boping
Huang, Liyan
Wu, Yihang
Wang, Jing
Guan, Jingyuan
Li, Xinqing
Zhang, Yuhui
Zhang, Jian
author_facet Feng, Jiayu
Zhao, Xuemei
Huang, Boping
Huang, Liyan
Wu, Yihang
Wang, Jing
Guan, Jingyuan
Li, Xinqing
Zhang, Yuhui
Zhang, Jian
author_sort Feng, Jiayu
collection PubMed
description OBJECTIVES: Inflammation is involved in the mechanisms of non-ischemic heart failure (NIHF). We aimed to investigate the prognostic value of 21 inflammatory biomarkers and construct a biomarker risk score to improve risk prediction for patients with NIHF. METHODS: Patients diagnosed with NIHF without infection during hospitalization were included. The primary outcome was defined as all-cause mortality and heart transplantations. We used elastic net Cox regression with cross-validation to select inflammatory biomarkers and construct the best biomarker risk score model. Discrimination, calibration, and reclassification were evaluated to assess the predictive value of the biomarker risk score. RESULTS: Of 1,250 patients included (median age, 53 years, 31.9% women), 436 patients (34.9%) experienced the primary outcome during a median of 2.8 years of follow-up. The final biomarker risk score included high-sensitivity C-reactive protein-to-albumin ratio (CAR) and red blood cell distribution width-standard deviation (RDW-SD), both of which were 100% selected in 1,000 times cross-validation folds. Incorporating the biomarker risk score into the best basic model improved the discrimination (ΔC-index = 0.012, 95% CI 0.003–0.018) and reclassification (IDI, 2.3%, 95% CI 0.7%–4.9%; NRI, 17.3% 95% CI 6.4%–32.3%) in risk identification. In the cross-validation sets, the mean time-dependent AUC ranged from 0.670 to 0.724 for the biomarker risk score and 0.705 to 0.804 for the basic model with a biomarker risk score, from 1 to 8 years. In multivariable Cox regression, the biomarker risk score was independently associated with the outcome in patients with NIHF (HR 1.76, 95% CI 1.49–2.08, p < 0.001, per 1 score increase). CONCLUSIONS: An inflammatory biomarker-derived risk score significantly improved prognosis prediction and risk stratification, providing potential individualized therapeutic targets for NIHF patients.
format Online
Article
Text
id pubmed-10463734
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-104637342023-08-30 Incorporating inflammatory biomarkers into a prognostic risk score in patients with non-ischemic heart failure: a machine learning approach Feng, Jiayu Zhao, Xuemei Huang, Boping Huang, Liyan Wu, Yihang Wang, Jing Guan, Jingyuan Li, Xinqing Zhang, Yuhui Zhang, Jian Front Immunol Immunology OBJECTIVES: Inflammation is involved in the mechanisms of non-ischemic heart failure (NIHF). We aimed to investigate the prognostic value of 21 inflammatory biomarkers and construct a biomarker risk score to improve risk prediction for patients with NIHF. METHODS: Patients diagnosed with NIHF without infection during hospitalization were included. The primary outcome was defined as all-cause mortality and heart transplantations. We used elastic net Cox regression with cross-validation to select inflammatory biomarkers and construct the best biomarker risk score model. Discrimination, calibration, and reclassification were evaluated to assess the predictive value of the biomarker risk score. RESULTS: Of 1,250 patients included (median age, 53 years, 31.9% women), 436 patients (34.9%) experienced the primary outcome during a median of 2.8 years of follow-up. The final biomarker risk score included high-sensitivity C-reactive protein-to-albumin ratio (CAR) and red blood cell distribution width-standard deviation (RDW-SD), both of which were 100% selected in 1,000 times cross-validation folds. Incorporating the biomarker risk score into the best basic model improved the discrimination (ΔC-index = 0.012, 95% CI 0.003–0.018) and reclassification (IDI, 2.3%, 95% CI 0.7%–4.9%; NRI, 17.3% 95% CI 6.4%–32.3%) in risk identification. In the cross-validation sets, the mean time-dependent AUC ranged from 0.670 to 0.724 for the biomarker risk score and 0.705 to 0.804 for the basic model with a biomarker risk score, from 1 to 8 years. In multivariable Cox regression, the biomarker risk score was independently associated with the outcome in patients with NIHF (HR 1.76, 95% CI 1.49–2.08, p < 0.001, per 1 score increase). CONCLUSIONS: An inflammatory biomarker-derived risk score significantly improved prognosis prediction and risk stratification, providing potential individualized therapeutic targets for NIHF patients. Frontiers Media S.A. 2023-08-15 /pmc/articles/PMC10463734/ /pubmed/37649485 http://dx.doi.org/10.3389/fimmu.2023.1228018 Text en Copyright © 2023 Feng, Zhao, Huang, Huang, Wu, Wang, Guan, Li, Zhang and Zhang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Feng, Jiayu
Zhao, Xuemei
Huang, Boping
Huang, Liyan
Wu, Yihang
Wang, Jing
Guan, Jingyuan
Li, Xinqing
Zhang, Yuhui
Zhang, Jian
Incorporating inflammatory biomarkers into a prognostic risk score in patients with non-ischemic heart failure: a machine learning approach
title Incorporating inflammatory biomarkers into a prognostic risk score in patients with non-ischemic heart failure: a machine learning approach
title_full Incorporating inflammatory biomarkers into a prognostic risk score in patients with non-ischemic heart failure: a machine learning approach
title_fullStr Incorporating inflammatory biomarkers into a prognostic risk score in patients with non-ischemic heart failure: a machine learning approach
title_full_unstemmed Incorporating inflammatory biomarkers into a prognostic risk score in patients with non-ischemic heart failure: a machine learning approach
title_short Incorporating inflammatory biomarkers into a prognostic risk score in patients with non-ischemic heart failure: a machine learning approach
title_sort incorporating inflammatory biomarkers into a prognostic risk score in patients with non-ischemic heart failure: a machine learning approach
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463734/
https://www.ncbi.nlm.nih.gov/pubmed/37649485
http://dx.doi.org/10.3389/fimmu.2023.1228018
work_keys_str_mv AT fengjiayu incorporatinginflammatorybiomarkersintoaprognosticriskscoreinpatientswithnonischemicheartfailureamachinelearningapproach
AT zhaoxuemei incorporatinginflammatorybiomarkersintoaprognosticriskscoreinpatientswithnonischemicheartfailureamachinelearningapproach
AT huangboping incorporatinginflammatorybiomarkersintoaprognosticriskscoreinpatientswithnonischemicheartfailureamachinelearningapproach
AT huangliyan incorporatinginflammatorybiomarkersintoaprognosticriskscoreinpatientswithnonischemicheartfailureamachinelearningapproach
AT wuyihang incorporatinginflammatorybiomarkersintoaprognosticriskscoreinpatientswithnonischemicheartfailureamachinelearningapproach
AT wangjing incorporatinginflammatorybiomarkersintoaprognosticriskscoreinpatientswithnonischemicheartfailureamachinelearningapproach
AT guanjingyuan incorporatinginflammatorybiomarkersintoaprognosticriskscoreinpatientswithnonischemicheartfailureamachinelearningapproach
AT lixinqing incorporatinginflammatorybiomarkersintoaprognosticriskscoreinpatientswithnonischemicheartfailureamachinelearningapproach
AT zhangyuhui incorporatinginflammatorybiomarkersintoaprognosticriskscoreinpatientswithnonischemicheartfailureamachinelearningapproach
AT zhangjian incorporatinginflammatorybiomarkersintoaprognosticriskscoreinpatientswithnonischemicheartfailureamachinelearningapproach