Heart Failure Probability and Early Outcomes of Critically Ill Patients With COVID-19: A Prospective, Multicenter Study
Background: The relationship between cardiac functions and the fatal outcome of coronavirus disease 2019 (COVID-19) is still largely underestimated. We aim to explore the role of heart failure (HF) and NT-proBNP in the prognosis of critically ill patients with COVID-19 and construct an easy-to-use p...
Guardado en:
Autores principales: | , , , , , , , , , |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/fa38b788a32043dfaa41af6c5f6a2654 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
id |
oai:doaj.org-article:fa38b788a32043dfaa41af6c5f6a2654 |
---|---|
record_format |
dspace |
spelling |
oai:doaj.org-article:fa38b788a32043dfaa41af6c5f6a26542021-12-01T05:38:49ZHeart Failure Probability and Early Outcomes of Critically Ill Patients With COVID-19: A Prospective, Multicenter Study2297-055X10.3389/fcvm.2021.738814https://doaj.org/article/fa38b788a32043dfaa41af6c5f6a26542021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fcvm.2021.738814/fullhttps://doaj.org/toc/2297-055XBackground: The relationship between cardiac functions and the fatal outcome of coronavirus disease 2019 (COVID-19) is still largely underestimated. We aim to explore the role of heart failure (HF) and NT-proBNP in the prognosis of critically ill patients with COVID-19 and construct an easy-to-use predictive model using machine learning.Methods: In this multicenter and prospective study, a total of 1,050 patients with clinical suspicion of COVID-19 were consecutively screened. Finally, 402 laboratory-confirmed critically ill patients with COVID-19 were enrolled. A “triple cut-point” strategy of NT-proBNP was applied to assess the probability of HF. The primary outcome was 30-day all-cause in-hospital death. Prognostic risk factors were analyzed using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression, further formulating a nomogram to predict mortality.Results: Within a 30-day follow-up, 27.4% of the 402 patients died. The mortality rate of patients with HF likely was significantly higher than that of the patient with gray zone and HF unlikely (40.8% vs. 25 and 16.5%, respectively, P < 0.001). HF likely [Odds ratio (OR) 1.97, 95% CI 1.13–3.42], age (OR 1.04, 95% CI 1.02–1.06), lymphocyte (OR 0.36, 95% CI 0.19–0.68), albumin (OR 0.92, 95% CI 0.87–0.96), and total bilirubin (OR 1.02, 95% CI 1–1.04) were independently associated with the prognosis of critically ill patients with COVID-19. Moreover, a nomogram was developed by bootstrap validation, and C-index was 0.8 (95% CI 0.74–0.86).Conclusions: This study established a novel nomogram to predict the 30-day all-cause mortality of critically ill patients with COVID-19, highlighting the predominant role of the “triple cut-point” strategy of NT-proBNP, which could assist in risk stratification and improve clinical sequelae.Weibo GaoJiasai FanDi SunMengxi YangWei GuoLiyuan TaoJingang ZhengJihong ZhuTianbing WangJingyi RenFrontiers Media S.A.articleCOVID-19heart failureNT-ProBNPnomogramprognosisDiseases of the circulatory (Cardiovascular) systemRC666-701ENFrontiers in Cardiovascular Medicine, Vol 8 (2021) |
institution |
DOAJ |
collection |
DOAJ |
language |
EN |
topic |
COVID-19 heart failure NT-ProBNP nomogram prognosis Diseases of the circulatory (Cardiovascular) system RC666-701 |
spellingShingle |
COVID-19 heart failure NT-ProBNP nomogram prognosis Diseases of the circulatory (Cardiovascular) system RC666-701 Weibo Gao Jiasai Fan Di Sun Mengxi Yang Wei Guo Liyuan Tao Jingang Zheng Jihong Zhu Tianbing Wang Jingyi Ren Heart Failure Probability and Early Outcomes of Critically Ill Patients With COVID-19: A Prospective, Multicenter Study |
description |
Background: The relationship between cardiac functions and the fatal outcome of coronavirus disease 2019 (COVID-19) is still largely underestimated. We aim to explore the role of heart failure (HF) and NT-proBNP in the prognosis of critically ill patients with COVID-19 and construct an easy-to-use predictive model using machine learning.Methods: In this multicenter and prospective study, a total of 1,050 patients with clinical suspicion of COVID-19 were consecutively screened. Finally, 402 laboratory-confirmed critically ill patients with COVID-19 were enrolled. A “triple cut-point” strategy of NT-proBNP was applied to assess the probability of HF. The primary outcome was 30-day all-cause in-hospital death. Prognostic risk factors were analyzed using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression, further formulating a nomogram to predict mortality.Results: Within a 30-day follow-up, 27.4% of the 402 patients died. The mortality rate of patients with HF likely was significantly higher than that of the patient with gray zone and HF unlikely (40.8% vs. 25 and 16.5%, respectively, P < 0.001). HF likely [Odds ratio (OR) 1.97, 95% CI 1.13–3.42], age (OR 1.04, 95% CI 1.02–1.06), lymphocyte (OR 0.36, 95% CI 0.19–0.68), albumin (OR 0.92, 95% CI 0.87–0.96), and total bilirubin (OR 1.02, 95% CI 1–1.04) were independently associated with the prognosis of critically ill patients with COVID-19. Moreover, a nomogram was developed by bootstrap validation, and C-index was 0.8 (95% CI 0.74–0.86).Conclusions: This study established a novel nomogram to predict the 30-day all-cause mortality of critically ill patients with COVID-19, highlighting the predominant role of the “triple cut-point” strategy of NT-proBNP, which could assist in risk stratification and improve clinical sequelae. |
format |
article |
author |
Weibo Gao Jiasai Fan Di Sun Mengxi Yang Wei Guo Liyuan Tao Jingang Zheng Jihong Zhu Tianbing Wang Jingyi Ren |
author_facet |
Weibo Gao Jiasai Fan Di Sun Mengxi Yang Wei Guo Liyuan Tao Jingang Zheng Jihong Zhu Tianbing Wang Jingyi Ren |
author_sort |
Weibo Gao |
title |
Heart Failure Probability and Early Outcomes of Critically Ill Patients With COVID-19: A Prospective, Multicenter Study |
title_short |
Heart Failure Probability and Early Outcomes of Critically Ill Patients With COVID-19: A Prospective, Multicenter Study |
title_full |
Heart Failure Probability and Early Outcomes of Critically Ill Patients With COVID-19: A Prospective, Multicenter Study |
title_fullStr |
Heart Failure Probability and Early Outcomes of Critically Ill Patients With COVID-19: A Prospective, Multicenter Study |
title_full_unstemmed |
Heart Failure Probability and Early Outcomes of Critically Ill Patients With COVID-19: A Prospective, Multicenter Study |
title_sort |
heart failure probability and early outcomes of critically ill patients with covid-19: a prospective, multicenter study |
publisher |
Frontiers Media S.A. |
publishDate |
2021 |
url |
https://doaj.org/article/fa38b788a32043dfaa41af6c5f6a2654 |
work_keys_str_mv |
AT weibogao heartfailureprobabilityandearlyoutcomesofcriticallyillpatientswithcovid19aprospectivemulticenterstudy AT jiasaifan heartfailureprobabilityandearlyoutcomesofcriticallyillpatientswithcovid19aprospectivemulticenterstudy AT disun heartfailureprobabilityandearlyoutcomesofcriticallyillpatientswithcovid19aprospectivemulticenterstudy AT mengxiyang heartfailureprobabilityandearlyoutcomesofcriticallyillpatientswithcovid19aprospectivemulticenterstudy AT weiguo heartfailureprobabilityandearlyoutcomesofcriticallyillpatientswithcovid19aprospectivemulticenterstudy AT liyuantao heartfailureprobabilityandearlyoutcomesofcriticallyillpatientswithcovid19aprospectivemulticenterstudy AT jingangzheng heartfailureprobabilityandearlyoutcomesofcriticallyillpatientswithcovid19aprospectivemulticenterstudy AT jihongzhu heartfailureprobabilityandearlyoutcomesofcriticallyillpatientswithcovid19aprospectivemulticenterstudy AT tianbingwang heartfailureprobabilityandearlyoutcomesofcriticallyillpatientswithcovid19aprospectivemulticenterstudy AT jingyiren heartfailureprobabilityandearlyoutcomesofcriticallyillpatientswithcovid19aprospectivemulticenterstudy |
_version_ |
1718405517544521728 |