Cargando…

Nomogram for prediction of fatal outcome in patients with severe COVID-19: a multicenter study

BACKGROUND: To develop an effective model of predicting fatal outcomes in the severe coronavirus disease 2019 (COVID-19) patients. METHODS: Between February 20, 2020 and April 4, 2020, consecutive confirmed 2541 COVID-19 patients from three designated hospitals were enrolled in this study. All patie...

Descripción completa

Detalles Bibliográficos
Autores principales: Yang, Yun, Zhu, Xiao-Fei, Huang, Jian, Chen, Cui, Zheng, Yang, He, Wei, Zhao, Ling-Hao, Gao, Qian, Huang, Xuan-Xuan, Fu, Li-Juan, Zhang, Yu, Chang, Yan-Qin, Zhang, Huo-Jun, Lu, Zhi-Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7967101/
https://www.ncbi.nlm.nih.gov/pubmed/33731184
http://dx.doi.org/10.1186/s40779-021-00315-6
_version_ 1783665801005367296
author Yang, Yun
Zhu, Xiao-Fei
Huang, Jian
Chen, Cui
Zheng, Yang
He, Wei
Zhao, Ling-Hao
Gao, Qian
Huang, Xuan-Xuan
Fu, Li-Juan
Zhang, Yu
Chang, Yan-Qin
Zhang, Huo-Jun
Lu, Zhi-Jie
author_facet Yang, Yun
Zhu, Xiao-Fei
Huang, Jian
Chen, Cui
Zheng, Yang
He, Wei
Zhao, Ling-Hao
Gao, Qian
Huang, Xuan-Xuan
Fu, Li-Juan
Zhang, Yu
Chang, Yan-Qin
Zhang, Huo-Jun
Lu, Zhi-Jie
author_sort Yang, Yun
collection PubMed
description BACKGROUND: To develop an effective model of predicting fatal outcomes in the severe coronavirus disease 2019 (COVID-19) patients. METHODS: Between February 20, 2020 and April 4, 2020, consecutive confirmed 2541 COVID-19 patients from three designated hospitals were enrolled in this study. All patients received chest computed tomography (CT) and serological examinations at admission. Laboratory tests included routine blood tests, liver function, renal function, coagulation profile, C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and arterial blood gas. The SaO(2) was measured using pulse oxygen saturation in room air at resting status. Independent high-risk factors associated with death were analyzed using Cox proportional hazard model. A prognostic nomogram was constructed to predict the survival of severe COVID-19 patients. RESULTS: There were 124 severe patients in the training cohort, and there were 71 and 76 severe patients in the two independent validation cohorts, respectively. Multivariate Cox analysis indicated that age ≥ 70 years (HR = 1.184, 95% CI 1.061–1.321), panting (breathing rate ≥ 30/min) (HR = 3.300, 95% CI 2.509–6.286), lymphocyte count < 1.0 × 10(9)/L (HR = 2.283, 95% CI 1.779–3.267), and interleukin-6 (IL-6) >  10 pg/ml (HR = 3.029, 95% CI 1.567–7.116) were independent high-risk factors associated with fatal outcome. We developed the nomogram for identifying survival of severe COVID-19 patients in the training cohort (AUC = 0.900, 95% CI 0.841–0.960, sensitivity 95.5%, specificity 77.5%); in validation cohort 1 (AUC = 0.811, 95% CI 0.763–0.961, sensitivity 77.3%, specificity 73.5%); in validation cohort 2 (AUC = 0.862, 95% CI 0.698–0.924, sensitivity 92.9%, specificity 64.5%). The calibration curve for probability of death indicated a good consistence between prediction by the nomogram and the actual observation. The prognosis of severe COVID-19 patients with high levels of IL-6 receiving tocilizumab were better than that of those patients without tocilizumab both in the training and validation cohorts, but without difference (P = 0.105 for training cohort, P = 0.133 for validation cohort 1, and P = 0.210 for validation cohort 2). CONCLUSIONS: This nomogram could help clinicians to identify severe patients who have high risk of death, and to develop more appropriate treatment strategies to reduce the mortality of severe patients. Tocilizumab may improve the prognosis of severe COVID-19 patients with high levels of IL-6. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40779-021-00315-6.
format Online
Article
Text
id pubmed-7967101
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-79671012021-03-17 Nomogram for prediction of fatal outcome in patients with severe COVID-19: a multicenter study Yang, Yun Zhu, Xiao-Fei Huang, Jian Chen, Cui Zheng, Yang He, Wei Zhao, Ling-Hao Gao, Qian Huang, Xuan-Xuan Fu, Li-Juan Zhang, Yu Chang, Yan-Qin Zhang, Huo-Jun Lu, Zhi-Jie Mil Med Res Research BACKGROUND: To develop an effective model of predicting fatal outcomes in the severe coronavirus disease 2019 (COVID-19) patients. METHODS: Between February 20, 2020 and April 4, 2020, consecutive confirmed 2541 COVID-19 patients from three designated hospitals were enrolled in this study. All patients received chest computed tomography (CT) and serological examinations at admission. Laboratory tests included routine blood tests, liver function, renal function, coagulation profile, C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and arterial blood gas. The SaO(2) was measured using pulse oxygen saturation in room air at resting status. Independent high-risk factors associated with death were analyzed using Cox proportional hazard model. A prognostic nomogram was constructed to predict the survival of severe COVID-19 patients. RESULTS: There were 124 severe patients in the training cohort, and there were 71 and 76 severe patients in the two independent validation cohorts, respectively. Multivariate Cox analysis indicated that age ≥ 70 years (HR = 1.184, 95% CI 1.061–1.321), panting (breathing rate ≥ 30/min) (HR = 3.300, 95% CI 2.509–6.286), lymphocyte count < 1.0 × 10(9)/L (HR = 2.283, 95% CI 1.779–3.267), and interleukin-6 (IL-6) >  10 pg/ml (HR = 3.029, 95% CI 1.567–7.116) were independent high-risk factors associated with fatal outcome. We developed the nomogram for identifying survival of severe COVID-19 patients in the training cohort (AUC = 0.900, 95% CI 0.841–0.960, sensitivity 95.5%, specificity 77.5%); in validation cohort 1 (AUC = 0.811, 95% CI 0.763–0.961, sensitivity 77.3%, specificity 73.5%); in validation cohort 2 (AUC = 0.862, 95% CI 0.698–0.924, sensitivity 92.9%, specificity 64.5%). The calibration curve for probability of death indicated a good consistence between prediction by the nomogram and the actual observation. The prognosis of severe COVID-19 patients with high levels of IL-6 receiving tocilizumab were better than that of those patients without tocilizumab both in the training and validation cohorts, but without difference (P = 0.105 for training cohort, P = 0.133 for validation cohort 1, and P = 0.210 for validation cohort 2). CONCLUSIONS: This nomogram could help clinicians to identify severe patients who have high risk of death, and to develop more appropriate treatment strategies to reduce the mortality of severe patients. Tocilizumab may improve the prognosis of severe COVID-19 patients with high levels of IL-6. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40779-021-00315-6. BioMed Central 2021-03-17 /pmc/articles/PMC7967101/ /pubmed/33731184 http://dx.doi.org/10.1186/s40779-021-00315-6 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Yang, Yun
Zhu, Xiao-Fei
Huang, Jian
Chen, Cui
Zheng, Yang
He, Wei
Zhao, Ling-Hao
Gao, Qian
Huang, Xuan-Xuan
Fu, Li-Juan
Zhang, Yu
Chang, Yan-Qin
Zhang, Huo-Jun
Lu, Zhi-Jie
Nomogram for prediction of fatal outcome in patients with severe COVID-19: a multicenter study
title Nomogram for prediction of fatal outcome in patients with severe COVID-19: a multicenter study
title_full Nomogram for prediction of fatal outcome in patients with severe COVID-19: a multicenter study
title_fullStr Nomogram for prediction of fatal outcome in patients with severe COVID-19: a multicenter study
title_full_unstemmed Nomogram for prediction of fatal outcome in patients with severe COVID-19: a multicenter study
title_short Nomogram for prediction of fatal outcome in patients with severe COVID-19: a multicenter study
title_sort nomogram for prediction of fatal outcome in patients with severe covid-19: a multicenter study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7967101/
https://www.ncbi.nlm.nih.gov/pubmed/33731184
http://dx.doi.org/10.1186/s40779-021-00315-6
work_keys_str_mv AT yangyun nomogramforpredictionoffataloutcomeinpatientswithseverecovid19amulticenterstudy
AT zhuxiaofei nomogramforpredictionoffataloutcomeinpatientswithseverecovid19amulticenterstudy
AT huangjian nomogramforpredictionoffataloutcomeinpatientswithseverecovid19amulticenterstudy
AT chencui nomogramforpredictionoffataloutcomeinpatientswithseverecovid19amulticenterstudy
AT zhengyang nomogramforpredictionoffataloutcomeinpatientswithseverecovid19amulticenterstudy
AT hewei nomogramforpredictionoffataloutcomeinpatientswithseverecovid19amulticenterstudy
AT zhaolinghao nomogramforpredictionoffataloutcomeinpatientswithseverecovid19amulticenterstudy
AT gaoqian nomogramforpredictionoffataloutcomeinpatientswithseverecovid19amulticenterstudy
AT huangxuanxuan nomogramforpredictionoffataloutcomeinpatientswithseverecovid19amulticenterstudy
AT fulijuan nomogramforpredictionoffataloutcomeinpatientswithseverecovid19amulticenterstudy
AT zhangyu nomogramforpredictionoffataloutcomeinpatientswithseverecovid19amulticenterstudy
AT changyanqin nomogramforpredictionoffataloutcomeinpatientswithseverecovid19amulticenterstudy
AT zhanghuojun nomogramforpredictionoffataloutcomeinpatientswithseverecovid19amulticenterstudy
AT luzhijie nomogramforpredictionoffataloutcomeinpatientswithseverecovid19amulticenterstudy