A risk score based on baseline risk factors for predicting mortality in COVID-19 patients
BACKGROUND: To develop a sensitive and clinically applicable risk assessment tool identifying coronavirus disease 2019 (COVID-19) patients with a high risk of mortality at hospital admission. This model would assist frontline clinicians in optimizing medical treatment with limited resources.
METHODS: 6415 patients from seven hospitals in Wuhan city were assigned to the training and testing cohorts. A total of 6351 patients from another three hospitals in Wuhan, 2169 patients from outside of Wuhan, and 553 patients from Milan, Italy were assigned to three independent validation cohorts. A total of 64 candidate clinical variables at hospital admission were analyzed by random forest and least absolute shrinkage and selection operator (LASSO) analyses.
RESULTS: Eight factors, namely, Oxygen saturation, blood Urea nitrogen, Respiratory rate, admission before the date the national Maximum number of daily new cases was reached, Age, Procalcitonin, C-reactive protein (CRP), and absolute Neutrophil counts, were identified as having significant associations with mortality in COVID-19 patients. A composite score based on these eight risk factors, termed the OURMAPCN-score, predicted the risk of mortality among the COVID-19 patients, with a C-statistic of 0.92 (95% confidence interval [CI] 0.90-0.93). The hazard ratio for all-cause mortality between patients with OURMAPCN-score >11 compared with those with scores ≤ 11 was 18.18 (95% CI 13.93-23.71; p < .0001). The predictive performance, specificity, and sensitivity of the score were validated in three independent cohorts.
CONCLUSIONS: The OURMAPCN score is a risk assessment tool to determine the mortality rate in COVID-19 patients based on a limited number of baseline parameters. This tool can assist physicians in optimizing the clinical management of COVID-19 patients with limited hospital resources.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:37 |
|---|---|
| Enthalten in: |
Current medical research and opinion - 37(2021), 6 vom: 03. Juni, Seite 917-927 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Chen, Ze [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
COVID-19 |
|---|
| Anmerkungen: |
Date Completed 17.06.2021 Date Revised 17.06.2021 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1080/03007995.2021.1904862 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM322865085 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM322865085 | ||
| 003 | DE-627 | ||
| 005 | 20231225182940.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1080/03007995.2021.1904862 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1076.xml |
| 035 | |a (DE-627)NLM322865085 | ||
| 035 | |a (NLM)33729889 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Chen, Ze |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A risk score based on baseline risk factors for predicting mortality in COVID-19 patients |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 17.06.2021 | ||
| 500 | |a Date Revised 17.06.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND: To develop a sensitive and clinically applicable risk assessment tool identifying coronavirus disease 2019 (COVID-19) patients with a high risk of mortality at hospital admission. This model would assist frontline clinicians in optimizing medical treatment with limited resources | ||
| 520 | |a METHODS: 6415 patients from seven hospitals in Wuhan city were assigned to the training and testing cohorts. A total of 6351 patients from another three hospitals in Wuhan, 2169 patients from outside of Wuhan, and 553 patients from Milan, Italy were assigned to three independent validation cohorts. A total of 64 candidate clinical variables at hospital admission were analyzed by random forest and least absolute shrinkage and selection operator (LASSO) analyses | ||
| 520 | |a RESULTS: Eight factors, namely, Oxygen saturation, blood Urea nitrogen, Respiratory rate, admission before the date the national Maximum number of daily new cases was reached, Age, Procalcitonin, C-reactive protein (CRP), and absolute Neutrophil counts, were identified as having significant associations with mortality in COVID-19 patients. A composite score based on these eight risk factors, termed the OURMAPCN-score, predicted the risk of mortality among the COVID-19 patients, with a C-statistic of 0.92 (95% confidence interval [CI] 0.90-0.93). The hazard ratio for all-cause mortality between patients with OURMAPCN-score >11 compared with those with scores ≤ 11 was 18.18 (95% CI 13.93-23.71; p < .0001). The predictive performance, specificity, and sensitivity of the score were validated in three independent cohorts | ||
| 520 | |a CONCLUSIONS: The OURMAPCN score is a risk assessment tool to determine the mortality rate in COVID-19 patients based on a limited number of baseline parameters. This tool can assist physicians in optimizing the clinical management of COVID-19 patients with limited hospital resources | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a in-hospital | |
| 650 | 4 | |a modeling | |
| 650 | 4 | |a mortality | |
| 650 | 4 | |a risk score | |
| 700 | 1 | |a Chen, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Jianghua |e verfasserin |4 aut | |
| 700 | 1 | |a Lei, Fang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Feng |e verfasserin |4 aut | |
| 700 | 1 | |a Qin, Juan-Juan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xiao-Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Lihua |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Ye-Mao |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Haitao |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Ming-Ming |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Yan-Ci |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Lijun |e verfasserin |4 aut | |
| 700 | 1 | |a Song, Xiaohui |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xingyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Chengzhang |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Weifang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xiao |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Deliang |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Youqin |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Mingyu |e verfasserin |4 aut | |
| 700 | 1 | |a Mao, Weiming |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Liming |e verfasserin |4 aut | |
| 700 | 1 | |a Ye, Ping |e verfasserin |4 aut | |
| 700 | 1 | |a Xiao, Bing |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Pengcheng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Zixiong |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Zhigang |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Junhai |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Haofeng |e verfasserin |4 aut | |
| 700 | 1 | |a Xia, Xigang |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Daihong |e verfasserin |4 aut | |
| 700 | 1 | |a Liao, Xiaofeng |e verfasserin |4 aut | |
| 700 | 1 | |a Peng, Gang |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Liang |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Guohua |e verfasserin |4 aut | |
| 700 | 1 | |a Azzolini, Elena |e verfasserin |4 aut | |
| 700 | 1 | |a Aghemo, Alessio |e verfasserin |4 aut | |
| 700 | 1 | |a Ciccarelli, Michele |e verfasserin |4 aut | |
| 700 | 1 | |a Condorelli, Gianluigi |e verfasserin |4 aut | |
| 700 | 1 | |a Stefanini, Giulio G |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Xiang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Bing-Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Xiaodong |e verfasserin |4 aut | |
| 700 | 1 | |a Xia, Jiahong |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Yufeng |e verfasserin |4 aut | |
| 700 | 1 | |a She, Zhi-Gang |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Jiao |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yibin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Peng |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Hongliang |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Current medical research and opinion |d 1972 |g 37(2021), 6 vom: 03. Juni, Seite 917-927 |w (DE-627)NLM000081019 |x 1473-4877 |7 nnns |
| 773 | 1 | 8 | |g volume:37 |g year:2021 |g number:6 |g day:03 |g month:06 |g pages:917-927 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1080/03007995.2021.1904862 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 37 |j 2021 |e 6 |b 03 |c 06 |h 917-927 | ||