Shangrong Wu1, Zhiguo Du1, Sanying Shen2, Bo Zhang2, Hong Yang3, Xia Li4, Wei Cui4, Fangxiong Chen1, Jin Huang1. 1. Department of Clinical Laboratory, Wuhan Fourth Hospital, Puai Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 2. Department of Respiratory Disease, Wuhan Fourth Hospital, Puai Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 3. Department of Emergency Medicine, Wuhan Fourth Hospital, Puai Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 4. Department of Radiology, Wuhan Fourth Hospital, Puai Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Abstract
BACKGROUND: This study aims to identify a prognostic biomarker to predict the disease prognosis and reduce the mortality rate of COVID-19, which has caused a worldwide pandemic. METHODS: COVID-19 patients were randomly divided into training and test groups. Univariate and multivariate Cox regression analyses were performed to identify the disease prognosis signature, which was selected to establish a risk model in the training group. Furthermore, the disease prognosis signature of COVID-19 was validated in the test group. RESULTS: The signature of COVID-19 was combined with five indicators, namely neutrophil count, lymphocyte count, procalcitonin, older age, and C-reactive protein. The signature stratified patients into high- and low-risk groups with significantly relevant disease prognosis (log-rank test, P<0.001) in the training group. The survival analysis indicated that the high-risk group displayed substantially lower survival probability than the low-risk group (log-rank test P<0.001). The area under ROC curve (AUC) showed that the signature of COVID-19 displayed the highest predictive accuracy regarding disease prognosis, which was 0.955 in the training group and 0.945 in the test group. The ROC analysis of both groups demonstrated that the predictive ability of the signature surpassed the use of each of the five indicators alone. CONCLUSION: The signature of COVID-19 presents a novel predictor and prognostic biomarker for closely monitoring patients and providing timely treatment for those who are severely or critically ill.
RCT Entities:
BACKGROUND: This study aims to identify a prognostic biomarker to predict the disease prognosis and reduce the mortality rate of COVID-19, which has caused a worldwide pandemic. METHODS:COVID-19patients were randomly divided into training and test groups. Univariate and multivariate Cox regression analyses were performed to identify the disease prognosis signature, which was selected to establish a risk model in the training group. Furthermore, the disease prognosis signature of COVID-19 was validated in the test group. RESULTS: The signature of COVID-19 was combined with five indicators, namely neutrophil count, lymphocyte count, procalcitonin, older age, and C-reactive protein. The signature stratified patients into high- and low-risk groups with significantly relevant disease prognosis (log-rank test, P<0.001) in the training group. The survival analysis indicated that the high-risk group displayed substantially lower survival probability than the low-risk group (log-rank test P<0.001). The area under ROC curve (AUC) showed that the signature of COVID-19 displayed the highest predictive accuracy regarding disease prognosis, which was 0.955 in the training group and 0.945 in the test group. The ROC analysis of both groups demonstrated that the predictive ability of the signature surpassed the use of each of the five indicators alone. CONCLUSION: The signature of COVID-19 presents a novel predictor and prognostic biomarker for closely monitoring patients and providing timely treatment for those who are severely or critically ill.
Authors: Irene Mollinedo-Gajate; Felipe Villar-Álvarez; María de Los Ángeles Zambrano-Chacón; Laura Núñez-García; Laura de la Dueña-Muñoz; Carlos López-Chang; Miguel Górgolas; Alfonso Cabello; Olga Sánchez-Pernaute; Fredeswinda Romero-Bueno; Álvaro Aceña; Nicolás González-Mangado; Germán Peces-Barba; Faustino Mollinedo Journal: Crit Care Explor Date: 2021-02-22
Authors: William Galanter; Jorge Mario Rodríguez-Fernández; Kevin Chow; Samuel Harford; Karl M Kochendorfer; Maryam Pishgar; Julian Theis; John Zulueta; Houshang Darabi Journal: BMC Med Inform Decis Mak Date: 2021-07-24 Impact factor: 2.796
Authors: David J Altschul; Santiago R Unda; Joshua Benton; Rafael de la Garza Ramos; Phillip Cezayirli; Mark Mehler; Emad N Eskandar Journal: Sci Rep Date: 2020-10-07 Impact factor: 4.379