Mengyuan Liang1, Miao He1, Jian Tang1, Xinliang He1, Zhijun Liu2, Siwei Feng1, Ping Chen1, Hui Li1, Yu'e Xue1, Tao Bai3, Yanling Ma4, Jianchu Zhang5. 1. Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical Collage, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, Hubei Province, China. 2. Department of Neurology, Union Hospital, Tongji Medical Collage, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, Hubei Province, China. 3. Department of Gastroenterology, Union Hospital, Tongji Medical Collage, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, Hubei Province, China. 4. Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical Collage, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, Hubei Province, China. mayanling811@hust.edu.cn. 5. Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical Collage, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, Hubei Province, China. zsn0928@163.com.
Abstract
BACKGROUND: The mortality rate from acute respiratory distress syndrome (ARDS) is high among hospitalized patients with coronavirus disease 2019 (COVID-19). Hence, risk evaluation tools are required to immediately identify high-risk patients upon admission for early intervention. METHODS: A cohort of 220 consecutive patients with COVID-19 were included in this study. To analyze the risk factors of ARDS, data obtained from approximately 70% of the participants were randomly selected and used as training dataset to establish a logistic regression model. Meanwhile, data obtained from the remaining 30% of the participants were used as test dataset to validate the effect of the model. RESULTS: Lactate dehydrogenase, blood urea nitrogen, D-dimer, procalcitonin, and ferritin levels were included in the risk score system and were assigned a score of 25, 15, 34, 20, and 24, respectively. The cutoff value for the total score was > 35, with a sensitivity of 100.00% and specificity of 81.20%. The area under the receiver operating characteristic curve and the Hosmer-Lemeshow test were 0.967 (95% confidence interval [CI]: 0.925-0.989) and 0.437(P Value = 0.437). The model had excellent discrimination and calibration during internal validation. CONCLUSIONS: The novel risk score may be a valuable risk evaluation tool for screening patients with COVID-19 who are at high risk of ARDS.
BACKGROUND: The mortality rate from acute respiratory distress syndrome (ARDS) is high among hospitalized patients with coronavirus disease 2019 (COVID-19). Hence, risk evaluation tools are required to immediately identify high-risk patients upon admission for early intervention. METHODS: A cohort of 220 consecutive patients with COVID-19 were included in this study. To analyze the risk factors of ARDS, data obtained from approximately 70% of the participants were randomly selected and used as training dataset to establish a logistic regression model. Meanwhile, data obtained from the remaining 30% of the participants were used as test dataset to validate the effect of the model. RESULTS:Lactate dehydrogenase, blood ureanitrogen, D-dimer, procalcitonin, and ferritin levels were included in the risk score system and were assigned a score of 25, 15, 34, 20, and 24, respectively. The cutoff value for the total score was > 35, with a sensitivity of 100.00% and specificity of 81.20%. The area under the receiver operating characteristic curve and the Hosmer-Lemeshow test were 0.967 (95% confidence interval [CI]: 0.925-0.989) and 0.437(P Value = 0.437). The model had excellent discrimination and calibration during internal validation. CONCLUSIONS: The novel risk score may be a valuable risk evaluation tool for screening patients with COVID-19 who are at high risk of ARDS.
Authors: X H Yao; T Y Li; Z C He; Y F Ping; H W Liu; S C Yu; H M Mou; L H Wang; H R Zhang; W J Fu; T Luo; F Liu; Q N Guo; C Chen; H L Xiao; H T Guo; S Lin; D F Xiang; Y Shi; G Q Pan; Q R Li; X Huang; Y Cui; X Z Liu; W Tang; P F Pan; X Q Huang; Y Q Ding; X W Bian Journal: Zhonghua Bing Li Xue Za Zhi Date: 2020-05-08
Authors: V Marco Ranieri; Gordon D Rubenfeld; B Taylor Thompson; Niall D Ferguson; Ellen Caldwell; Eddy Fan; Luigi Camporota; Arthur S Slutsky Journal: JAMA Date: 2012-06-20 Impact factor: 56.272
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