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Clinical characteristics and a decision tree model to predict death outcome in severe COVID-19 patients.
Yang, Qiao; Li, Jixi; Zhang, Zhijia; Wu, Xiaocheng; Liao, Tongquan; Yu, Shiyong; You, Zaichun; Hou, Xianhua; Ye, Jun; Liu, Gang; Ma, Siyuan; Xie, Ganfeng; Zhou, Yi; Li, Mengxia; Wu, Meihui; Feng, Yimei; Wang, Weili; Li, Lufeng; Xie, Dongjing; Hu, Yunhui; Liu, Xi; Wang, Bin; Zhao, Songtao; Li, Li; Luo, Chunmei; Tang, Tang; Wu, Hongmei; Hu, Tianyu; Yang, Guangrong; Luo, Bangyu; Li, Lingchen; Yang, Xiu; Li, Qi; Xu, Zhi; Wu, Hao; Sun, Jianguo.
  • Yang Q; Department of Ultrasound, The 941st Hospital of the PLA Joint Logistic Support Force, Xining, People's Republic of China.
  • Li J; Cancer Institute, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Zhang Z; Department of Clinical Laboratory, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Wu X; Department of Emergency, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Liao T; Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Yu S; Department of Cardiology, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • You Z; Department of General Medicine, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Hou X; Department of Neurology, Southwest Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Ye J; Department of Gastroenterology, Southwest Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Liu G; Pulmonary and Critical Care Medicine Center, Chinese PLA Respiratory Disease Institute, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Ma S; Institute of Burn Research, State Key Laboratory of Trauma, Burns and Combined Injury, Army Medical University, Chongqing, People's Republic of China.
  • Xie G; Department of Oncology, Southwest Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Zhou Y; Cancer Institute, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Li M; Cancer Center, Army Medical Center, Chongqing, People's Republic of China.
  • Wu M; Nursing Department, Army Medical Center, Chongqing, People's Republic of China.
  • Feng Y; Department of Hematology, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Wang W; Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Kidney Center of PLA, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Li L; Department of Infectious Diseases, Southwest Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Xie D; Department of Neurology, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Hu Y; Department of Cardiology, The 958th Hospital, Southwest Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Liu X; Department of Gastroenterology, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Wang B; Pulmonary and Critical Care Medicine Center, Chinese PLA Respiratory Disease Institute, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Zhao S; Department of Infectious Diseases, Southwest Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Li L; Department of Respiratory Medicine, Army Medical Center, Chongqing, People's Republic of China.
  • Luo C; Department of Orthopedics, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Tang T; Department of Obstetrics and Gynecology, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Wu H; Pulmonary and Critical Care Medicine Center, Chinese PLA Respiratory Disease Institute, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Hu T; Department of Nosocomial Infection Control, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Yang G; Cancer Institute, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Luo B; Cancer Institute, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Li L; Cancer Institute, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Yang X; Cancer Institute, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China.
  • Li Q; Pulmonary and Critical Care Medicine Center, Chinese PLA Respiratory Disease Institute, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China. liqioliver@sina.com.
  • Xu Z; Pulmonary and Critical Care Medicine Center, Chinese PLA Respiratory Disease Institute, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China. xu_zhi999@163.com.
  • Wu H; Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China. ewuhao@163.com.
  • Sun J; Cancer Institute, Xinqiao Hospital, Army Medical University, Chongqing, People's Republic of China. sunjg09@aliyun.com.
BMC Infect Dis ; 21(1): 783, 2021 Aug 09.
Article in English | MEDLINE | ID: covidwho-1350140
ABSTRACT

BACKGROUND:

The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death.

METHODS:

A total of 2169 adult COVID-19 patients were enrolled from Wuhan, China, from February 10th to April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups, as well as between survivors and non-survivors. In addition, we developed a decision tree model to predict death outcome in severe patients.

RESULTS:

Of the 2169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed as severe illness, and 75 patients died. An older median age and a higher proportion of male patients were found in severe group or non-survivors compared to their counterparts. Significant differences in clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A decision tree, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in training and test datasets. The accuracy of this model were 0.98 in both datasets.

CONCLUSION:

We performed a comprehensive analysis of COVID-19 patients from the outbreak in Wuhan, China, and proposed a simple and clinically operable decision tree to help clinicians rapidly identify COVID-19 patients at high risk of death, to whom priority treatment and intensive care should be given.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Adult / Humans / Male / Infant, Newborn Country/Region as subject: Asia Language: English Journal: BMC Infect Dis Journal subject: Communicable Diseases Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Adult / Humans / Male / Infant, Newborn Country/Region as subject: Asia Language: English Journal: BMC Infect Dis Journal subject: Communicable Diseases Year: 2021 Document Type: Article