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Development and validation of prognosis model of mortality risk in patients with COVID-19.
Ma, Xuedi; Ng, Michael; Xu, Shuang; Xu, Zhouming; Qiu, Hui; Liu, Yuwei; Lyu, Jiayou; You, Jiwen; Zhao, Peng; Wang, Shihao; Tang, Yunfei; Cui, Hao; Yu, Changxiao; Wang, Feng; Shao, Fei; Sun, Peng; Tang, Ziren.
  • Ma X; AI Research Division, A.I. Phoenix Technology Co., Ltd, Hong Kong, China.
  • Ng M; Research Division for Mathematical and Statistical Science, University of Hong Kong, Hong Kong, China.
  • Xu S; Department of Emergency Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xu Z; AI Research Division, A.I. Phoenix Technology Co., Ltd, Hong Kong, China.
  • Qiu H; Research Division for Mathematical and Statistical Science, University of Hong Kong, Hong Kong, China.
  • Liu Y; Department of Emergency Surgery, The west campus of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Lyu J; AI Research Division, A.I. Phoenix Technology Co., Ltd, Hong Kong, China.
  • You J; AI Research Division, A.I. Phoenix Technology Co., Ltd, Hong Kong, China.
  • Zhao P; AI Research Division, A.I. Phoenix Technology Co., Ltd, Hong Kong, China.
  • Wang S; AI Research Division, A.I. Phoenix Technology Co., Ltd, Hong Kong, China.
  • Tang Y; AI Research Division, A.I. Phoenix Technology Co., Ltd, Hong Kong, China.
  • Cui H; AI Research Division, A.I. Phoenix Technology Co., Ltd, Hong Kong, China.
  • Yu C; Department of Emergency Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
  • Wang F; Department of Emergency Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
  • Shao F; Department of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
  • Sun P; Beijing Institute of Respiratory Medicine, Beijing Engineering Research Center for Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
  • Tang Z; Beijing Key Laboratory of Respiratory and Pulmonary Circulation Disorders, Beijing, China.
Epidemiol Infect ; 148: e168, 2020 08 04.
Article in English | MEDLINE | ID: covidwho-1537262
Semantic information from SemMedBD (by NLM)
1. COVID-19 PROCESS_OF Patients
Subject
COVID-19
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PROCESS_OF
Object
Patients
2. COVID-19 PROCESS_OF inpatient
Subject
COVID-19
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PROCESS_OF
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inpatient
3. Borg Category-Ratio 10 Perceived Exertion Score 5 PROCESS_OF Patients
Subject
Borg Category-Ratio 10 Perceived Exertion Score 5
Predicate
PROCESS_OF
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Patients
4. COVID-19 PROCESS_OF Patients
Subject
COVID-19
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PROCESS_OF
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Patients
5. COVID-19 PROCESS_OF inpatient
Subject
COVID-19
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PROCESS_OF
Object
inpatient
6. Borg Category-Ratio 10 Perceived Exertion Score 5 PROCESS_OF Patients
Subject
Borg Category-Ratio 10 Perceived Exertion Score 5
Predicate
PROCESS_OF
Object
Patients
ABSTRACT
This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Logistic Models / Coronavirus Infections / Machine Learning Type of study: Etiology study / Observational study / Prognostic study / Randomized controlled trials / Risk factors Limits: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged / Young adult Country/Region as subject: Asia Language: English Journal: Epidemiol Infect Journal subject: Communicable Diseases / Epidemiology Year: 2020 Document Type: Article Affiliation country: S0950268820001727

Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Logistic Models / Coronavirus Infections / Machine Learning Type of study: Etiology study / Observational study / Prognostic study / Randomized controlled trials / Risk factors Limits: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged / Young adult Country/Region as subject: Asia Language: English Journal: Epidemiol Infect Journal subject: Communicable Diseases / Epidemiology Year: 2020 Document Type: Article Affiliation country: S0950268820001727