Identification of Clinical Features Associated with Mortality in COVID-19 Patients

被引：2
作者：
Eskandarian R. [1 ]
Alizadehsani R. [2 ]
Behjati M. [3 ]
Zahmatkesh M. [1 ]
Sani Z.A. [4 ]
Haddadi A. [5 ]
Kakhi K. [2 ]
Roshanzamir M. [6 ]
Shoeibi A. [7 ]
Hussain S. [8 ]
Khozeimeh F. [2 ]
Darbandy M.T. [9 ]
Joloudari J.H. [10 ,11 ]
Lashgari R. [12 ]
Khosravi A. [2 ]
Nahavandi S. [2 ,13 ]
Islam S.M.S. [14 ,15 ,16 ]
机构：
[1] Internal Medicine Research Center, Semnan University of Medical Sciences, Semnan
[2] Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria
[3] Rajaei Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran
[4] Omid Hospital, Iran University of Medical Sciences, Tehran
[5] Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord
[6] Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa
[7] Data Science and Computational Intelligence Institute, University of Granada, Granada
[8] System Administrator, Dibrugarh University, Assam
[9] School of Architecture, Islamic Azad University Taft, Taft
[10] Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand
[11] Department of Computer Engineering, Amol Institute of Higher Education, Amol
[12] Institute of Medical Science and Technology, Shahid Beheshti University, Tehran
[13] School of Engineering and Applied Sciences, Harvard Paulson, Harvard University, Allston, 02134, MA
[14] Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, 3220, VIC
[15] Cardiovascular Division, The George Institute for Global Health, Newtown
[16] Sydney Medical School, University of Sydney, Camperdown
来源：
Operations Research Forum | / 4卷 / 1期
基金：
英国医学研究理事会;
关键词：
COVID‐19; Machine learning; Mortality; Risk factors; Symptoms;
D O I：
10.1007/s43069-022-00191-3
中图分类号：
学科分类号：
摘要：
Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3 ± 18.7 years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation < 93%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings. © 2023, The Author(s).
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