Image and structured data analysis for prognostication of health outcomes in patients presenting to the ED during the COVID-19 pandemic. (February 2022)

• Record Type: Journal Article
• Authors: Butler, Liam; Karabayir, Ibrahim; Samie Tootooni, Mohammad; Afshar, Majid; Goldberg, Ari; Akbilgic, Oguz
• Is Part Of: International journal of medical informatics. Volume 158(2022)
• Publication Date: 2022-02
• Subjects: COVID-19 -- ARDS -- ICU -- Mortality -- Machine/deep learning -- Chest radiographs; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2021.104662
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Light Gradient Boosting Machines (LightGBM) was used as the main machine learning algorithm to develop models to predict 4 main outcomes: COVID-19 infection, development of ARDS, ICU admission and risk of mortality. We built models using 42 clinical variables (full model) and compact clinical models limited to 15 clinical variables. Transfer learning from the CheXNet model was implemented on chest radiographs to predict the 4 aforementioned outcomes. Results of this study show that we can detect COVID-19 infection, development of ARDS, need for ICU admission and risk of mortality at maximum moderate accuracies of AUC = 0.790, 0.781, 0.675 and 0.759 respectively. These results can help in clinical decision making, during the assessment of patients admitted to the ED with or without COVID-19 symptoms. Abstract: Background: Patients admitted to the emergency department (ED) with COVID-19 symptoms are routinely required to have chest radiographs and computed tomography (CT) scans. COVID-19 infection has been directly related to the development of acute respiratory distress syndrome (ARDS) and severe infections could lead to admission to intensive care and increased risk of death. The use of clinical data in machine learning models available at time of admission to ED can be used to assess possible risk of ARDS, the need for intensive care (admission to the Intensive Care Unit; ICU) as well as risk of mortality. In addition, chest radiographs can be inputted into a deep learning model to further assess these risks. Purpose: This research aimed to develop machine and deep learning models using both structured clinical data and image data from the electronic health record (EHR) to predict adverse outcomes following ED admission. Materials and Methods: Light Gradient Boosting Machine (LightGBM) was used as the main machine learning algorithm using all clinical data including 42 variables. Compact models were also developed using the 15 most important variables to increase applicability of the models in clinical settings. To predict risk (or early stratified risk) of the aforementioned health outcome events, transfer learning from the CheXNet model was also implemented on the available data. This research utilized clinical data and chest radiographs of 3, 571 patients, 18 years and older, admitted to the emergency department between 9th March 2020 and 29th October 2020 at Loyola University Medical Center. Main Findings: The research results show that we can detect COVID-19 infection (AUC = 0.790 (0.746–0.835)), predict the risk of developing ARDS (AUC = 0.781 (0.690–0.872), risk stratification of the need for ICU admission (AUC = 0.675 (0.620–0.713)) and mortality (AUC = 0.759 (0.678–0.840)) at moderate accuracy from both chest X-ray images and clinical data. Principal Conclusions: The results can help in clinical decision making, especially when addressing ARDS and mortality, during the assessment of patients admitted to the ED with or without COVID-19 symptoms.