Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19

• Published in: PloS one Vol. 16; no. 4; p. e0249285
• Main Authors: Yu, Limin, Halalau, Alexandra, Dalal, Bhavinkumar, Abbas, Amr E., Ivascu, Felicia, Amin, Mitual, Nair, Girish B.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2021; Public Library of Science (PLoS)
• Subjects: Aged; Alcohol; Algorithms; Anticoagulants; Artificial respiration; Autoimmune diseases; Biology and Life Sciences; Bleeding; Blood pressure; C-reactive protein; Calcium-binding protein; Cardiovascular diseases; Computer and Information Sciences; Congestive heart failure; Connective tissue diseases; Coronary artery disease; Coronaviridae; Coronaviruses; COVID-19; COVID-19 - mortality; Critical care; Data collection; Diabetes; Diabetes mellitus; Dimers; Draft (gas flow); Drafting software; Editing; Emergency Service, Hospital - trends; Female; Fibrillation; Forecasts and trends; Health risks; Hospital Mortality - trends; Hospitalization; Humans; Hyperlipidemia; Hypertension; Industrial applications; Ischemia; Kidney diseases; Kidneys; Laboratories; Learning algorithms; Lung diseases; Machine Learning; Male; Malignancy; Mechanical ventilation; Medicine; Medicine and Health Sciences; Methodology; Middle Aged; Mortality; Oxygen; Oxygen content; Oxygen saturation; Pandemics - statistics & numerical data; Pathology; Patients; Performance evaluation; Physical Sciences; Prognosis; Research and Analysis Methods; Respiration; Respiration, Artificial - statistics & numerical data; Respiratory diseases; Respiratory rate; Retrospective Studies; Severe acute respiratory syndrome; Severe acute respiratory syndrome coronavirus 2; Sex; Steroid hormones; Steroids; Stroke; Structured Query Language-SQL; Surgery; Thromboembolism; Transient ischemic attack; United States; Vascular diseases; Ventilation; Ventilators; Ventilators, Mechanical - statistics & numerical data; Viral diseases; Vital signs; United States; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0249285

The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide. To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted. Two cohorts were used for the two different aims. 1980 COVID-19 patients were enrolled for the aim of prediction ofMV. 1036 patients' data, including demographics, past smoking and drinking history, past medical history and vital signs at emergency room (ER), laboratory values, and treatments were collected for training and 674 patients were enrolled for validation using XGBoost algorithm. For the second aim to predict in-hospital mortality, 3491 hospitalized patients via ER were enrolled. CatBoost, a new gradient-boosting algorithm was applied for training and validation of the cohort. Older age, higher temperature, increased respiratory rate (RR) and a lower oxygen saturation (SpO2) from the first set of vital signs were associated with an increased risk of MV amongst the 1980 patients in the ER. The model had a high accuracy of 86.2% and a negative predictive value (NPV) of 87.8%. While, patients who required MV, had a higher RR, Body mass index (BMI) and longer length of stay in the hospital were the major features associated with in-hospital mortality. The second model had a high accuracy of 80% with NPV of 81.6%. Machine learning models using XGBoost and catBoost algorithms can predict need for mechanical ventilation and mortality with a very high accuracy in COVID-19 patients.