1. Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19
- Author
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Jun S. Kim, Varun Arvind, Samuel K. Cho, Brian Cho, and Eric Geng
- Subjects
Adult ,Male ,Coronavirus disease 2019 (COVID-19) ,Hospitalized patients ,medicine.medical_treatment ,Future risk ,Critical Care and Intensive Care Medicine ,Machine learning ,computer.software_genre ,Article ,Cohort Studies ,03 medical and health sciences ,0302 clinical medicine ,Predictive Value of Tests ,Intubation, Intratracheal ,medicine ,Humans ,Intubation ,In patient ,Clinical care ,Aged ,Retrospective Studies ,Aged, 80 and over ,Mechanical ventilation ,SARS-CoV-2 ,business.industry ,COVID-19 ,030208 emergency & critical care medicine ,Retrospective cohort study ,Respiratory distress ,Middle Aged ,Prognosis ,Respiration, Artificial ,Hospitalization ,030228 respiratory system ,Area Under Curve ,Female ,New York City ,Supervised Machine Learning ,Artificial intelligence ,Prediction ,business ,computer ,Algorithm ,Algorithms - Abstract
Purpose The purpose of this study is to develop a machine learning algorithm to predict future intubation among patients diagnosed or suspected with COVID-19. Materials and methods This is a retrospective cohort study of patients diagnosed or under investigation for COVID-19. A machine learning algorithm was trained to predict future presence of intubation based on prior vitals, laboratory, and demographic data. Model performance was compared to ROX index, a validated prognostic tool for prediction of mechanical ventilation. Results 4087 patients admitted to five hospitals between February 2020 and April 2020 were included. 11.03% of patients were intubated. The machine learning model outperformed the ROX-index, demonstrating an area under the receiver characteristic curve (AUC) of 0.84 and 0.64, and area under the precision-recall curve (AUPRC) of 0.30 and 0.13, respectively. In the Kaplan-Meier analysis, patients alerted by the model were more likely to require intubation during their admission (p, Highlights • Machine learning on routinely collected health data can predict future intubation status among COVID-19 patients. • Learned decision criteria by the model closely matched normal-abnormal boundary thresholds for vitals and laboratory values. • Patients alerted by the model were more likely to require intubation during their admission.
- Published
- 2021