Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial

Autor: Anna Siefkas, Abigail Green-Saxena, Carson Lam, Gina Barnes, Jana Hoffman, Gregory Braden, Jacob Calvert, R. Phillip Dellinger, Hoyt Burdick, Emily Pellegrini, Andrea McCoy, Jean Louis Vincent, Ritankar Das, Samson Mataraso
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Male
0301 basic medicine
medicine.medical_treatment
Informatique appliquée logiciel
computer.software_genre
law.invention
Machine Learning
COVID-19 Testing
0302 clinical medicine
Mechanical ventilation
law
Medicine
Prospective Studies
Aged
80 and over
Middle Aged
Prognosis
Early warning score
Computer Science Applications
Ventilation (architecture)
Female
Coronavirus Infections
Respiratory Insufficiency
Algorithms
Adult
Pneumonia
Viral
Health Informatics
Machine learning
Sensitivity and Specificity
Article
Betacoronavirus
03 medical and health sciences
Humans
Decompensation
Pandemics
Aged
Clinical Laboratory Techniques
SARS-CoV-2
business.industry
Computational Biology
COVID-19
Informatique médicale
Respiration
Artificial
Triage
United States
COVID-19 Drug Treatment
Mews
Clinical trial
030104 developmental biology
Diagnostic odds ratio
Artificial intelligence
business
Prediction
computer
030217 neurology & neurosurgery
Zdroj: Computers in biology and medicine, 124
Computers in Biology and Medicine
Popis: Background: Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective for identifying patient decompensation. Machine learning (ML) may offer an alternative strategy. A prospectively validated method to predict the need for ventilation in COVID-19 patients is essential to help triage patients, allocate resources, and prevent emergency intubations and their associated risks. Methods: In a multicenter clinical trial, we evaluated the performance of a machine learning algorithm for prediction of invasive mechanical ventilation of COVID-19 patients within 24 h of an initial encounter. We enrolled patients with a COVID-19 diagnosis who were admitted to five United States health systems between March 24 and May 4, 2020. Results: 197 patients were enrolled in the REspirAtory Decompensation and model for the triage of covid-19 patients: a prospective studY (READY) clinical trial. The algorithm had a higher diagnostic odds ratio (DOR, 12.58) for predicting ventilation than a comparator early warning system, the Modified Early Warning Score (MEWS). The algorithm also achieved significantly higher sensitivity (0.90) than MEWS, which achieved a sensitivity of 0.78, while maintaining a higher specificity (p < 0.05). Conclusions: In the first clinical trial of a machine learning algorithm for ventilation needs among COVID-19 patients, the algorithm demonstrated accurate prediction of the need for mechanical ventilation within 24 h. This algorithm may help care teams effectively triage patients and allocate resources. Further, the algorithm is capable of accurately identifying 16% more patients than a widely used scoring system while minimizing false positive results.
SCOPUS: ar.j
info:eu-repo/semantics/published
Databáze: OpenAIRE
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