Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Clinical Trial
. 2020 Sep:124:103949.
doi: 10.1016/j.compbiomed.2020.103949. Epub 2020 Aug 6.

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

Hoyt Burdick  1 , Carson Lam  2 , Samson Mataraso  2 , Anna Siefkas  3 , Gregory Braden  4 , R Phillip Dellinger  5 , Andrea McCoy  6 , Jean-Louis Vincent  7 , Abigail Green-Saxena  2 , Gina Barnes  2 , Jana Hoffman  2 , Jacob Calvert  2 , Emily Pellegrini  2 , Ritankar Das  2
Affiliations
Clinical Trial

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

Hoyt Burdick et al. Comput Biol Med. 2020 Sep.

Abstract

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.

Keywords: COVID-19; Machine learning; Mechanical ventilation; Prediction.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Patient inclusion flowchart.
Fig. 2
Fig. 2
Comparison of sensitivity and specificity for the machine learning algorithm and MEWS score. Abbreviations: MLA: Machine Learning Algorithm. MEWS: Modified Early Warning Score.
See this image and copyright information in PMC

References

    1. Subbe C.P., Slater A., Menon D. Validation of physiological scoring systems in the accident and emergency department. Emerg. Med. J. 2006 Nov;23(11):841–845. - PMC - PubMed
    1. Smith M.E.B., Chiovaro J.C., O'Neil M. Early warning system scores for clinical deterioration in hospitalized patients: a systematic review. Ann Am Thorac Soc. 2014 Oct 8;11(9):1454–1465. - PubMed
    1. Hu H., Yao N., Qiu Y. Comparing rapid scoring systems in mortality prediction of critically ill patients with novel coronavirus disease. Acad. Emerg. Med. 2020;27(6):461–468. doi: 10.1111/acem.13992. - DOI - PMC - PubMed
    1. Moghadas S.M., Shoukat A., Fitzpatrick M.C. Projecting hospital utilization during the COVID-19 outbreaks in the United States. Proc. Natl. Acad. Sci. U. S. A. 2020;117(16):9122–9126. doi: 10.1073/pnas.2004064117. - DOI - PMC - PubMed
    1. CDC . Centers for Disease Control and Prevention; 2020. Coronavirus Disease 2019 (COVID-19) [Internet]https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html [cited 2020 May 15]. Available from:

MeSH terms