Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients

Roshan Karri, Yi Ping Phoebe Chen, Aidan J.C. Burrell, Jahan C. Penny-Dimri, Tessa Broadley, Tony Trapani, Adam M. Deane, Andrew A. Udy, Mark P. Plummer, for the SPRINT-SARI Australia Investigators

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2 Citations (Scopus)

Abstract

Objective(s) To use machine learning (ML) to predict short-term requirements for invasive ventilation in patients with COVID-19 admitted to Australian intensive care units (ICUs). Design A machine learning study within a national ICU COVID-19 registry in Australia. Participants Adult patients who were spontaneously breathing and admitted to participating ICUs with laboratory-confirmed COVID-19 from 20 February 2020 to 7 March 2021. Patients intubated on day one of their ICU admission were excluded. Main outcome measures Six machine learning models predicted the requirement for invasive ventilation by day three of ICU admission from variables recorded on the first calendar day of ICU admission; (1) random forest classifier (RF), (2) decision tree classifier (DT), (3) logistic regression (LR), (4) K neighbours classifier (KNN), (5) support vector machine (SVM), and (6) gradient boosted machine (GBM). Cross-validation was used to assess the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of machine learning models. Results 300 ICU admissions collected from 53 ICUs across Australia were included. The median [IQR] age of patients was 59 [50–69] years, 109 (36%) were female and 60 (20%) required invasive ventilation on day two or three. Random forest and Gradient boosted machine were the best performing algorithms, achieving mean (SD) AUCs of 0.69 (0.06) and 0.68 (0.07), and mean sensitivities of 77 (19%) and 81 (17%), respectively. Conclusion Machine learning can be used to predict subsequent ventilation in patients with COVID-19 who were spontaneously breathing and admitted to Australian ICUs.

Original languageEnglish
Article numbere0276509
Number of pages15
JournalPLoS ONE
Volume17
Issue number10
DOIs
Publication statusPublished - Oct 2022

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