PT  - JOURNAL ARTICLE
AU  - Molly J Douglas
AU  - Brian W Bell
AU  - Adrienne Kinney
AU  - Sarah A Pungitore
AU  - Brian P Toner
TI  - Early COVID-19 respiratory risk stratification using machine learning
AID  - 10.1136/tsaco-2022-000892
DP  - 2022 Aug 01
TA  - Trauma Surgery & Acute Care Open
PG  - e000892
VI  - 7
IP  - 1
4099  - http://tsaco.bmj.com/content/7/1/e000892.short
4100  - http://tsaco.bmj.com/content/7/1/e000892.full
SO  - Trauma Surg Acute Care Open2022 Aug 01; 7
AB  - Background COVID-19 has strained healthcare systems globally. In this and future pandemics, providers with limited critical care experience must distinguish between moderately ill patients and those who will require aggressive care, particularly endotracheal intubation. We sought to develop a machine learning-informed Early COVID-19 Respiratory Risk Stratification (ECoRRS) score to assist in triage, by providing a prediction of intubation within the next 48 hours based on objective clinical parameters.Methods Electronic health record data from 3447 COVID-19 hospitalizations, 20.7% including intubation, were extracted. 80% of these records were used as the derivation cohort. The validation cohort consisted of 20% of the total 3447 records. Multiple randomizations of the training and testing split were used to calculate confidence intervals. Data were binned into 4-hour blocks and labeled as cases of intubation or no intubation within the specified time frame. A LASSO (least absolute shrinkage and selection operator) regression model was tuned for sensitivity and sparsity.Results Six highly predictive parameters were identified, the most significant being fraction of inspired oxygen. The model achieved an area under the receiver operating characteristic curve of 0.789 (95% CI 0.785 to 0.812). At 90% sensitivity, the negative predictive value was 0.997.Discussion The ECoRRS score enables non-specialists to identify patients with COVID-19 at risk of intubation within 48 hours with minimal undertriage and enables health systems to forecast new COVID-19 ventilator needs up to 48 hours in advance.Level of evidence IV.Data may be obtained from a third party and are not publicly available. The data set generated and analyzed in the current study is protected by a Banner Health data use agreement (DUA), which prohibits placing the data in a public repository. The institution requires approval of a new DUA with any individual wishing to access the data. Requests for data access may be directed to the corresponding author, who will facilitate the request for a DUA through Banner Health. New requests typically require 3 to 6 months to process.