Abstract
BackgroundPredicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease.ObjectiveOur objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department.MethodsData were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics.ResultsThe XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics.ConclusionsThe XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.
Highlights
On March 11, 2020, COVID-19, the disease caused by SARS-CoV-2 infection, was declared a pandemic by the World Health Organization [1]
We identified 11,525 patients admitted from the emergency department (ED) with a diagnosis of COVID-19
We presented three models for predicting early respiratory failure in patients given a diagnosis of COVID-19 and admitted to the hospital from the ED
Summary
On March 11, 2020, COVID-19, the disease caused by SARS-CoV-2 infection, was declared a pandemic by the World Health Organization [1]. Respiratory failure is the leading cause of death among patients with COVID-19, with up to one-third of patients admitted with COVID-19 requiring invasive mechanical ventilation (IMV) [3,4,5,6,7,8]. Many patients with severe disease appear comfortable despite profound hypoxemia, and they are commonly managed with supplemental oxygen, self-proning, and close monitoring [9,10] Some of these patients subsequently deteriorate and require IMV following transfer from the emergency department (ED). Objective: Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19
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