Abstract
Objectives: Approximately 20–30% of patients with COVID-19 require hospitalization, and 5–12% may require critical care in an intensive care unit (ICU). A rapid surge in cases of severe COVID-19 will lead to a corresponding surge in demand for ICU care. Because of constraints on resources, frontline healthcare workers may be unable to provide the frequent monitoring and assessment required for all patients at high risk of clinical deterioration. We developed a machine learning-based risk prioritization tool that predicts ICU transfer within 24 h, seeking to facilitate efficient use of care providers’ efforts and help hospitals plan their flow of operations. Methods: A retrospective cohort was comprised of non-ICU COVID-19 admissions at a large acute care health system between 26 February and 18 April 2020. Time series data, including vital signs, nursing assessments, laboratory data, and electrocardiograms, were used as input variables for training a random forest (RF) model. The cohort was randomly split (70:30) into training and test sets. The RF model was trained using 10-fold cross-validation on the training set, and its predictive performance on the test set was then evaluated. Results: The cohort consisted of 1987 unique patients diagnosed with COVID-19 and admitted to non-ICU units of the hospital. The median time to ICU transfer was 2.45 days from the time of admission. Compared to actual admissions, the tool had 72.8% (95% CI: 63.2–81.1%) sensitivity, 76.3% (95% CI: 74.7–77.9%) specificity, 76.2% (95% CI: 74.6–77.7%) accuracy, and 79.9% (95% CI: 75.2–84.6%) area under the receiver operating characteristics curve. Conclusions: A ML-based prediction model can be used as a screening tool to identify patients at risk of imminent ICU transfer within 24 h. This tool could improve the management of hospital resources and patient-throughput planning, thus delivering more effective care to patients hospitalized with COVID-19.
Highlights
With more than 3 million cases and 200,000 deaths [1] by the end of April 2020, the COVID-19 pandemic has rapidly emerged as a serious global health emergency [2], testing the ability of health care systems to respond
These estimates indicate that the rate of intensive care unit (ICU) transfer of hospitalized patients with COVID-19 is significantly higher than the ICU transfer rates of 11% reported for other hospitalized patients [6,7]
After performing majority-class under-sampling, the final training set consisted of 2008 feature vectors, representing each non-ICU stay of 401 unique patients
Summary
With more than 3 million cases and 200,000 deaths [1] by the end of April 2020, the COVID-19 pandemic has rapidly emerged as a serious global health emergency [2], testing the ability of health care systems to respond. From 5% to 12% of all patients diagnosed with COVID-19 and up to 33% of hospitalized patients require supportive critical care in an intensive care unit (ICU) [3,4,5]. COVID-19 patients admitted to non-ICU units often experience rapid clinical deterioration [13] and, require frequent clinical assessments. With resources stretched thin, frequent assessment is difficult and can increase the risk of exposure among frontline personnel. To efficiently manage these finite resources and personnel, optimal prioritization of patients and efficient use of hospital resources are necessary
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