About: Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients
Creator	Freeman, Robert Kia, Arash Kohli-Seth, Roopa Levin, Matthew Reich, David Tandon, Pranai Mazumdar, Madhu Timsina, Prem Cheng, Fu-Yuan Joshi, Himanshu
source	Medline; PMC
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.
has issue date	2020-06-01(xsd:dateTime)
bibo:doi	10.3390/jcm9061668
bibo:pmid	32492874
has license	cc-by
sha1sum (hex)	c7e8a36b45803936929d22dd92a5a24cdb041722
schema:url	https://doi.org/10.3390/jcm9061668
resource representing a document's title	Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients
has PubMed Central identifier	PMC7356638
has PubMed identifier	32492874
schema:publication	J Clin Med
resource representing a document's body	covid:c7e8a36b45803936929d22dd92a5a24cdb041722#body_text
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