Background: The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. Main body: While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for MLsupported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. Conclusion: This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.
Background Literature indicates an atypical presentation of COVID-19 among older adults (OAs). Our purpose is to identify the frequency of atypical presentation and compare demographic and clinical factors, and short-term outcomes, between typical versus atypical presentations in OAs hospitalized with COVID-19 during the first surge of the pandemic. Methods Data from the inpatient electronic health record were extracted for patients aged 65 and older, admitted to our health systems’ hospitals with COVID-19 between March 1 and April 20, 2020. Presentation as reported by the OA or his/her representative is documented by the admitting professional and includes both symptoms and signs. Natural language processing was used to code the presence/absence of each symptom or sign. Typical presentation was defined as words indicating fever, cough, or shortness of breath; atypical presentation was defined as words indicating functional decline or altered mental status. Results Of 4 961 unique OAs, atypical presentation characterized by functional decline or altered mental status was present in 24.9% and 11.3%, respectively. Atypical presentation was associated with older age, female gender, Black race, non-Hispanic ethnicity, higher comorbidity index, and the presence of dementia and diabetes mellitus. Those who presented typically were 1.39 times more likely than those who presented atypically to receive intensive care unit–level care. Hospital outcomes of mortality, length of stay, and 30-day readmission were similar between OAs with typical versus atypical presentations. Conclusion Although atypical presentation in OAs is not associated with the same need for acute intervention as respiratory distress, it must not be dismissed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.