Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality

As the world emerges from the COVID-19 pandemic, there is an urgent need to understand patient factors that may be used to predict the occurrence of severe cases and patient mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful action...

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Published in:PLOS digital health Vol. 3; no. 4; p. e0000327
Main Authors: Rajwa, Bartek, Naved, Md Mobasshir Arshed, Adibuzzaman, Mohammad, Grama, Ananth Y., Khan, Babar A., Dundar, M. Murat, Rochet, Jean-Christophe
Format: Journal Article
Language:English
Published: United States Public Library of Science 01.04.2024
Public Library of Science (PLoS)
Subjects:
Biology and life sciences
Cognitive ability
Computer and Information Sciences
COVID-19
Cytokine storm
Delirium
Electronic health records
Feature selection
Generalized linear models
Hospitals
Hypotheses
Infections
Machine learning
Medicine and Health Sciences
Mortality
Pandemics
Patients
Regression analysis
Severe acute respiratory syndrome coronavirus 2
United States > US
Indianapolis Indiana
ISSN:2767-3170, 2767-3170
Online Access:Get full text
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Summary:As the world emerges from the COVID-19 pandemic, there is an urgent need to understand patient factors that may be used to predict the occurrence of severe cases and patient mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful actions of inflammatory mediators. Patients with severe COVID-19 are often afflicted with neurologic symptoms, and individuals with pre-existing neurodegenerative disease have an increased risk of severe COVID-19. Although collectively, these observations point to a bidirectional relationship between severe COVID-19 and neurologic disorders, little is known about the underlying mechanisms. Here, we analyzed the electronic health records of 471 patients with severe COVID-19 to identify clinical characteristics most predictive of mortality. Feature discovery was conducted by training a regularized logistic regression classifier that serves as a machine-learning model with an embedded feature selection capability. SHAP analysis using the trained classifier revealed that a small ensemble of readily observable clinical features, including characteristics associated with cognitive impairment, could predict in-hospital mortality with an accuracy greater than 0.85 (expressed as the area under the ROC curve of the classifier). These findings have important implications for the prioritization of clinical measures used to identify patients with COVID-19 (and, potentially, other forms of acute respiratory distress syndrome) having an elevated risk of death.
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The authors have declared that no competing interests exist.
ISSN:2767-3170
2767-3170
DOI:10.1371/journal.pdig.0000327