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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Vydáno v:	PLOS digital health Ročník 3; číslo 4; s. e0000327
Hlavní autoři:	Rajwa, Bartek, Naved, Md Mobasshir Arshed, Adibuzzaman, Mohammad, Grama, Ananth Y., Khan, Babar A., Dundar, M. Murat, Rochet, Jean-Christophe
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 01.04.2024 Public Library of Science (PLoS)
Témata:	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
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Abstract	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.
AbstractList	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. Gaining insight into the patient attributes that are indicative of poor outcomes resulting from infections caused by highly lethal viruses such as SARS-CoV-2 is of paramount importance for hospitals and healthcare systems in order to adequately anticipate and address forthcoming outbreaks. Approximately 20% of the infections in the initial phase of the COVID-19 pandemic resulted in severe respiratory complications triggered by inflammatory responses. Moreover, individuals with severe COVID-19, particularly those with pre-existing neurodegenerative conditions, who were at a heightened risk, frequently displayed neurologic symptoms. Although there is a documented association between severe COVID-19 and neurologic issues, the underlying causes remain elusive. In this study, we examined the electronic health records of 471 patients with severe COVID-19 to identify clinical indicators that could predict mortality. Through machine learning models, we discovered a series of clinical features, notably those associated with cognitive dysfunction, that accurately predict mortality (area under the ROC curve > 0.85). These results underscore the significance of specific clinical indicators in recognizing patients at increased risk of mortality from COVID-19, thus enabling more focused and effective healthcare strategies. 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. 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.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.
Author	Adibuzzaman, Mohammad Khan, Babar A. Rajwa, Bartek Naved, Md Mobasshir Arshed Dundar, M. Murat Rochet, Jean-Christophe Grama, Ananth Y.
AuthorAffiliation	5 Regenstrief Institute, Indianapolis, Indiana, United States of America 6 Dept. of Computer and Information Science, IUPUI, Indianapolis, Indiana, United States of America 7 Borch Dept. of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana, United States of America 2 Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, United States of America CSL Behring / Swiss Institute for Translational and Entrepreneurial Medicine (SITEM), SWITZERLAND 3 Dept. of Computer Science, Purdue University, West Lafayette, Indiana, United States of America 1 Bindley Bioscience Center, Purdue University, West Lafayette, Indiana, United States of America 4 Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, Oregon, United States of America
AuthorAffiliation_xml	– name: 1 Bindley Bioscience Center, Purdue University, West Lafayette, Indiana, United States of America – name: 4 Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, Oregon, United States of America – name: 2 Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, United States of America – name: CSL Behring / Swiss Institute for Translational and Entrepreneurial Medicine (SITEM), SWITZERLAND – name: 7 Borch Dept. of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana, United States of America – name: 5 Regenstrief Institute, Indianapolis, Indiana, United States of America – name: 6 Dept. of Computer and Information Science, IUPUI, Indianapolis, Indiana, United States of America – name: 3 Dept. of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
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SubjectTerms	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
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Title	Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality
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