CovidSeniorV1, Main, Curation, bibRecord, 000920

Prediction model and risk scores of ICU admission and mortality in COVID-19.

Identifieur interne : 000920 ( Main/Curation ); précédent : 000919; suivant : 000921

Prediction model and risk scores of ICU admission and mortality in COVID-19.

Auteurs : Zirun Zhao [États-Unis] ; Anne Chen [États-Unis] ; Wei Hou [États-Unis] ; James M. Graham [États-Unis] ; Haifang Li [États-Unis] ; Paul S. Richman [États-Unis] ; Henry C. Thode [États-Unis] ; Adam J. Singer [États-Unis] ; Tim Q. Duong [États-Unis]

Source :

PloS one [ 1932-6203 ] ; 2020.

RBID : pubmed:32730358

Descripteurs français

English descriptors

KwdEn :
- Aged (MeSH), Aged, 80 and over (MeSH), Area Under Curve (MeSH), Betacoronavirus (MeSH), Clinical Decision-Making (MeSH), Coronavirus Infections (epidemiology), Coronavirus Infections (mortality), Coronavirus Infections (virology), Female (MeSH), Hospitals, University (MeSH), Humans (MeSH), Intensive Care Units (MeSH), Logistic Models (MeSH), Male (MeSH), Middle Aged (MeSH), Models, Theoretical (MeSH), New York (epidemiology), Pandemics (MeSH), Patient Admission (trends), Pneumonia, Viral (epidemiology), Pneumonia, Viral (mortality), Pneumonia, Viral (virology), Prognosis (MeSH), ROC Curve (MeSH), Retrospective Studies (MeSH), Risk Factors (MeSH).
MESH :
- geographic , epidemiology : New York.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- mortality : Coronavirus Infections, Pneumonia, Viral.
- trends : Patient Admission.
- virology : Coronavirus Infections, Pneumonia, Viral.
- Aged, Aged, 80 and over, Area Under Curve, Betacoronavirus, Clinical Decision-Making, Female, Hospitals, University, Humans, Intensive Care Units, Logistic Models, Male, Middle Aged, Models, Theoretical, Pandemics, Prognosis, ROC Curve, Retrospective Studies, Risk Factors.

Abstract

This study aimed to develop risk scores based on clinical characteristics at presentation to predict intensive care unit (ICU) admission and mortality in COVID-19 patients. 641 hospitalized patients with laboratory-confirmed COVID-19 were selected from 4997 persons under investigation. We performed a retrospective review of medical records of demographics, comorbidities and laboratory tests at the initial presentation. Primary outcomes were ICU admission and death. Logistic regression was used to identify independent clinical variables predicting the two outcomes. The model was validated by splitting the data into 70% for training and 30% for testing. Performance accuracy was evaluated using area under the curve (AUC) of the receiver operating characteristic analysis (ROC). Five significant variables predicting ICU admission were lactate dehydrogenase, procalcitonin, pulse oxygen saturation, smoking history, and lymphocyte count. Seven significant variables predicting mortality were heart failure, procalcitonin, lactate dehydrogenase, chronic obstructive pulmonary disease, pulse oxygen saturation, heart rate, and age. The mortality group uniquely contained cardiopulmonary variables. The risk score model yielded good accuracy with an AUC of 0.74 ([95% CI, 0.63-0.85], p = 0.001) for predicting ICU admission and 0.83 ([95% CI, 0.73-0.92], p<0.001) for predicting mortality for the testing dataset. This study identified key independent clinical variables that predicted ICU admission and mortality associated with COVID-19. This risk score system may prove useful for frontline physicians in clinical decision-making under time-sensitive and resource-constrained environment.

DOI: 10.1371/journal.pone.0236618
PubMed: 32730358
PubMed Central: PMC7392248

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Le document en format XML

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<term>Clinical Decision-Making (MeSH)</term>
<term>Coronavirus Infections (epidemiology)</term>
<term>Coronavirus Infections (mortality)</term>
<term>Coronavirus Infections (virology)</term>
<term>Female (MeSH)</term>
<term>Hospitals, University (MeSH)</term>
<term>Humans (MeSH)</term>
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<term>Logistic Models (MeSH)</term>
<term>Male (MeSH)</term>
<term>Middle Aged (MeSH)</term>
<term>Models, Theoretical (MeSH)</term>
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<term>Pandemics (MeSH)</term>
<term>Patient Admission (trends)</term>
<term>Pneumonia, Viral (epidemiology)</term>
<term>Pneumonia, Viral (mortality)</term>
<term>Pneumonia, Viral (virology)</term>
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<term>Aire sous la courbe (MeSH)</term>
<term>Betacoronavirus (MeSH)</term>
<term>Courbe ROC (MeSH)</term>
<term>Facteurs de risque (MeSH)</term>
<term>Femelle (MeSH)</term>
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<term>Infections à coronavirus (mortalité)</term>
<term>Infections à coronavirus (virologie)</term>
<term>Infections à coronavirus (épidémiologie)</term>
<term>Modèles logistiques (MeSH)</term>
<term>Modèles théoriques (MeSH)</term>
<term>Mâle (MeSH)</term>
<term>Pandémies (MeSH)</term>
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<term>Pneumopathie virale (virologie)</term>
<term>Pneumopathie virale (épidémiologie)</term>
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<term>Facteurs de risque</term>
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<term>Sujet âgé</term>
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<front><div type="abstract" xml:lang="en">This study aimed to develop risk scores based on clinical characteristics at presentation to predict intensive care unit (ICU) admission and mortality in COVID-19 patients. 641 hospitalized patients with laboratory-confirmed COVID-19 were selected from 4997 persons under investigation. We performed a retrospective review of medical records of demographics, comorbidities and laboratory tests at the initial presentation. Primary outcomes were ICU admission and death. Logistic regression was used to identify independent clinical variables predicting the two outcomes. The model was validated by splitting the data into 70% for training and 30% for testing. Performance accuracy was evaluated using area under the curve (AUC) of the receiver operating characteristic analysis (ROC). Five significant variables predicting ICU admission were lactate dehydrogenase, procalcitonin, pulse oxygen saturation, smoking history, and lymphocyte count. Seven significant variables predicting mortality were heart failure, procalcitonin, lactate dehydrogenase, chronic obstructive pulmonary disease, pulse oxygen saturation, heart rate, and age. The mortality group uniquely contained cardiopulmonary variables. The risk score model yielded good accuracy with an AUC of 0.74 ([95% CI, 0.63-0.85], p = 0.001) for predicting ICU admission and 0.83 ([95% CI, 0.73-0.92], p<0.001) for predicting mortality for the testing dataset. This study identified key independent clinical variables that predicted ICU admission and mortality associated with COVID-19. This risk score system may prove useful for frontline physicians in clinical decision-making under time-sensitive and resource-constrained environment.</div>
</front>
</TEI>
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<DateCompleted><Year>2020</Year>
<Month>08</Month>
<Day>21</Day>
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<DateRevised><Year>2020</Year>
<Month>08</Month>
<Day>21</Day>
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<Issue>7</Issue>
<PubDate><Year>2020</Year>
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<Title>PloS one</Title>
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<Abstract><AbstractText>This study aimed to develop risk scores based on clinical characteristics at presentation to predict intensive care unit (ICU) admission and mortality in COVID-19 patients. 641 hospitalized patients with laboratory-confirmed COVID-19 were selected from 4997 persons under investigation. We performed a retrospective review of medical records of demographics, comorbidities and laboratory tests at the initial presentation. Primary outcomes were ICU admission and death. Logistic regression was used to identify independent clinical variables predicting the two outcomes. The model was validated by splitting the data into 70% for training and 30% for testing. Performance accuracy was evaluated using area under the curve (AUC) of the receiver operating characteristic analysis (ROC). Five significant variables predicting ICU admission were lactate dehydrogenase, procalcitonin, pulse oxygen saturation, smoking history, and lymphocyte count. Seven significant variables predicting mortality were heart failure, procalcitonin, lactate dehydrogenase, chronic obstructive pulmonary disease, pulse oxygen saturation, heart rate, and age. The mortality group uniquely contained cardiopulmonary variables. The risk score model yielded good accuracy with an AUC of 0.74 ([95% CI, 0.63-0.85], p = 0.001) for predicting ICU admission and 0.83 ([95% CI, 0.73-0.92], p<0.001) for predicting mortality for the testing dataset. This study identified key independent clinical variables that predicted ICU admission and mortality associated with COVID-19. This risk score system may prove useful for frontline physicians in clinical decision-making under time-sensitive and resource-constrained environment.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhao</LastName>
<ForeName>Zirun</ForeName>
<Initials>Z</Initials>
<AffiliationInfo><Affiliation>Department of Radiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, United States of America.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Chen</LastName>
<ForeName>Anne</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Department of Radiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, United States of America.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Hou</LastName>
<ForeName>Wei</ForeName>
<Initials>W</Initials>
<AffiliationInfo><Affiliation>Department of Radiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, United States of America.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Graham</LastName>
<ForeName>James M</ForeName>
<Initials>JM</Initials>
<AffiliationInfo><Affiliation>Department of Radiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, United States of America.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Li</LastName>
<ForeName>Haifang</ForeName>
<Initials>H</Initials>
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</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Richman</LastName>
<ForeName>Paul S</ForeName>
<Initials>PS</Initials>
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</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Thode</LastName>
<ForeName>Henry C</ForeName>
<Initials>HC</Initials>
<AffiliationInfo><Affiliation>Department of Emergency Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Singer</LastName>
<ForeName>Adam J</ForeName>
<Initials>AJ</Initials>
<AffiliationInfo><Affiliation>Department of Emergency Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Duong</LastName>
<ForeName>Tim Q</ForeName>
<Initials>TQ</Initials>
<Identifier Source="ORCID">0000-0001-6403-2827</Identifier>
<AffiliationInfo><Affiliation>Department of Radiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
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Prediction model and risk scores of ICU admission and mortality in COVID-19.

Prediction model and risk scores of ICU admission and mortality in COVID-19.

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