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

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About: Prediction model and risk scores of ICU admission and mortality in COVID-19 Goto Sponge NotDistinct Permalink

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Prediction model and risk scores of ICU admission and mortality in COVID-19
Creator	Hou, Wei Graham, James Chen, Anne Li, Haifang Richman, Paul Singer, Adam Thode, Henry Zhao, Zirun Duongid, Tim
Source	Medline; PMC
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.
has issue date	2020-07-30(xsd:dateTime)
bibo:doi	10.1371/journal.pone.0236618
bibo:pmid	32730358
has license	cc-by
sha1sum (hex)	94310f437664763acbb472df37158b9694a3bf3a
schema:url	https://doi.org/10.1371/journal.pone.0236618
resource representing a document's title	Prediction model and risk scores of ICU admission and mortality in COVID-19
has PubMed Central identifier	PMC7392248
has PubMed identifier	32730358
schema:publication	PLoS One
resource representing a document's body	covid:94310f437664763acbb472df37158b9694a3bf3a#body_text
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