Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Jul 30;15(7):e0236618.
doi: 10.1371/journal.pone.0236618. eCollection 2020.

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

Affiliations

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

Zirun Zhao et al. PLoS One. .

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.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flowchart describing patient selection.
Of the 4997 PUIs, 641 hospitalized and laboratory-confirmed COVID-19 patients who did not meet exclusion criteria were included in the study. There was an overlap of 34 patients between ICU and dead group because some patients expired in ICU and others died but did not receive ICU care.
Fig 2
Fig 2. Risk score stratifications for ICU admission and for mortality.
(A) Risk score stratification for ICU admission. (B) Risk score stratification for mortality. There were no patients with risk score of 7 in the mortality group.

References

    1. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA. 2020;323(11):1061–9. - PMC - PubMed
    1. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497–506. 10.1016/S0140-6736(20)30183-5 - DOI - PMC - PubMed
    1. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med. 2020;382(8):727–33. 10.1056/NEJMoa2001017 - DOI - PMC - PubMed
    1. WHO. Coronavirus disease 2019 (COVID-19) Situation Report 2020 [Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situatio....
    1. Johns Hopkins University Coronavirus Resource Center 2020 [Available from: https://coronavirus.jhu.edu.

MeSH terms