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Usefulness of the CONUT index upon hospital admission as a potential prognostic indicator of COVID-19 health outcomes

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摘要 Background:In-hospital mortality in patients with coronavirus disease 2019(COVID-19)is high.Simple prognostic indices are needed to identify patients at high-risk of COVID-19 health outcomes.We aimed to determine the usefulness of the CONtrolling NUTritional status(CONUT)index as a potential prognostic indicator of mortality in COVID-19 patients upon hospital admission.Methods:Our study design is of a retrospective observational study in a large cohort of COVID-19 patients.In addition to descriptive statistics,a Kaplan-Meier mortality analysis and a Cox regression were performed,as well as receiver operating curve(ROC).Results:From February 5,2020 to January 21,2021,there was a total of 2969 admissions for COVID-19 at our hospital,corresponding to 2844 patients.Overall,baseline(within 4 days of admission)CONUT index could be scored for 1627(57.2%)patients.Patients’age was 67.3±16.5 years and 44.9%were women.The CONUT severity distribution was:194(11.9%)normal(0-1);769(47.2%)light(2-4);585(35.9%)moderate(5-8);and 79(4.9%)severe(9-12).Mortality of 30 days after admission was 3.1%in patients with normal risk CONUT,9.0%light,22.7%moderate,and 40.5%in those with severe CONUT(P<0.05).An increased risk of death associated with a greater baseline CONUT stage was sustained in a multivariable Cox regression model(P<0.05).An increasing baseline CONUT stage was associated with a longer duration of admission,a greater requirement for the use of non-invasive and invasive mechanical ventilation,and other clinical outcomes(all P<0.05).The ROC of CONUT for mortality had an area under the curve(AUC)and 95%confidence interval of 0.711(0.676-0746).Conclusion:The CONUT index upon admission is potentially a reliable and independent prognostic indicator of mortality and length of hospitalization in COVID-19 patients.
出处 《Chinese Medical Journal》 SCIE CAS CSCD 2022年第2期187-193,共7页 中华医学杂志(英文版)
基金 The work is supported by a grant from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement(No 101016216).
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