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BMC Pulm Med ; 21(1): 338, 2021 Oct 29.
Article in English | MEDLINE | ID: covidwho-1486570

ABSTRACT

Severe coronavirus disease 2019 (COVID-19) accompanies hypercytokinemia, similar to secondary hemophagocytic lymphohistiocytosis (sHLH). We aimed to find if HScore could predict disease severity in COVID-19. HScore was calculated in hospitalized children and adult patients with a proven diagnosis of COVID-19. The need for intensive care unit (ICU), hospital length of stay (LOS), and in-hospital mortality were recorded. The median HScore was 43.0 (IQR 0.0-63.0), which was higher in those who needed ICU care (59.7, 95% CI 46.4-72.7) compared to those admitted to non-ICU medical wards (38.8, 95% CI 32.2-45.4; P = 0.003). It was also significantly higher in patients who died of COVID-19 (105.1, 95% CI 53.7-156.5) than individuals who survived (41.5, 95% CI 35.8-47.1; P = 0.005). Multivariable logistic regression analysis revealed that higher HScore was associated with a higher risk of ICU admission (adjusted OR = 4.93, 95% CI 1.5-16.17, P = 0.008). The risk of death increased by 20% for every ten units increase in HScore (adjusted OR 1.02, 95% CI 1.00-1.04, P = 0.009). Time to discharge was statistically longer in high HScore levels than low levels (HR = 0.41, 95% CI 0.24-0.69). HScore is much lower in patients with severe COVID-19 than sHLH. Higher HScore is associated with more ICU admission, more extended hospitalization, and a higher mortality rate. A modified HScore with a new cut-off seems more practical in predicting disease severity in patients with severe COVID-19.


Subject(s)
COVID-19/diagnosis , Severity of Illness Index , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/mortality , COVID-19/pathology , COVID-19/therapy , COVID-19 Testing , Child , Child, Preschool , Critical Care/statistics & numerical data , Cytokine Release Syndrome/diagnosis , Cytokine Release Syndrome/virology , Female , Hospital Mortality , Hospitalization , Humans , Infant , Iran/epidemiology , Length of Stay/statistics & numerical data , Logistic Models , Male , Middle Aged , Predictive Value of Tests , Prognosis , Prospective Studies , Survival Analysis , Young Adult
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