Application of ordinal logistic regression analysis to identify the determinants of illness severity of COVID-19 in China

Abstract

Corona Virus Disease 2019 (COVID-19) has presented an unprecedented challenge to the health-care system across the world. The current study aims to identify the determinants of illness severity of COVID-19 based on ordinal responses. A retrospective cohort of COVID-19 patients from four hospitals in three provinces in China was established, and 598 patients were included from 1 January to 8 March 2020, and divided into moderate, severe and critical illness group. Relative variables were retrieved from electronic medical records. The univariate and multivariate ordinal logistic regression models were fitted to identify the independent predictors of illness severity. The cohort included 400 (66.89%) moderate cases, 85 (14.21%) severe and 113 (18.90%) critical cases, of whom 79 died during hospitalisation as of 28 April. Patients in the age group of 70+ years (OR = 3.419, 95% CI: 1.596–7.323), age of 40–69 years (OR = 1.586, 95% CI: 0.824–3.053), hypertension (OR = 3.372, 95% CI: 2.185–5.202), ALT >50 μ/l (OR = 3.304, 95% CI: 2.107–5.180), cTnI >0.04 ng/ml (OR = 7.464, 95% CI: 4.292–12.980), myohaemoglobin>48.8 ng/ml (OR = 2.214, 95% CI: 1.42–3.453) had greater risk of developing worse severity of illness. The interval between illness onset and diagnosis (OR = 1.056, 95% CI: 1.012–1.101) and interval between illness onset and admission (OR = 1.048, 95% CI: 1.009–1.087) were independent significant predictors of illness severity. Patients of critical illness suffered from inferior survival, as compared with patients in the severe group (HR = 14.309, 95% CI: 5.585–36.659) and in the moderate group (HR = 41.021, 95% CI: 17.588–95.678). Our findings highlight that the identified determinants may help to predict the risk of developing more severe illness among COVID-19 patients and contribute to optimising arrangement of health resources.

Fatality and risk features for prognosis in COVID-19 according to the care approach – a retrospective cohort study

“…Hypotension and raised BNP and troponins at presentation strongly suggest heart involvement. The associations with subsequent disease worsening [37] and fatal outcome ( [11], present report) support the role of troponins as a useful marker of disease progression and prognosis in COVID-19. Elevated troponin levels are frequent in COVID-19, ranging 12-28% patients [5,15,18,43], this rise correlates with ICU admission [18] and in-hospital mortality [5,18,43,44].…”

“…Seven readmissions occurred (2.3%), and no patient was lost to follow-up. Median (IQR) length of admission and follow-up were 9 (5-14) and 43 days (33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48), respectively.…”

“…The analysis of fatality in COVID-19 should consider the disease severity to compare across population and to identify outcome predictors [37]. In the absence of validated scales for COVID-19, a reasonable approach is to use the fatality/IMV ratio to compare severity.…”

Characteristics, and predictive factors of disease severity in hospitalized patients with SARS-COV-2 in Fayoum governorate, Egypt: a multicenter study

“…Overall, in the current study, elderly patients were more prominent in severe/critical than asymptomatic /non-severe group with a statistically significant difference (p<0.001) especially patients with comorbidity such as DM, and HTN with significant value (p<0.001). A similar result was reported in the study of Xu et al [10].…”

“…Our study focused on in-depth the predictive factors of severe/critical novel coronavirus pneumonia by multivariate logistic regression analysis, increasing age, CRP, LMR, and the radiographic score were possible predictors of severe/critical SARS-CoV-2 cases. Similarly, a higher total radiographic score, and increasing age were predictive factors of severity in Zhang et al [16], and XU et al [10] respectively. However, Liu et al [25], excluded age as a predictor of death by multivariate analysis.…”