Association between IL-6 and severe disease and mortality in COVID-19 disease: a systematic review and meta-analysis.

BACKGROUND: So far, SARS-CoV-2 is the seventh coronavirus found to infect humans and cause disease with quite a strong infectivity. Patients diagnosed as severe or critical cases are prone to multiple organ dysfunction syndrome, acute respiratory distress syndrome and even death. Proinflammatory cytokine IL-6 has been reported to be associated with the severity of disease and mortality in patients with COVID-19. OBJECTIVE: This systematic review and meta-analysis were carried out to evaluate the association between IL-6 and severe disease and mortality in COVID-19 disease. METHODS: A systematic literature search using China National Knowledge Infrastructure, Wanfang databases, China Science and Technology Journal Database, Chinese Biomedical Literature, Embase, PubMed and Cochrane Central Register of Controlled Trials was performed from inception until 16 January 2021. RESULTS: 12 studies reported the value of IL-6 for predicting the severe disease in patients with COVID-19. The pooled area under the curve (AUC) was 0.85 (95% CI 0.821 to 0.931). 5 studies elaborated the predictive value of IL-6 on mortality. The pooled sensitivity, specificity and AUC were 0.15 (95% CI 0.13 to 0.17, I2=98.9%), 0.73 (95% CI 0.65 to 0.79, I2=91.8%) and 0.531 (95% CI 0.451 to 0.612), respectively. Meta-regression analysis showed that country, technique used, cut-off, sample, study design and detection time did not contribute to the heterogeneity of mortality. CONCLUSION: IL-6 is an adequate predictor of severe disease in patients infected with the COVID-19. The finding of current study may guide clinicians and healthcare providers in identifying potentially severe or critical patients with COVID-19 at the initial stage of the disease. Moreover, we found that only monitoring IL-6 levels does not seem to predict mortality and was not associated with COVID-19's mortality. PROSPERO REGISTRATION NUMBER: CRD42021233649.

Analysis of Risk Factors for Thromboembolic Events in 88 Patients with COVID-19 Pneumonia in Wuhan, China: A Retrospective Descriptive Report.

BACKGROUND Since the outbreak of COVID-19 in December 2019, there have been 96 623 laboratory-confirmed cases and 4784 deaths by December 29 in China. We aimed to analyze the risk factors and the incidence of thrombosis from patients with confirmed COVID-19 pneumonia. MATERIAL AND METHODS Eighty-eight inpatients with confirmed COVID-19 pneumonia were reported (31 critical cases, 33 severe cases, and 24 common cases). The thrombosis risk factor assessment, laboratory results, ultrasonographic findings, and prognoses of these patients were analyzed, and compared among groups with different severity. RESULTS Nineteen of the 88 cases developed DVT (12 critical cases, 7 severe cases, and no common cases). In addition, among the 18 patients who died, 5 were diagnosed with DVT. Positive correlations were observed between the increase in D-dimer level (≥5 µg/mL) and the severity of COVID-19 pneumonia (r=0.679, P<0.01), and between the high Padua score (≥4) and the severity (r=0.799, P<0.01). In addition, the CRP and LDH levels on admission had positive correlations with the severity of illness (CRP: r=0.522, P<0.01; LDH: r=0.600, P<0.01). A negative correlation was observed between the lymphocyte count on admission and the severity of illness (r=-0.523, P<0.01). There was also a negative correlation between the lymphocyte count on admission and mortality in critical patients (r=-0.499, P<0.01). Univariable logistic regression analysis showed that the occurrence of DVT was positively correlated with disease severity (crude odds ratio: 3.643, 95% CI: 1.218-10.896, P<0.05). CONCLUSIONS Our report illustrates that critically or severely ill patients have an associated high D-dimer value and high Padua score, and illustrates that a low threshold to screen for DVT may help improve detection of thromboembolism in these groups of patients, especially in asymptomatic patients. Our results suggest that early administration of prophylactic anticoagulant would benefit the prognosis of critical patients with COVID-19 pneumonia and would likely reduce thromboembolic rates.

Prediction of the Severity of the Coronavirus Disease and Its Adverse Clinical Outcomes.

This study aims to investigate blood and biochemical laboratory findings in patients with severe coronavirus disease 2019 (COVID-19) and to develop a joint predictor for predicting the likelihood of severe COVID-19 and its adverse clinical outcomes and to provide more information for treatment. We collected the data of 88 patients with laboratory-confirmed COVID-19. Further, the patients were divided into a non-severe group and a critical group (including critically ill cases). Univariate analysis showed that the absolute lymphocyte count, albumin level, albumin/globulin ratio, lactate dehydrogenase level, interleukin-6 (IL-6) level, erythrocyte count, globulin level, blood glucose level, and age were significantly correlated with the severity of COVID-19. The multivariate binary logistic regression model revealed that age, absolute lymphocyte count, and IL-6 level were independent risk factors in patients with COVID-19. The receiver operating characteristic curve revealed that the combination of IL-6 level, absolute lymphocyte count, and age is superior to a single factor as predictors for severe COVID-19, regardless of whether it is in terms of the area under the curve or the prediction sensitivity and specificity. Early application is beneficial to early identification of critically ill patients and timing individual treatments to reduce mortality.

Prediction of adverse clinical outcomes in patients with coronavirus disease 2019.

OBJECTIVE: This study aims to investigate blood and biochemical laboratory findings in patients with coronavirus disease (COVID-19) and analyze the potential predictors of poor outcome in patients with COVID-19. METHODS: The clinical, laboratory, and outcome data of 87 patients with COVID-19 were collected and retrospectively analyzed. Only data collected at the time of admission were used in the analysis for predictors of poor outcome. These patients were divided into two groups: the adverse prognosis group (36 patients) and the non-adverse prognosis group (51 patients). The adverse prognosis of COVID-19 patients was defined as admission to the intensive care unit or death. RESULTS: On the univariate analysis, age, white blood cell (WBC) count, neutrophil counts, lymphocytes count, neutrophils-to-lymphocytes ratio (NLR), interleukin-6, albumin-to-globulin ratio (AGR), albumin, lactate dehydrogenase, glutamyl transpeptidase, and blood glucose were found to be the significant predictors. On the multivariate analysis, the predictors of poor outcome of patients with COVID-19 were NLR (OR = 2.741, [95% CI = 1.02 ~ 7.35], P = .045) and IL-6 (OR = 1.405, [95% CI = 1.04 ~ 1.89, P = .025]). The receiver operating characteristic (ROC) curve revealed that the AUC of NLR, interleukin-6, pneumonia severity index (PSI) score, and Confusion-Urea-Respiratory Rate-Blood pressure-65 (CURB-65) score were 0.883, 0.852, 0.824, and 0.782, respectively. CONCLUSION: High interleukin-6 (6 pg/mL, cuff value) and NLR (4.48, cuff value) can be used to predict poor outcomes in patients with COVID-19 on admission, thus can serve as a beneficial tool for timely identifying COVID-19 patients prone to poor outcome and reduce patient mortality through early intervention.