Severe Adaptive Immune Suppression May Be Why Patients With Severe COVID-19 Cannot Be Discharged From the ICU Even After Negative Viral Tests.

During the COVID-19 pandemic, a phenomenon emerged in which some patients with severe disease were critically ill and could not be discharged from the ICU even though they exhibited negative viral tests. To explore the underlying mechanism, we collected blood samples from these patients and analyzed the gene expression profiles of peripheral immune cells. We found that all enrolled patients, regardless of changes in genes related to different symptoms and inflammatory responses, showed universally and severely decreased expression of adaptive immunity-related genes, especially those related to T/B cell arms and HLA molecules, and that these patients exhibited long-term secondary infections. In addition, no significant change was found in the expression of classic immunosuppression molecules including PD-1, PD-L1, and CTLA-4, suggesting that the adaptive immune suppression may not be due to the change of these genes. According to the published literatures and our data, this adaptive immunosuppression is likely to be caused by the "dysregulated host response" to severe infection, similar to the immunosuppression that exists in other severely infected patients with sepsis.

Association between glucocorticoids treatment and viral clearance delay in patients with COVID-19: a systematic review and meta-analysis.

BACKGROUND: Evidence of glucocorticoids on viral clearance delay of COVID-19 patients is not clear. METHODS: In this systematic review and meta-analysis, we searched for studies on Medline, Embase, EBSCO, ScienceDirect, Web of Science, Cochrane Library, and ClinicalTrials.gov from 2019 to April 20, 2021. We mainly pooled the risk ratios (RRs) and mean difference (MD) for viral clearance delay and did subgroup analyses by the severity of illness and doses of glucocorticoids. RESULTS: 38 studies with a total of 9572 patients were identified. Glucocorticoids treatment was associated with delayed viral clearance in COVID-19 patients (adjusted RR 1.52, 95% CI 1.29 to 1.80, I2 = 52%), based on moderate-quality evidence. In subgroup analyses, risk of viral clearance delay was significant both for COVID-19 patients being mild or moderate ill (adjusted RR 1.86, 95% CI 1.35 to 2.57, I2 = 48%), and for patients of being severe or critical ill (adjusted RR 1.59, 95% CI 1.23 to 2.07, I2 = 0%); however, this risk significantly increased for patients taking high doses (unadjusted RR 1.85, 95% CI 1.08 to 3.18; MD 7.19, 95% CI 2.78 to 11.61) or medium doses (adjusted RR 1.86, 95% CI 0.96 to 3.62, I2 = 45%; MD 3.98, 95% CI 3.07 to 4.88, I2 = 4%), rather those taking low doses (adjusted RR 1.38, 95% CI 0.94 to 2.02, I2 = 59%; MD 1.46, 95% CI -0.79 to 3.70, I2 = 82%). CONCLUSIONS: Glucocorticoids treatment delayed viral clearance in COVID-19 patients of taking high doses or medium doses, rather in those of taking low doses of glucocorticoids.

The neutrophil to lymphocyte ratio is an independent predictor for severe COVID-19 : Evidence from a multicenter case-control study and meta-analyses.

PURPOSE: The aim of this study was to determine whether the neutrophil to lymphocyte ratio (NLR) can predict severe Coronavirus disease 2019 (COVID-19). PATIENTS AND METHODS: A multicenter case-control study was conducted to investigate whether the NLR can help predict the severity of COVID-19. Patients confirmed to have COVID-19 between 16 January 2020 and 15 March 2020 were enrolled. Furthermore, meta-analyses were conducted based on both previous studies and our case-control study. RESULTS: In the case-control study, 213 patients (severe: 81) were included. The results suggested that the NLR was an independent risk factor (odds ratio [OR], 1.155, 95% confidence interval [95% CI]: 1.043-1.279, P = 0.006) and a great predictor (the area under the ROC curve was 0.728, 95% CI: 0.656-0.800) for severe COVID-19. In total, 18 datasets from 16 studies combined with our case-control study (severe: 1211; non-severe: 5838) were included in the meta-analyses and the results showed that the NLR of the severe COVID-19 group was significantly higher than that of the non-severe group (SMD = 1.10, 95% CI: 0.90-1.31, P < 0.001). Based on the 2â€¯× 2 data from 6 studies, the SROC of NLR for predicting severe COVID-19 was 0.802, with a sensitivity of 0.67 (95% CI: 0.61-0.72) and a specificity of 0.75 (95% CI: 0.73-0.78). CONCLUSION: Based on a multicenter case-control study and a meta-analysis, we found that the initial NLR was a great predictor of severe COVID-19.

Comparison of Associations Between Glucocorticoids Treatment and Mortality in COVID-19 Patients and SARS Patients: A Systematic Review and Meta-Analysis.

BACKGROUND: The response to glucocorticoids treatment may be different between coronavirus disease 2019 (Covid-19) and severe acute respiratory syndrome (SARS). METHODS: In this systematic review and meta-analysis, we searched studies on Medline, Embase, EBSCO, ScienceDirect, Web of Science, Cochrane Library, ClinicalTrials.gov, International Clinical Trials Registry Platform from 2002 to October 7, 2020. We used fixed-effects and random-effects models to compute the risk ratio of death in the group receiving glucocorticoids treatment and the control group for COVID-19 and SARS, respectively. RESULTS: Ten trials and 71 observational studies, with a total of 45,935 patients, were identified. Glucocorticoids treatment was associated with decreased all-cause mortality both in COVID-19 (risk ratio, 0.88; 95% confidence interval, 0.82-0.94; I2 = 26%) and SARS (0.48; 0.29-0.79; 10%), based on high-quality evidence, as well as decreased all-cause mortality-including composite outcome of COVID-19 (0.89; 0.82-0.98; 0%). In subgroup analyses, all-cause mortality was significantly lower among COVID-19 patients being accompanied by severe ARDS but not mild ARDS, taking low-dose or pulse glucocorticoids, being critically severe but not only severe, being of critical severity and old but not young, being of critical severity and men but not women, non-early taking glucocorticoids, taking dexamethasone or methylprednisolone, and with the increased inflammatory state; but for SARS, lower mortality was observed among those who were taking medium-high dose glucocorticoids, being severe or critically severe, early taking glucocorticoids, and taking methylprednisolone or prednisolone. CONCLUSIONS: Glucocorticoids treatment reduced mortality in COVID-19 and SARS patients of critical severity; however, different curative effects existed between the two diseases among subpopulations, mainly regarding sex- and age-specific effects, optimal doses, and use timing of glucocorticoids.

[Annual progress in critical care medicine in 2020].

The epidemic of coronavirus disease 2019 (COVID-19) puts higher demands on critical care medicine. Lots of studies have been conducted to solve COVID-19-related problems. Therefore, we reviewed the annual progress for COVID-19-related issues including antivirals threapies, respiratory support and immunomodulatory therapies and other critical issues, including the effect of antibiotic on mitochondrial damage and its relationship with sepsis, the goal and direction of antimicrobial de-escalation, drug prophylaxis of constipation, bleeding in gastrointestinal disorders and management of critical illness in the informalization era and so on. We hope to provide reference for clinical and scientific research work of the intensivists.

Elevated cardiac biomarkers may be effective prognostic predictors for patients with COVID-19: A multicenter, observational study.

PURPOSE: The coronavirus disease 19 (COVID-19) has become a global health event. Cardiac biomarkers like creatine kinase isoenzyme (CK-MB), myoglobin, and high-sensitivity troponin T were usually elevated in early stages. This study aimed to investigate whether the elevated cardiac biomarkers could become effective prognostic predictors for COVID-19 patients. METHODS: The present study involved 357 COVID-19 patients. The potential predictors for two study outcomes (in-hospital death and recovery status) in 28 days were selected by LASSO regression analysis. Prognostic values of cardiac biomarkers selected were evaluated using the receiver operating characteristic curve (ROC) and the area under ROC (AUC). RESULTS: After 28-day follow-up, overall 357 patients were divided into death group (n = 25) and survival group (n = 332), or non-recovery group (n = 43) and recovery group (n = 314). The LASSO regression analysis showed elevated CK-MB and myoglobin were independent risk predictors for in-hospital death, and CK-MB and myoglobin were also independent risk predictors for non-recovery. The AUC of CK-MB and myoglobin for in-hospital death were 0.862 (95%CL: 0.804-0.920, p < 0.001) and 0.838 respectively (95%CL: 0.729-0.947, p < 0.001). The AUC of CK-MB and myoglobin for non-recovery were 0.839 (95%CL: 0.786-0.892, p < 0.001) and 0.841 (95%CL: 0.765-0.918, p < 0.001) respectively. We also found AUC of combined use of CK-MB and myoglobin for in-hospital death and non-recovery were 0.883 (95CL: 0.813-0.952, p < 0.001), and 0.873 (95%CL: 0.817-0.930, p < 0.001) respectively. CONCLUSIONS: In patients with COVID-19, elevated CK-MB and myoglobin on admission may be effective predictors for adverse outcomes, and combined use of CK-MB and myoglobin had a better performance for prediction.

The Prognostic Nutritional Index is associated with mortality of COVID-19 patients in Wuhan, China.

BACKGROUND: Declared as pandemic by WHO, the coronavirus disease 2019 (COVID-19) pneumonia has brought great damage to human health. The uncontrollable spread and poor progression of COVID-19 have attracted much attention from all over the world. We designed this study to develop a prognostic nomogram incorporating Prognostic nutritional index (PNI) in COVID-19 patients. METHODS: Patients confirmed with COVID-19 and treated in Renmin Hospital of Wuhan University from January to February 2020 were included in this study. We used logistic regression analysis to find risk factors of mortality in these patients. A prognostic nomogram was constructed and receiver operating characteristics (ROC) curve was drawn to evaluate the predictive value of PNI and this prognostic model. RESULTS: Comparison of baseline characteristics showed non-survivors had higher age (P < .001), male ratio (P = .038), neutrophil-to-lymphocyte ratio (NLR) (P < .001), platelet-to-lymphocyte ratio (PLR) (P < .001), and PNI (P < .001) than survivors. In the multivariate logistic regression analysis, independent risk factors of mortality in COVID-19 patients included white blood cell (WBC) (OR 1.285, P = .039), PNI (OR 0.790, P = .029), LDH (OR 1.011, P < .015). These three factors were combined to build the prognostic model. Area under the ROC curve (AUC) of only PNI and the prognostic model was 0.849 (95%Cl 0.811-0.888) and 0.950 (95%Cl 0.922-0.978), respectively. And calibration plot showed good stability of the prognostic model. CONCLUSION: This research indicates PNI is independently associated with the mortality of COVID-19 patients. Prognostic model incorporating PNI is beneficial for clinicians to evaluate progression and strengthen monitoring for COVID-19 patients.

Neutrophil-to-lymphocyte ratio as a predictive biomarker for moderate-severe ARDS in severe COVID-19 patients.

TRIAL REGISTRATION: ChiCTR, ChiCTR2000029758. Registered 12 February 2020 - Retrospectively registered.