Targeting Cytokine Storm in COVID-19: A Role of Online Hemodiafiltration with Asymmetric Cellulose Triacetate in Maintenance Hemodialysis Patients-A Report of 10 Cases

Inflammatory reflex disruption in COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified in Wuhan, China, in late 2019 and caused coronavirus disease 2019 (COVID-19), which is still a global pandemic. In most infected people, SARS-CoV-2 can only cause moderate symptoms, while in other patients, it leads to severe illness and eventually death. Although the main clinical manifestation of COVID-19 is often seen in the lungs, this disease affects almost all body organs. The excessive and prolonged release of inflammatory cytokines that may occur in COVID-19 patients, known as cytokine storms, stimulates undesired immune responses and can cause various tissues damage. In the current review article, we focus on the potential advantages of the intrinsic cholinergic anti-inflammatory pathway (CAP) as the efferent arm of inflammatory reflex in COVID-19 management. Considering this endogenous protective mechanism against chronic inflammation, we focused on the effects of SARS-CoV-2 in the destruction of this anti-inflammatory system. Several studies indicated the interaction of SARS-CoV-2 with the alpha7 subtype of the nicotinic acetylcholine receptor as the effector molecule of the inflammatory reflex. On the other hand, neurological manifestations have increasingly been identified as significant extrapulmonary manifestations of COVID-19. The rational connection between these findings and COVID-19 pathogenesis may be an important issue in both our understanding and dealing with this disease. COVID-19 is deeply rooted in our daily life and requires an urgent need for the establishment of effective therapeutic options, and all the possible treatments must be considered for the control of such inflammatory conditions.

Can systemic immune inflammation index at admission predict in-hospital mortality in chronic kidney disease patients with SARS-CoV-2 infection?

BACKGROUND AND AIM

Patients with chronic kidney disease (CKD) are susceptible to SARS-CoV-2 infection and more prone to develop severe disease. It is important to know predictors of poor outcomes to optimize the strategies of care.

METHODS

93 patients with CKD and 93 age-sex matched patients without CKD were included in the study. Data on demographic, clinical features, hematological indices and outcomes were noted and compared between the groups. Neutrophile to lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune inflammation index (SII) (platelet counts×neutrophil counts/lymphocyte counts) and lymphocyte-to-CRP ratio (LCR) were calculated on admission and the association of these markers with disease mortality in CKD patients was identified.

RESULTS

CKD patients had higher risk of severe disease, and mortality compared to non-CKD patients (72% vs 50.5%, p=0.003, 36.6% vs 10.8%, p<0.001, respectively) and were more likely to have higher values of immuno-inflammatory indices (leukocyte count, neutrophil, NLR, SII and C-reactive protein, etc.) and lower level of lymphocyte and LCR. Also, higher levels of NLR, SII, PLR and lower level of LCR were seen in CKD patients who died compared to those recovered. In a receiver operating characteristic curve analysis, NLR, SII, PLR and LCR area under the curve for in-hospital mortality of CKD patients were 0.830, 0.811, 0.664 and 0.712, respectively. Among all parameters, NLR and SII gave us the best ability to distinguish patients with higher risk of death. Based on the cut-off value of 1180.5, the sensitivity and specificity of the SII for predicting in-hospital mortality were found to be 67.5% and 79.6%, respectively. The corresponding sensitivity and specificity of the NLR were 85.2% and 66.1%, respectively, at the cut-off value of 5.1. Forward stepwise logistic regression analysis showed that NLR (≥5.1), SII (≥1180.5) and LCR (≤9) were predictors for in-hospital mortality.

CONCLUSION

We report for the first time that SII is able to distinguish COVID-19 infected CKD patients of worse survival and it is as powerful as NLR in this regard. As SII is easily quantified from blood sample data, it may assist for early identification and timely management of CKD patients with worse survival.

[Can systemic immune inflammation index at admission predict in-hospital mortality in chronic kidney disease patients with SARS-CoV-2 infection]

BACKGROUND AND AIM

Patients with chronic kidney disease (CKD) are susceptible to SARS-CoV-2 infection and more prone to develop severe disease. It is important to know predictors of poor outcomes to optimize the strategies of care.

METHODS

93 patients with CKD and 93 age-sex matched patients without CKD were included in the study. Data on demographic, clinical features, hematological indices and outcomes were noted and compared between the groups. Neutrophile to lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune inflammation index (SII) (platelet counts × neutrophil counts / lymphocyte counts) and lymphocyte-to-CRP ratio (LCR) were calculated on admission and the association of these markers with disease mortality in CKD patients was identified.

RESULTS

CKD patients had higher risk of severe disease, and mortality compared to non-CKD patients (72% vs 50.5%, p=0.003, 36,6% vs 10.8%, p<0.001, respectively) and were more likely to have higher values of immuno-inflammatory indices (leucocyte count, neutrophil, NLR, SII and C-reactive protein etc.) and lower level of lymphocyte and LCR. Also, higher levels of NLR, SII, PLR and lower level of LCR were seen in CKD patients who died compared to those recovered. In a receiver operating characteristic curve analysis, NLR, SII, PLR and LCR area under the curve for in-hospital mortality of CKD patients were 0.830, 0.811, 0.664 and 0.712, respectively. Among all parameters, NLR and SII gave us the best ability to distinguish patients with higher risk of death. Based on the cut-off value of 1180.5, the sensitivity and specificity of the SII for predicting in-hospital mortality were found to be 67.5% and 79.6%, respectively. The corresponding sensitivity and specificity of the NLR were 85.2% and 66.1%, respectively, at the cut-off value of 5.1. Forward stepwise logistic regression analysis showed that NLR (≥5.1), SII (≥1180.5) and LCR (≤9) were predictors for in-hospital mortality.

CONCLUSION

We report for the first time that SII is able to distinguish COVID-19 infected CKD patients of worse survival and it is as powerful as NLR in this regard. As SII is easily quantified from blood sample data, it may assist for early identification and timely management of CKD patients with worse survival.