Comparison of anakinra and tocilizumab in management of severe COVID-19: a retrospective cohort study.

BACKGROUND: Studies regarding effectiveness of anakinra and tocilizumab treatments in coronavirus disease 2019 (COVID-19) have contradictory results. Furthermore, there is scarce comparative data regarding superiority of any agent. To further elucidate any superiority between these two agents, we retrospectively investigated and compared outcomes in hospitalized COVID-19 patients of our inpatient cohort who received anakinra or tocilizumab. METHODS: This study was designed as a single-center, retrospective, cross-sectional cohort study. Hospitalized patients with confirmed diagnosis of COVID-19 who had Brescia-COVID respiratory severity scale score ≥3 and hyperinflammation (defined as elevation of C reactive protein ≥50 g/L or ferritin ≥700 ng/mL) and received anakinra or tocilizumab in addition to standard care were enrolled in the study. Length of hospital stay after initiation of antiinflammatory treatment, need for mechanical ventilation, need for intensive care unit admission, mortality were set as primary outcomes and compared between tocilizumab and anakinra recipients after propensity score matching. RESULTS: One hundred and six patients were placed in each group after propensity score matching. In the anakinra group, relative risk reduction for intensive care unit admission was 50% when compared to the tocilizumab group and the number needed to treat to avert an intensive care unit admission was 3 (95% CI, 2-5). In terms of mortality, a 52% relative risk reduction was observed with anakinra treatment and the number needed to treat to avert an intensive care unit admission was 8 (95% CI, 4-50). Significantly more patients were observed to receive glucocorticoids in the anakinra group. DISCUSSION: Anakinra administration in severe COVID-19 patients was significantly associated with better survival and greater clinical improvement compared to the tocilizumab administration in our study. Increased rate of glucocorticoid use in the anakinra group might have contributed to better outcomes.

Hydroxychloroquine shortened hospital stay and reduced intensive care unit admissions in hospitalized COVID-19 patients.

INTRODUCTION: Effectiveness of hydroxychloroquine against SARS-CoV-2 has been highly controversial. In our research, we aimed to investigate the effects of hydroxychloroquine on disease outcomes in hospitalized patients with COVID-19. METHODOLOGY: A total of 393 hospitalized patients with COVID-19 were retrospectively assigned to the standard of care therapy group (n = 180) or the standard of care plus hydroxychloroquine group (n = 213). The standard of care therapy comprised favipiravir, low molecular weight heparin, acetylsalicylic acid. Status of oxygenation at baseline and on the seventh day, laboratory tests at baseline and at discharge were recorded. Length of hospital stay, administration of anti-inflammatory treatment, admission to the intensive care unit and 28th day mortality were set as primary endpoints. RESULTS: There were no statistically significant differences between groups in terms of oxygen delivery route and mortality after seven days of treatment (p = 0.592). C-reactive protein levels of the standard of care plus hydroxychloroquine group were significantly lower than that of the standard of care group at discharge (p = 0.034). Patients in the standard of care plus hydroxychloroquine group had shorter hospital stay (p = 0.007). The standard of care plus hydroxychloroquine group was favored over standard of care group in terms of rate of intensive care unit admissions (21.7% vs. 10.8%; relative risk with 95% CI = 0.49 [0.31-0.80], p = 0.003). CONCLUSIONS: Hydroxychloroquine in addition to standard of care was associated with less intensive care unit admissions, early discharge and greater C-reactive protein reduction. There was no difference in 28-day mortality.

Development and validation of nomogram to predict severe illness requiring intensive care follow up in hospitalized COVID-19 cases.

BACKGROUND: Early identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values. METHODS: Patients hospitalized with laboratory-confirmed COVID-19 between March 15, 2020, and June 15, 2020, were enrolled in this retrospective study with 35 variables obtained upon admission considered. Univariate and multivariable logistic regression models were constructed to select potential predictive parameters using 1000 bootstrap samples. Afterward, a nomogram was developed with 5 variables selected from multivariable analysis. The nomogram model was evaluated by Area Under the Curve (AUC) and bias-corrected Harrell's C-index with 95% confidence interval, Hosmer-Lemeshow Goodness-of-fit test, and calibration curve analysis. RESULTS: Out of a total of 1022 patients, 686 cases without missing data were used to construct the nomogram. Of the 686, 104 needed ICU follow-up. The final model includes oxygen saturation, CRP, PCT, LDH, troponin as independent factors for the prediction of need for ICU admission. The model has good predictive power with an AUC of 0.93 (0.902-0.950) and a bias-corrected Harrell's C-index of 0.91 (0.899-0.947). Hosmer-Lemeshow test p-value was 0.826 and the model is well-calibrated (p = 0.1703). CONCLUSION: We developed a simple, accessible, easy-to-use nomogram with good distinctive power for severe illness requiring ICU follow-up. Clinicians can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up by using clinical and laboratory values of patients available upon admission.

Which hematological markers have predictive value as early indicators of severe COVID-19 cases in the emergency department?

Background/aim: Coronavirus 2019 disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a pandemic infectious disease that causes morbidity and mortality. As a result of high mortality rate among the severe COVID-19 patients, the early detection of the disease stage and early effective interventions are very important in reducing mortality. Hence, it is important to differentiate severe and nonsevere cases from each other. To date, there are no proven diagnostic or prognostic parameters that can be used in this manner. Due to the expensive and not easily accessible tests that are performed for COVID-19, researchers are investigating some parameters that can be easily used. In some recent studies, hematological parameters have been evaluated to see if they can be used as predictive parameters. Materials and methods: In the current study, almost all hematological parameters were used, including the neutrophil/lymphocyte ratio, platelet/lymphocyte ratio, monocyte/lymphocyte ratio, mean platelet volume to lymphocyte ratio, mean platelet volume to platelet ratio, plateletcrit, and D-dimer/fibrinogen ratio, neutrophil/lymphocyte/platelet scoring system, and systemic immune-inflammation index. A total of 750 patients, who were admitted to Ankara City Hospital due to COVID-19, were evaluated in this study. The patients were classified into 2 groups according to their diagnosis (confirmed or probable) and into 2 groups according to the stage of the disease (nonsevere or severe). Results: The values of the combinations of inflammatory markers and other hematological parameters in all of the patients with severe COVID-19 were calculated, and the predicted values of these parameters were compared. According to results of the study, nearly all of the hematological parameters could be used as potential diagnostic biomarkers for subsequent analysis, because the area under the curve (AUC) was higher than 0.50, especially for the DFR and NLR, which had the highest AUC among the parameters. Conclusion: Our findings indicate that, the parameters those enhanced from complete blood count, which is a simple laboratory test, can help to identify and classify COVID-19 patients into non-severe to severe groups.