What did we learn about tocilizumab use against COVID-19? A single-center observational study from an intensive care unit in Serbia
Bibliographic record
Abstract
Background Selection of effective and safe therapy for management of patients with coronavirus disease is challenging. Tocilizumab (TZB) has emerged as a potential treatment option for COVID-19. Several aspects regarding Tocilizumab treatment remain uncertain, such as the optimal timing for its administration and the safety profile, including the potential risk of infections. The aim of the study is to present the clinical characteristics of patients with COVID-19 following the application of Tocilizumab. Methods This is a retrospective analysis of 121 patients with severe forms of COVID-19 previously treated with Tocilizumab was conducted. All patients were admitted to intensive care units (ICUs). Results Of 121 patients, the majority were men 72 (59.5%) with a median age at presentation of 65 ± 13 years. Only 9 (7.43%) patients were without comorbidities, while the other 112 (92.55%) had two or more comorbidities. Almost all of the 120 patients (99.2%) needed oxygen therapy, such as nasal cannulas in 110 (90.9%) patients, high flow nasal catheter (HFNC) in 4 (3.3%) patients, and continuous positive airway pressure (CPAP) in 5 (4.1%) patients while 1 patient was intubated at the time of hospital admission. The average time from Tocilizumab application to admission to the ICU was 3 days. During clinical deterioration, almost half 57 (47.1%) of the patients were intubated, and 52 (82.5%) of these intubated patients (p < 0.001) had lethal outcomes. The most significant predictors for a lethal outcome according to multivariate analysis were diabetes mellitus (p < 0.001) followed by a subsequent elevation in C-reactive protein levels (CRP; p < 0.002) and ferritin (p < 0.013) after Tocilizumab application. Bloodstream infections were found in 20 (16.5%) patients, most frequently with Gram-negative pathogens like Acinetobacter spp. as in 12 (18.6%) patients, Klebsiella spp. in 6 (8%) patients, and Pseudomonas spp. in 2 (3.2%) patients. Urine culture isolates were found in 9 (7.43%) patients, with Candida spp. being most frequently isolated in 7 (5.8%) patients, followed by Klebsiella spp. and Pseudomonas spp. in 1 patient each (0.8%). Significantly lower survival was seen in patients with proven infection. Conclusion The benefit of tocilizumab was not found in our study. The high mortality rate among intubated patients after Tocilizumab use suggests appropriate patient selection and monitoring and emphasizes the risk of superinfections. Diabetes mellitus, increased levels of CRP, and ferritin were identified as the most significant predictors of poor outcomes in contrast to increased levels of IL-6.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".