A comparison of the effectiveness of different doses of tocilizumab and sarilumab in the treatment of severe COVID-19: a natural experiment due to drug shortages

Objectives IL-6 inhibitors are administered to treat hospitalized COVID-19 patients. In 2021, due to shortages, different dosing regimens of tocilizumab, and a switch to sarilumab, were consecutively implemented. Using real world data, we compare the effectiveness of these IL-6 inhibitors. Methods Hospitalized COVID-19 patients, treated with IL-6 inhibitors, were included in this natural-experiment study. Sixty-day survival, hospital- and ICU length of stay and progression to ICU or death were compared between 8 mg/kg tocilizumab, fixed dose tocilizumab, low dose tocilizumab and fixed dose sarilumab treatment groups. Results 5485 patients from 49 hospitals were included. After correction for confounding, increased hazard ratios for 60-day mortality were observed for fixed dose tocilizumab (HR 1.20, 95% CI 1.04-1.39), low dose tocilizumab (HR 1.12, 95% CI 0.97-1.31) and sarilumab (HR 1.24, 95% CI 1.08-1.42), all relative to 8 mg/kg. The 8 mg/kg dosing regimen had lower odds for progression to ICU or death. Both hospital- and ICU length of stay were shorter for low dose tocilizumab than for the 8 mg/kg group. Conclusions We found differences in the probability of 60-day survival and the incidence of the combined outcome of mortality or ICU admission, mostly favoring 8 mg/kg tocilizumab. Because of potential time associated residual confounding, further clinical studies are warranted.

A comparison of the effectiveness of different doses of tocilizumab and sarilumab in the treatment of severe COVID-19: a natural experiment due to drug shortages.

OBJECTIVES: Interleukin (IL)-6 inhibitors are administered to treat patients hospitalized with COVID-19. In 2021, due to shortages, different dosing regimens of tocilizumab, and a switch to sarilumab, were consecutively implemented. Using real-world data, we compare the effectiveness of these IL-6 inhibitors. METHODS: Hospitalized patients with COVID-19, treated with IL-6 inhibitors, were included in this natural experiment study. Sixty-day survival, hospital- and intensive care unit (ICU) length of stay, and progression to ICU or death were compared between 8 mg/kg tocilizumab, fixed-dose tocilizumab, low-dose tocilizumab, and fixed-dose sarilumab treatment groups. RESULTS: A total of 5485 patients from 49 hospitals were included. After correction for confounding, increased hazard ratios (HRs) for 60-day mortality were observed for fixed-dose tocilizumab (HR 1.20, 95% confidence interval [CI] 1.04-1.39), low-dose tocilizumab (HR 1.12, 95% CI 0.97-1.31), and sarilumab (HR 1.24, 95% CI 1.08-1.42), all relative to 8 mg/kg. The 8 mg/kg dosing regimen had lower odds of progression to ICU or death. Both hospital- and ICU length of stay were shorter for low-dose tocilizumab than for the 8 mg/kg group. CONCLUSION: We found differences in the probability of 60-day survival and the incidence of the combined outcome of mortality or ICU admission, mostly favoring 8 mg/kg tocilizumab. Because of potential time-associated residual confounding, further clinical studies are warranted.

Outcome of COVID-19 in hospitalised immunocompromised patients: An analysis of the WHO ISARIC CCP-UK prospective cohort study.

BACKGROUND: Immunocompromised patients may be at higher risk of mortality if hospitalised with Coronavirus Disease 2019 (COVID-19) compared with immunocompetent patients. However, previous studies have been contradictory. We aimed to determine whether immunocompromised patients were at greater risk of in-hospital death and how this risk changed over the pandemic. METHODS AND FINDINGS: We included patients > = 19 years with symptomatic community-acquired COVID-19 recruited to the ISARIC WHO Clinical Characterisation Protocol UK prospective cohort study. We defined immunocompromise as immunosuppressant medication preadmission, cancer treatment, organ transplant, HIV, or congenital immunodeficiency. We used logistic regression to compare the risk of death in both groups, adjusting for age, sex, deprivation, ethnicity, vaccination, and comorbidities. We used Bayesian logistic regression to explore mortality over time. Between 17 January 2020 and 28 February 2022, we recruited 156,552 eligible patients, of whom 21,954 (14%) were immunocompromised. In total, 29% (n = 6,499) of immunocompromised and 21% (n = 28,608) of immunocompetent patients died in hospital. The odds of in-hospital mortality were elevated for immunocompromised patients (adjusted OR 1.44, 95% CI [1.39, 1.50], p < 0.001). Not all immunocompromising conditions had the same risk, for example, patients on active cancer treatment were less likely to have their care escalated to intensive care (adjusted OR 0.77, 95% CI [0.7, 0.85], p < 0.001) or ventilation (adjusted OR 0.65, 95% CI [0.56, 0.76], p < 0.001). However, cancer patients were more likely to die (adjusted OR 2.0, 95% CI [1.87, 2.15], p < 0.001). Analyses were adjusted for age, sex, socioeconomic deprivation, comorbidities, and vaccination status. As the pandemic progressed, in-hospital mortality reduced more slowly for immunocompromised patients than for immunocompetent patients. This was particularly evident with increasing age: the probability of the reduction in hospital mortality being less for immunocompromised patients aged 50 to 69 years was 88% for men and 83% for women, and for those >80 years was 99% for men and 98% for women. The study is limited by a lack of detailed drug data prior to admission, including steroid doses, meaning that we may have incorrectly categorised some immunocompromised patients as immunocompetent. CONCLUSIONS: Immunocompromised patients remain at elevated risk of death from COVID-19. Targeted measures such as additional vaccine doses, monoclonal antibodies, and nonpharmaceutical preventive interventions should be continually encouraged for this patient group. TRIAL REGISTRATION: ISRCTN 66726260.

Distinct clinical symptom patterns in patients hospitalised with COVID-19 in an analysis of 59,011 patients in the ISARIC-4C study.

COVID-19 is clinically characterised by fever, cough, and dyspnoea. Symptoms affecting other organ systems have been reported. However, it is the clinical associations of different patterns of symptoms which influence diagnostic and therapeutic decision-making. In this study, we applied clustering techniques to a large prospective cohort of hospitalised patients with COVID-19 to identify clinically meaningful sub-phenotypes. We obtained structured clinical data on 59,011 patients in the UK (the ISARIC Coronavirus Clinical Characterisation Consortium, 4C) and used a principled, unsupervised clustering approach to partition the first 25,477 cases according to symptoms reported at recruitment. We validated our findings in a second group of 33,534 cases recruited to ISARIC-4C, and in 4,445 cases recruited to a separate study of community cases. Unsupervised clustering identified distinct sub-phenotypes. First, a core symptom set of fever, cough, and dyspnoea, which co-occurred with additional symptoms in three further patterns: fatigue and confusion, diarrhoea and vomiting, or productive cough. Presentations with a single reported symptom of dyspnoea or confusion were also identified, alongside a sub-phenotype of patients reporting few or no symptoms. Patients presenting with gastrointestinal symptoms were more commonly female, had a longer duration of symptoms before presentation, and had lower 30-day mortality. Patients presenting with confusion, with or without core symptoms, were older and had a higher unadjusted mortality. Symptom sub-phenotypes were highly consistent in replication analysis within the ISARIC-4C study. Similar patterns were externally verified in patients from a study of self-reported symptoms of mild disease. The large scale of the ISARIC-4C study enabled robust, granular discovery and replication. Clinical interpretation is necessary to determine which of these observations have practical utility. We propose that four sub-phenotypes are usefully distinct from the core symptom group: gastro-intestinal disease, productive cough, confusion, and pauci-symptomatic presentations. Importantly, each is associated with an in-hospital mortality which differs from that of patients with core symptoms.

Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19.

The increasing body of literature describing the role of host factors in COVID-19 pathogenesis demonstrates the need to combine diverse, multi-omic data to evaluate and substantiate the most robust evidence and inform development of therapies. Here we present a dynamic ranking of host genes implicated in human betacoronavirus infection (SARS-CoV-2, SARS-CoV, MERS-CoV, seasonal coronaviruses). We conducted an extensive systematic review of experiments identifying potential host factors. Gene lists from diverse sources were integrated using Meta-Analysis by Information Content (MAIC). This previously described algorithm uses data-driven gene list weightings to produce a comprehensive ranked list of implicated host genes. From 32 datasets, the top ranked gene was PPIA, encoding cyclophilin A, a druggable target using cyclosporine. Other highly-ranked genes included proposed prognostic factors (CXCL10, CD4, CD3E) and investigational therapeutic targets (IL1A) for COVID-19. Gene rankings also inform the interpretation of COVID-19 GWAS results, implicating FYCO1 over other nearby genes in a disease-associated locus on chromosome 3. Researchers can search and review the gene rankings and the contribution of different experimental methods to gene rank at https://baillielab.net/maic/covid19 . As new data are published we will regularly update the list of genes as a resource to inform and prioritise future studies.