Treatment effect of remdesivir on the mortality of hospitalised COVID-19 patients in Switzerland across different patient groups: a tree-based model analysis.

AIMS OF THE STUDY: Remdesivir has shown benefits against COVID-19. However, it remains unclear whether, to what extent, and among whom remdesivir can reduce COVID-19-related mortality. We explored whether the treatment response to remdesivir differed by patient characteristics. METHODS: We analysed data collected from a hospital surveillance study conducted in 21 referral hospitals in Switzerland between 2020 and 2022. We applied model-based recursive partitioning to group patients by the association between treatment levels and mortality. We included either treatment (levels: none, remdesivir within 7 days of symptom onset, remdesivir after 7 days, or another treatment), age and sex, or treatment only as regression variables. Candidate partitioning variables included a range of risk factors and comorbidities (and age and sex unless included in regression). We repeated the analyses using local centring to correct the results for the propensity to receive treatment. RESULTS: Overall (n = 21,790 patients), remdesivir within 7 days was associated with increased mortality (adjusted hazard ratios 1.28-1.54 versus no treatment). The CURB-65 score caused the most instability in the regression parameters of the model. When adjusted for age and sex, patients receiving remdesivir within 7 days of onset had higher mortality than those not treated in all identified eight patient groups. When age and sex were included as partitioning variables instead, the number of groups increased to 19-20; in five to six of those branches, mortality was lower among patients who received early remdesivir. Factors determining the groups where remdesivir was potentially beneficial included the presence of oncological comorbidities, male sex, and high age. CONCLUSIONS: Some subgroups of patients, such as individuals with oncological comorbidities or elderly males, may benefit from remdesivir.

[Epidemic forecasting and COVID-19]. / Prévisions épidémiques et Covid-19.

Since December 2019, the COVID-19 pandemic has had a major impact on global health and the economy. Epidemiological forecasts are crucial for governmental decisions, healthcare officials, and the general public. A collaboration between the Institute of Global Health at the University of Geneva and the Swiss Data Science Center created an interactive dashboard providing forecasts for over 200 countries and territories. This dashboard has been a valuable tool for the public and authorities alike. The pandemic has highlighted the importance of international collaborations and a robust national surveillance system. Data collection systems, pathogen-agnostic models, and communication tools need to be consolidated and maintained in operation.

Depuis décembre 2019, la pandémie de Covid-19 a eu un impact majeur sur la santé et l'économie mondiales. Les prévisions épidémiques sont essentielles pour les décisions gouvernementales, les responsables de la santé et le public. Un projet entre l'Institut de santé globale de l'Université de Genève et le Swiss Data Science Center a créé un tableau de bord interactif fournissant des prévisions pour plus de 200 pays et territoires, qui fut un outil précieux pour le public et les autorités. La pandémie a souligné l'importance des collaborations internationales et d'un système de surveillance national solide. Les systèmes de collecte de données, les modèles agnostiques aux pathogènes et les outils de communication doivent être consolidés et maintenus en fonctionnement.