Introduction While there are no pharmacological treatments with proven efficacy for coronavirus disease 2019 (COVID-19), tocilizumab has emerged as a candidate therapy. Some aspects of this therapy are still unknown, including the optimal timing of administration. Objective This observational study aimed to compare the 90-day mortality in two cohorts of patients when the drug was administered within the first 10 days from onset of symptoms or after day 11. Methods Patients hospitalised with severe COVID-19 pneumonia who had received tocilizumab were divided into two groups according to when the medication was administered. The primary outcome was 90-day mortality. Secondary outcomes were 30-day mortality, clinical improvement on a 6-item scale by day 6, biomarker improvement by day 6, radiological image improvement by day 10 and SaO₂ quotient by day 6. The results in the two groups were compared. Additionally, adverse events relating to tocilizumab were recorded. Results A total of 112 patients were analysed. Both groups were epidemiologically comparable. The results obtained in the primary efficacy variable of the study (90-day mortality) showed a statistically significant difference in the subgroups according to the time of administration of tocilizumab (18.6% vs 5.0%, p=0.048). There was clinical improvement in 24.1% of patients at 6 days, with similar behaviour in both subgroups. No statistically significant differences were found in the percentage of patients who achieved radiological improvement at 10 days or in the other inflammatory parameters, with the exception of significant reductions in lactate dehydrogenase and C-reactive protein. Administration of tocilizumab was not associated with relevant adverse events. Conclusion To our knowledge, this is the first report of data regarding the timing of administration of tocilizumab in patients with COVID-19 pneumonia. A strategy involving tocilizumab administration after 10 days from onset of symptoms may decrease mortality. Further randomised controlled trials are needed to confirm this emerging hypothesis.

Background and Objectives: Antimicrobial stewardship plays a key role in the surgical setting by reducing the incidence of healthcare-associated infections and limiting the emergence of antimicrobial resistance. Clinical Decision Support Systems (CDSSs), when integrated into routine practice, are valuable tools for optimizing antimicrobial prescribing. However, evidence regarding their impact on surgical patients, particularly across different specialties, remains limited. Materials and Methods: We conducted a quasi-experimental time series study in surgical patients at a primary-level hospital, evaluating the effect of a CDSS on postoperative antimicrobial therapy. The pre-intervention period included patients admitted from April 2017 to September 2020, and the post-intervention period included those admitted from October 2020 to March 2024. Antimicrobial consumption and expenditures were measured as defined daily doses (DDDs) per 1000 patient-days and euros (€) per 1000 patient-days, respectively. Subgroup analyses were performed by the surgical service. Clinical outcomes included mortality and length of stay (LOS). Results: Following CDSS implementation, overall antimicrobial consumption decreased by 4.4%. Significant reductions were observed in aminoglycosides (−52.0%), macrolides, lincosamides and streptogramins (−40.6%), and fluoroquinolones (−32.3%). Reductions were heterogeneous across surgical services, with significant reductions in Traumatology (−21.3%) and Urology (−14.3%). Expenditures decreased from 3185.4 to 2733.9€/1000 patient-days (−14.2%; p = 0.17). Mortality remained stable, whereas significant reductions in LOS were observed in Urology (5 to 4 days, p = 0.03) and traumatology (16 to 8.5 days, p < 0.01). During the post-intervention period, 476 stewardship recommendations were issued for 330 patients, with an acceptance rate of 76.1%. The most frequent interventions were discontinuation of antimicrobials (25.8%), transition to oral therapy (21.0%), and de-escalation (18.7%). Conclusions: Implementation of a CDSS in the surgical setting was associated with reduced antimicrobial consumption, a downward trend in expenditures, and high acceptance of stewardship recommendations. Mortality remained unchanged, while reductions in LOS in selected services support the safety and potential efficiency of this approach.

Background:Advances in oncology have led to the development of novel targeted therapies with demonstrated efficacy in clinical trials; however, their real-world economic impact prior to and after market introduction remains insufficiently characterized [1,2]. Cancer-related healthcare costs vary significantly depending on disease stage, time since diagnosis, tumor type, and therapeutic approach[3–6], making inter-hospital comparisons challenging due to heterogeneity in patient populations and information systems [7]. Therefore, integrating cost analysis with clinically meaningful patient stratification is essential to improve resource allocation and outcome evaluation[8–12]. Methods: A multicentre working group comprising four tertiary hospitals in Madrid (Spain) was established to develop and validate a novel classification system for adult oncohematological patients. A standardized methodology was designed to stratify patients into homogeneous groups (PATONCO categories) based on tumor location, therapeutic objective, and clinically relevant biomarkers. A cost indicator was defined as the average cost per patient per month for each PATONCO category. Data were extracted from pharmacy dispensing systems and analyzed using descriptive and inferential statistics, including Kruskal–Wallis and post hoc Dunn tests. Results: A total of 3,659 patients were included (3,168 oncology; 491 hematology), distributed across 62 programmes (54 oncology; 8 hematology). The PATONCOS tool enabled the identification and validation of a cost indicator (average cost/patient/month per category), allowing inter-hospital comparison. Significant differences in costs were observed across most high-prevalence categories, reflecting variability in therapeutic strategies and adoption of innovative treatments. The model demonstrated its capacity to detect intra-group homogeneity and inter-group variability, improving the identification of high-cost patient subgroups and supporting benchmarking across centres. Conclusions: The PATONCOS tool provides a novel, clinically oriented stratification methodology that integrates pharmacotherapy, biomarkers, and disease stage with economic evaluation. This approach enables more accurate comparisons of oncology treatment costs between institutions and supports data-driven decision-making in resource allocation. Its implementation may contribute to more sustainable healthcare systems by aligning clinical practice with economic outcomes.