Background: Gram-negative bacteremia is a major cause of morbidity and mortality in hospitalized patients. Data to guide the duration of antibiotic therapy are limited.Methods: This was a randomized, multicenter, open-label, noninferiority trial. Inpatients with gram-negative bacteremia, who were afebrile and hemodynamically stable for at least 48 hours, were randomized to receive 7 days (intervention) or 14 days (control) of covering antibiotic therapy. Patients with uncontrolled focus of infection were excluded. The primary outcome at 90 days was a composite of all-cause mortality; relapse, suppurative, or distant complications; and readmission or extended hospitalization (>14 days). The noninferiority margin was set at 10%.Results: We included 604 patients (306 intervention, 298 control) between January 2013 and August 2017 in 3 centers in Israel and Italy. The source of the infection was urinary in 411 of 604 patients (68%); causative pathogens were mainly Enterobacteriaceae (543/604 [90%]). A 7-day difference in the median duration of covering antibiotics was achieved. The primary outcome occurred in 140 of 306 patients (45.8%) in the 7-day group vs 144 of 298 (48.3%) in the 14-day group (risk difference, –2.6% [95% confidence interval, –10.5% to 5.3%]). No significant differences were observed in all other outcomes and adverse events, except for a shorter time to return to baseline functional status in the short-course therapy arm.Conclusions: In patients hospitalized with gram-negative bacteremia achieving clinical stability before day 7, an antibiotic course of 7 days was noninferior to 14 days. Reducing antibiotic treatment for uncomplicated gram-negative bacteremia to 7 days is an important antibiotic stewardship intervention.Clinical Trials Registration: NCT01737320.

Sepsis, a critical condition marked by systemic inflammation, profoundly impacts both innate and adaptive immunity, often resulting in lymphopenia. This immune alteration can spare regulatory T cells (Tregs) but significantly affects other lymphocyte subsets, leading to diminished effector functions, altered cytokine profiles, and metabolic changes. The complexity of sepsis stems not only from its pathophysiology but also from the heterogeneity of patient responses, posing significant challenges in developing universally effective therapies. This review emphasizes the importance of phenotyping in sepsis to enhance patient-specific diagnostic and therapeutic strategies. Phenotyping immune cells, which categorizes patients based on clinical and immunological characteristics, is pivotal for tailoring treatment approaches. Flow cytometry emerges as a crucial tool in this endeavor, offering rapid, low cost and detailed analysis of immune cell populations and their functional states. Indeed, this technology facilitates the understanding of immune dysfunctions in sepsis and contributes to the identification of novel biomarkers. Our review underscores the potential of integrating flow cytometry with omics data, machine learning and clinical observations to refine sepsis management, highlighting the shift towards personalized medicine in critical care. This approach could lead to more precise interventions, improving outcomes in this heterogeneously affected patient population.

Aims: The aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospitalized patients with COVID-19 pneumonia. Methods: This was an observational prospective study that comprised consecutive patients with COVID-19 pneumonia admitted to hospital from 21 February to 6 April 2020. The patients’ medical history, demographic, epidemiologic and clinical data were collected in an electronic patient chart. The dataset was used to train predictive models using an established machine learning framework leveraging a hybrid approach where clinical expertise is applied alongside a data-driven analysis. The study outcome was the onset of moderate to severe respiratory failure defined as PaO₂/FiO₂ ratio <150 mmHg in at least one of two consecutive arterial blood gas analyses in the following 48 hours. Shapley Additive exPlanations values were used to quantify the positive or negative impact of each variable included in each model on the predicted outcome. Results: A total of 198 patients contributed to generate 1068 usable observations which allowed to build 3 predictive models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth “boosted mixed model” included 20 variables was selected from the model 3, achieved the best predictive performance (AUC = 0.84) without worsening the FN rate. Its clinical performance was applied in a narrative case report as an example. Conclusion: This study developed a machine model with 84% prediction accuracy, which is able to assist clinicians in decision making process and contribute to develop new analytics to improve care at high technology readiness levels.

The coronavirus disease 2019 (COVID-19)-pandemic-related overload of health systems has compromised the application of antimicrobial stewardship (AS) models and infection prevention and control (IPC) programs. We aimed to evaluate the impact of COVID-19 on antimicrobial consumption (AC) and antimicrobial resistance (AMR) in the University Hospital of Modena. A time series analysis with an autoregressive integrated moving average model was conducted from January 2015 to October 2021 to evaluate the AC in the whole hospital and the intensive care unit (ICU), the incidence density (ID) of bloodstream infections (BSIs) due to the main multidrug-resistant organisms, and of C. difficile infections (CDIs). After an initial peak during the COVID-19 period, a decrease in the trend of AC was observed, both at the hospital (CT: −1.104, p = 0.025) and ICU levels (CT: −4.47, p = 0.047), with no significant difference in the single classes. Among the Gram-negative isolates, we observed a significant increase only in the level of BSIs due to carbapenem-susceptible Pseudomonas aeruginosa (CL: 1.477, 95% CI 0.130 to 2.824, p = 0.032). Considering Gram-positive bacteria, an increase in the level of BSIs due to methicillin-resistant Staphylococcus aureus and in the trend of CDIs were observed, though they did not reach statistical significance (CL: 0.72, 95% CI −0.039 to 1.48, p = 0.062; CT: 1.43, 95% CI −0.002 to 2.863, p = 0.051; respectively). Our findings demonstrated that the increases in AMR and AC that appeared in the first COVID-19 wave may be later controlled by restoring IPC and AS programs to pre-epidemic levels. A coordinated healthcare effort is necessary to address the longer-term impact of COVID-19 on AC to avoid irreversible consequences on AMR.