Background: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling. Objective: We aimed to develop a machine learning–based score—the Piacenza score—for 30-day mortality prediction in patients with COVID-19 pneumonia. Methods: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients’ medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO2 ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naïve Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naïve Bayes algorithm with 14 features chosen a priori. Results: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naïve Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively. Conclusions: Our findings demonstrated that a customizable machine learning–based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia.

The progress of physicians through residency training in anesthesiology can be monitored using an online logbook. The aim of this investigation was to establish how residents record clinical activities in their computerized web-based logbooks during their first years of anesthesiology training. For this retrospective observational trial, the ESSE 3© digital registry of the University of Modena and Reggio Emilia, Italy was used to record all anesthesia-related activities performed by three consecutive year-groups of residents (Groups A, B and C) between 2009 and 2012. The ratio of activities to sessions was chosen as a surrogate measure of compliance. A total of 41,348 actions were analyzed. The ratio of activities to sessions showed a statistically significant decline for all activities concerning the perioperative management of anesthesia, with a steady reduction from the first to the last year-group (Group A 23.7, Group B 14.1 and Group C 2.2; p = 0.003). An online activities logbook is a useful tool for recording and assessing the clinical activities undertaken by each resident during residency training in anesthesiology.

The COVID-19 pandemic has worked as a catalyst, pushing governments, private companies, and healthcare facilities to design, develop, and adopt innovative solutions to control it, as is often the case when people are driven by necessity. After 18 months since the first case, it is time to think about the pros and cons of such technologies, including artificial intelligence—which is probably the most complex and misunderstood by non-specialists—in order to get the most out of them, and to suggest future improvements and proper adoption. The aim of this narrative review was to select the relevant papers that directly address the adoption of artificial intelligence and new technologies in the management of pandemics and communicable diseases such as SARS-CoV-2: environmental measures; acquisition and sharing of knowledge in the general population and among clinicians; development and management of drugs and vaccines; remote psychological support of patients; remote monitoring, diagnosis, and follow-up; and maximization and rationalization of human and material resources in the hospital environment.

Background: Sepsis is a life-threatening syndrome caused by a dysregulated host response to infection leading to organ dysfunction. Distinguishing sepsis from localized infection is crucial, as it guides clinical decision-making and biomarker interpretation. Biomarkers may support diagnosis, prognosis, and therapeutic choices, but their integration into practice remains debated. Methods: This narrative review was conducted in accordance with the SANRA (Scale for the Assessment of Narrative Review Articles) guidelines. A comprehensive literature search was performed in PubMed, Embase, and Cochrane CENTRAL (January 2000–September 2025). Studies evaluating sepsis-related biomarkers for diagnosis, prognostication, shock assessment, antimicrobial stewardship, and post-acute follow-up were considered. Findings: Established biomarkers such as procalcitonin (PCT), C-reactive protein (CRP), and lactate remain widely used for diagnosis, monitoring of inflammatory response, and assessment of severity. Emerging candidates include pancreatic stone protein (PSP), neutrophil gelatinase-associated lipocalin (NGAL), and monocyte HLA-DR (mHLA-DR), which may provide insights into infection dynamics, renal injury, and immune suppression, respectively. However, limitations in standardization and heterogeneous evidence hinder routine implementation. Interleukin-6 (IL-6), despite extensive study, shows limited specificity and inconsistent clinical applicability. Renin has been proposed as a marker of shock severity rather than infection. Comparative evidence highlights the need for stage-specific biomarker use across prehospital, emergency, ICU, and recovery phases. Conclusions: No single biomarker is universally applicable in sepsis. Their utility depends on timing, clinical setting, and patient phenotype. Combining classical and emerging biomarkers with point-of-care technologies and dynamic monitoring may enhance personalized management. Limitations include heterogeneity of evidence and lack of standardized thresholds. Future research should validate biomarker panels, integrate them into stewardship strategies, and explore their cost-effectiveness in clinical practice.

Introduction: The study aims to describe the organization of one accredited school of Anesthesia and Intensive Care of University of Modena and Reggio Emilia, Italy. The analysis of the post-graduation period aims to measure the time-to-first job, the perceived challenges, what postgraduate residents choose as first employ, and the overall satisfaction rating of a cohort of residents completing their training until 2017 with the usual and standard training program. Methods: We collected organization and administrative records of the five-year program of the A-IC School of 4 cohorts of residents who joined from 2009 to 2012 and we performed a survey. We also analyzed the differences among school cohorts during the medical training. In the end, it was investigated as a reason to choose hub hospitals or not. Results: The focus of the training activities revolved around the operating room with a mean of 30.41 ± 6.6 (sd), months followed by Intensive care with 17.29 ± 4.49 (sd) months. Although 7.5% of the respondents were not fully satisfied of the school’s program, 89.7% of residents rated their training as adequate. In fact, 97.2% respondents reported they could overcome the professional challenges they faced after graduation. The multiple variables logistic regression showed a correlation among working in hub hospitals and training performed in university hospitals with a p value of 0.015. Conclusion: This paper describes the postgraduation period. This point should be examined as an integral part of the accreditation procedure. Knowing the satisfaction rate, perception autonomy, and which type of hospitals are preferred can measure the education training capacity of a postgraduation school.