Background: It is estimated that most patients with severe sepsis are admitted through the emergency department. Early identification and subsequent early appropriate therapy remain cornerstones of sepsis management. Early recognition of sepsis in the emergency department (ED) is crucial. The National Early Warning Score 2 (NEWS2) has shown limitations in prognostic accuracy. We aimed to develop and evaluate a prognostic model combining NEWS2 with triage data to predict 28- and 90-day mortality in adult patients with bacterial sepsis. Methods: We conducted a retrospective cohort study of 557 patients admitted to the ED with suspected bacterial infection between March 2017 and September 2019. Candidate predictors included triage variables (vital signs, comorbidities, blood gas data) and clinical scores (NEWS2, SOFA, qSOFA, APACHE2, and SIRS). Outcomes were 28- and 90-day mortality. Logit analysis was used to develop prognostic models, with assessment of discrimination and calibration. Results: Overall mortality was 24.6% at 28 days and 36.4% at 90 days. Models combining NEWS2, age, and lactates outperformed NEWS2 alone (28-day: 73.8% vs. 69%; 90-day: 71.6% vs. 67%). Including terminal status further improved accuracy. Finally, this paper proposes new criteria for the early identification of patients with sepsis in triage, with positive outcomes. Conclusions: Combining NEWS2 with age and lactates enhances prognostic accuracy at triage. This model may inform improved sepsis management.

In this paper, we present a novel approach to customer satisfaction analysis of airport services based on the analysis of distributional data for constructing a bivariate performance indicator. Distributional data was introduced for describing macro-data coming from the aggregation of micro-data observed at the individual level. We use them to represent the distribution of the ratings given by 165 classes (macro-units) of airport customers for twelve observed aspects. We describe the trend of passenger satisfaction over time by extracting 165 macro units from a survey conducted among 13,047 passengers at Bari and Brindisi airports during the peak and off-peak seasons of 2015, 2016 and 2017. To obtain a performance indicator, we performed a multiple factor analysis for distributional data. To our knowledge, no other methods exist for the factor analysis of multiple distributional variables. Further, we propose a new visualization tool called Green Eye Iris plot, which allows a joint visualization of our set of distributional values. The obtained results show that the distributional data analysis approach can provide valuable information at macro level that could be hidden when analyzing micro-data or when macro data are represented only by some features coming from summary statistics of groups.