Background. We endeavored to construct a simple score based entirely on epidemiological and clinical variables that would stratify patients who requi

The effect of emergency department (ED) length of stay (EDLOS) on in-hospital mortality (IHM) remains unclear. The aim of this systematic review and meta-analysis was to determine the association between EDLOS and IHM. We searched the PubMed, Medline, Embase, Web of Science, Cochrane Controlled Register of Trials, CINAHL, PsycInfo, and Scopus databases from their inception until 14–15 January 2022. We included studies reporting the association between EDLOS and IHM. A total of 11,337 references were identified, and 52 studies (total of 1,718,518 ED patients) were included in the systematic review and 33 in the meta-analysis. A statistically significant association between EDLOS and IHM was observed for EDLOS over 24 h in patients admitted to an intensive care unit (ICU) (OR = 1.396, 95% confidence interval [CI]: 1.147 to 1.701; p < 0.001, I² = 0%) and for low EDLOS in non-ICU-admitted patients (OR = 0.583, 95% CI: 0.453 to 0.745; p < 0.001, I² = 0%). No associations were detected for the other cut-offs. Our findings suggest that there is an association between IHM low EDLOS and EDLOS exceeding 24 h and IHM. Long stays in the ED should not be allowed and special attention should be given to patients admitted after a short stay in the ED.

Development of three classification trees (CT) based on the CART (Classification and Regression Trees), CHAID (Chi-Square Automatic Interaction Detection) and C4.5 methodologies for the calculation of probability of hospital mortality; the comparison of the results with the APACHE II, SAPS II and MPM II-24 scores, and with a model based on multiple logistic regression (LR).

Background: Classification trees (CTs) are widely used machine learning algorithms with growing applications in clinical research, especially for risk stratification. Their ability to generate interpretable decision rules makes them attractive to healthcare professionals. This review provides an accessible yet rigorous overview of CT methodology for clinicians, highlighting their utility through a case study addressing the “obesity paradox” in critically ill patients. Methods: We describe key methodological aspects of CTs, including model development, pruning, validation, and classification types (simple, ensemble, and hybrid). Using data from the ENPIC (Evaluation of Practical Nutrition Practices in the Critical Care Patient) study, which assessed artificial nutrition in ICU (intensive care unit) patients, we applied various CT approaches—CART (classification and regression trees), CHAID (chi-square automatic interaction detection), and XGBoost (extreme gradient boosting)—and compared them with logistic regression. SHAP (SHapley Additive exPlanation) values were used to interpret ensemble models. Results: CTs allowed for identification of optimal cut-off points in continuous variables and revealed complex, non-linear interactions among predictors. Although the obesity paradox was not confirmed in the full cohort, CTs uncovered a specific subgroup in which obesity was associated with reduced mortality. The ensemble model (XGBoost) achieved the best predictive performance (highest area under the ROC curve), though at the expense of interpretability. Conclusions: CTs are valuable tools in clinical epidemiology, complementing traditional models by uncovering hidden patterns and enhancing risk stratification. While ensemble models offer superior predictive accuracy, their complexity necessitates interpretability techniques such as SHAP. CT-based approaches can guide personalized medicine but require cautious interpretation and external validation.

The importance of artificial nutritional therapy is underrecognized, typically being considered an adjunctive rather than a primary therapy. We aimed to evaluate the influence of nutritional therapy on mortality in critically ill patients. This multicenter prospective observational study included adult patients needing artificial nutritional therapy for >48 h if they stayed in one of 38 participating intensive care units for ≥72 h between April and July 2018. Demographic data, comorbidities, diagnoses, nutritional status and therapy (type and details for ≤14 days), and outcomes were registered in a database. Confounders such as disease severity, patient type (e.g., medical, surgical or trauma), and type and duration of nutritional therapy were also included in a multivariate analysis, and hazard ratios (HRs) and 95% confidence intervals (95%CIs) were reported. We included 639 patients among whom 448 (70.1%) and 191 (29.9%) received enteral and parenteral nutrition, respectively. Mortality was 25.6%, with non-survivors having the following characteristics: older age; more comorbidities; higher Sequential Organ Failure Assessment (SOFA) scores (6.6 ± 3.3 vs 8.4 ± 3.7; P < 0.001); greater nutritional risk (Nutrition Risk in the Critically Ill [NUTRIC] score: 3.8 ± 2.1 vs 5.2 ± 1.7; P < 0.001); more vasopressor requirements (70.4% vs 83.5%; P=0.001); and more renal replacement therapy (12.2% vs 23.2%; P=0.001). Multivariate analysis showed that older age (HR: 1.023; 95% CI: 1.008-1.038; P=0.003), higher SOFA score (HR: 1.096; 95% CI: 1.036-1.160; P=0.001), higher NUTRIC score (HR: 1.136; 95% CI: 1.025-1.259; P=0.015), requiring parenteral nutrition after starting enteral nutrition (HR: 2.368; 95% CI: 1.168-4.798; P=0.017), and a higher mean Kcal/Kg/day intake (HR: 1.057; 95% CI: 1.015-1.101; P=0.008) were associated with mortality. By contrast, a higher mean protein intake protected against mortality (HR: 0.507; 95% CI: 0.263-0.977; P=0.042). Old age, higher organ failure scores, and greater nutritional risk appear to be associated with higher mortality. Patients who need parenteral nutrition after starting enteral nutrition may represent a high-risk subgroup for mortality due to illness severity and problems receiving appropriate nutritional therapy. Mean calorie and protein delivery also appeared to influence outcomes. ClinicaTrials.gov NCT: 03634943.