Predicting In-Hospital Mortality in Acute Mesenteric Ischemia: The RADIAL Score

<b>Background/Objectives:</b> Acute mesenteric ischemia (AMI) is a time-dependent condition associated with exceptionally high in-hospital mortality, particularly among elderly and comorbid patients. Early identification of patients at high risk of death remains challenging and has important implications for clinical decision-making. The objective of this study was to derive and internally validate a prognostic score for in-hospital mortality of patients with AMI. <b>Materials and Methods:</b> We conducted a multicenter, observational, retrospective cohort study including patients with AMI from 10 participating hospitals. A descriptive and analytical approach was performed. A Classification and Regression Tree (CART) model was used to determine cut-off points for continuous variables and assess their association with mortality. Based on these thresholds, a univariate analysis was performed, and variables with statistical significance (<i>p</i> < 0.05) were incorporated into a multivariate logistic regression model. A score-the RADIAL score-was then derived from the beta coefficients. The discriminative ability of the score was evaluated using the receiver operating characteristic (ROC) curve. <b>Results</b>: A total of 693 patients were studied. Thee mean age was 81 years (IQR 73-86) and 54.2% were women. A history of cardiovascular disease was present in 75.3% of participants. Overall mortality was 62.4%. Most patients (74%) were managed conservatively. Significant variables in the bivariate analysis included hypotension, age > 65 years, pH < 7.3, creatinine > 1.7 mg/dL, and absence of rectal bleeding. These variables were incorporated into the multivariate model. The resulting score showed an area under the ROC curve of 0.78 (95% CI: 0.74-0.82). <b>Conclusions</b>: The RADIAL score demonstrated robust predictive performance and allowed the identification of three mortality-risk groups: 30-40% (low), 50-60% (intermediate), and 80% (high). This tool may support clinical decision-making in the management of patients with AMI.