Introduction: Early prognostic stratification in patients hospitalized for acute infections is a major clinical challenge. Existing tools, such as the Sequential Organ Failure Assessment (SOFA) score and Charlson Comorbidity Index (CCI), were not specifically developed for this purpose. Objectives: We aimed to design a novel multidimensional prognostic score, the Acute Severity Infection score (ASIs), to predict in-hospital mortality using routinely available clinical data. Methods: This retrospective cohort study included 149 adults admitted with acute infections to an internal medicine unit between January 2023 and December 2024. In-hospital all-cause mortality was the primary outcome. Demographic, clinical and laboratory variables obtained within 12 h of admission were analyzed. Variables significantly associated with mortality in both univariate and multivariate regression were incorporated into the ASIs, which ranges from 0 to 7 points. Its performance was compared to SOFA and CCI using ROC curve and Cox regression models. Results: In-hospital mortality occurred in 25.5% of patients. Five variables were independently associated with mortality: lactate ≥ 2.2 mmol/l, frailty composite (confined to bed status, long-term oxygen therapy or advanced malignancy), hemodynamic instability or need for non-invasive ventilation, age ≥ 79.5 years and symptom onset ≥ 3.5 days before admission. ASIs showed the highest discriminative ability (AUC = 0.883) compared to SOFA (AUC = 0.612) and CCI (AUC = 0.742). In multivariate models including all three scores, only ASIs retained independent prognostic significance. Conclusions: The ASIs is a simple tool for early prognostic stratification of patients hospitalized with acute infections. It outperforms existing scores and may enhance clinical decision-making in real-world medical settings.
Early Prediction of In-Hospital Mortality in Patients with Acute Infections: Development of the Acute Severity Infection Score (ASIs)
Comitangelo, Alessio;Vozza, Alfredo;Ditaranto, Giovanna;Re, Giuseppe;Berloco, Ada;Porfido, Erasmo;Custodero, Carlo;Comitangelo, Domenico;Madaghiele, Sara;Portacci, Andrea;Tortorella, Cosimo;Piazzolla, Giuseppina
2025-01-01
Abstract
Introduction: Early prognostic stratification in patients hospitalized for acute infections is a major clinical challenge. Existing tools, such as the Sequential Organ Failure Assessment (SOFA) score and Charlson Comorbidity Index (CCI), were not specifically developed for this purpose. Objectives: We aimed to design a novel multidimensional prognostic score, the Acute Severity Infection score (ASIs), to predict in-hospital mortality using routinely available clinical data. Methods: This retrospective cohort study included 149 adults admitted with acute infections to an internal medicine unit between January 2023 and December 2024. In-hospital all-cause mortality was the primary outcome. Demographic, clinical and laboratory variables obtained within 12 h of admission were analyzed. Variables significantly associated with mortality in both univariate and multivariate regression were incorporated into the ASIs, which ranges from 0 to 7 points. Its performance was compared to SOFA and CCI using ROC curve and Cox regression models. Results: In-hospital mortality occurred in 25.5% of patients. Five variables were independently associated with mortality: lactate ≥ 2.2 mmol/l, frailty composite (confined to bed status, long-term oxygen therapy or advanced malignancy), hemodynamic instability or need for non-invasive ventilation, age ≥ 79.5 years and symptom onset ≥ 3.5 days before admission. ASIs showed the highest discriminative ability (AUC = 0.883) compared to SOFA (AUC = 0.612) and CCI (AUC = 0.742). In multivariate models including all three scores, only ASIs retained independent prognostic significance. Conclusions: The ASIs is a simple tool for early prognostic stratification of patients hospitalized with acute infections. It outperforms existing scores and may enhance clinical decision-making in real-world medical settings.| File | Dimensione | Formato | |
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