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Early Prediction of In-Hospital Mortality in Patients with Acute Infections: Development of the Acute Severity
Alessio Comitangelo1, Alfredo Vozza1, Giovanna Ditaranto1
1Interdisciplinary Department of Medicine, School of Medicine, University of Bari "Aldo Moro", Bari, Italy.
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.
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