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Introduction <p>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.</p> Objectives <p>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.</p> Methods <p>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&#xa0;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.</p> Results <p>In-hospital mortality occurred in 25.5% of patients. Five variables were independently associated with mortality: lactate ≥ 2.2&#xa0;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&#xa0;years and symptom onset ≥ 3.5&#xa0;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.</p> Conclusions <p>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.</p>

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Early Prediction of In-Hospital Mortality in Patients with Acute Infections: Development of the Acute Severity Infection Score (ASIs)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月26日

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  • Alessio Comitangelo,
  • Alfredo Vozza,
  • Giovanna Ditaranto,
  • Giuseppe Re,
  • Ada Berloco,
  • Erasmo Porfido,
  • Carlo Custodero,
  • Domenico Comitangelo,
  • Sara Madaghiele,
  • Andrea Portacci,
  • Cosimo Tortorella,
  • Giuseppina Piazzolla

摘要

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.