- Data, Statistics and AI for Well-Being of People and Organisations
- A cura di Luigi Fabbris, Alberto Franci, Pietro Renzi
Statistical analysis of necrotizing fasciitis: Clinical predictors and diagnostic score evaluation
- Gianfranco Piscopo
- Chiara Marciano
- Massimiliano Giacalone
- Riccardo Salzillo
- © 2026 Author(s) |
- CC BY 4.0
- DOI: 10.36253/979-12-215-1062-1.57
Necrotizing fasciitis (NF) is a potentially fatal soft tissue infection that requires rapid diagnosis and urgent treatment to prevent serious complications such as amputation, multi-organ failure, and death. Early identification of high-risk patients is essential to optimize clinical outcomes. This study offers a novel analysis of clinical parameters and survival rates in different groups of Italian patients diagnosed with NF. By applying advanced statistical techniques, it identifies prognostic indicators that can enhance evidence-based clinical decision-making. The analysis highlights the critical importance of monitoring specific biomarkers, particularly glycated hemoglobin (HbA1c), in diabetic patients. Elevated HbA1c levels are strongly associated with poor glycemic control, delayed wound healing, and a significantly increased risk of adverse outcomes, including amputation and mortality. These findings support the use of HbA1c as a predictive marker of risk stratification in this patient population. More accurate risk classification enables timely interventions and contributes to the development of personalized therapeutic protocols aimed at improving the prognosis and reducing the need for invasive procedures. Data analysis also provides valuable information on the clinical relevance of various patient-specific factors. A comparative evaluation of the two main diagnostic tools for NF, the LRINEC score and the SIARI score, demonstrates greater diagnostic precision of the SIARI score, particularly in diabetic patients. These results support the use of the SIARI score as the preferred tool for early diagnosis in high-risk subgroups, contributing to more effective and personalized management of necrotizing fasciitis.
- Keywords:
- Gender,
- Gender Equality Index,
- PLS-PM,
- Italy,
- Regional Studies,
University of Naples Federico II, Italy - ORCID: 0009-0005-8804-9865
University of Cassino and Southern Lazio, Italy - ORCID: 0009-0000-9656-5222
University of Campania Luigi Vanvitelli, Italy - ORCID: 0000-0002-4284-520X
University of Palermo, Italy - ORCID: 0009-0008-3149-787X
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Informazioni sul capitolo
Titolo del capitolo
Statistical analysis of necrotizing fasciitis: Clinical predictors and diagnostic score evaluation
Autori
Gianfranco Piscopo, Chiara Marciano, Massimiliano Giacalone, Riccardo Salzillo
Lingua
Inglese
DOI
10.36253/979-12-215-1062-1.57
Opera sottoposta a peer review
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
Data, Statistics and AI for Well-Being of People and Organisations
Curatori
Luigi Fabbris, Alberto Franci, Pietro Renzi
Opera sottoposta a peer review
Numero di pagine
420
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
Firenze University Press
DOI
10.36253/979-12-215-1062-1
ISBN Print
979-12-215-1061-4
eISBN (pdf)
979-12-215-1062-1
eISBN (xml)
979-12-215-1063-8
Collana
Applied Statistics Series