Autor: |
Marko Tarle, Marina Raguž, Ivica Lukšić |
Jazyk: |
angličtina |
Rok vydání: |
2024 |
Předmět: |
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Zdroj: |
Diagnostics, Vol 14, Iss 19, p 2163 (2024) |
Druh dokumentu: |
article |
ISSN: |
2075-4418 |
DOI: |
10.3390/diagnostics14192163 |
Popis: |
Background/Objectives: Odontogenic abscesses are a common cause of emergency visits to oral and maxillofacial surgery departments and can lead to life-threatening complications if they are not recognized and treated promptly. The aim of this study was to evaluate the prognostic value of the Aggregate Index of Systemic Inflammation (AISI) in comparison to other systemic inflammatory indices, including the Systemic Immune Inflammation Index (SII), the Neutrophil-to-Lymphocyte Ratio (NLR), the Platelet-to-Lymphocyte Ratio (PLR), and the Lymphocyte-to-Monocyte Ratio (LMR), in predicting the severity of odontogenic abscesses. Methods: This retrospective study included 221 patients hospitalized for odontogenic abscesses at Dubrava University Hospital between January 2019 and December 2023. Clinical and laboratory data, including AISI, SII, NLR, PLR, and LMR, were collected. The severity of the abscesses was assessed using the Symptom Severity (SS) Score and patients were categorized into less severe and severe groups based on their scores. An ROC curve analysis was used to assess the predictive accuracy of each inflammatory index. Results: The AISI was identified as the most effective predictor of abscess severity and had the highest sensitivity (SE = 82.93) and specificity (SP = 81.63) among the indices analyzed. It outperformed C-reactive protein (CRP) in predicting severe abscesses with an AUC of 0.90 compared to 0.74 for CRP. In addition, AISI showed significant correlations with length of hospital stay and the occurrence of systemic inflammatory response syndrome (SIRS). Conclusions: The AISI index is a better predictor of odontogenic abscess severity compared to other systemic inflammatory markers and CRP. Its integration into clinical practice could improve the early detection of high-risk patients, leading to better treatment outcomes and lower risks of complications. |
Databáze: |
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