Predictive factors for abscessing tonsillitis: a retrospective analysis.


Journal

European review for medical and pharmacological sciences
ISSN: 2284-0729
Titre abrégé: Eur Rev Med Pharmacol Sci
Pays: Italy
ID NLM: 9717360

Informations de publication

Date de publication:
Nov 2023
Historique:
medline: 20 11 2023
pubmed: 17 11 2023
entrez: 17 11 2023
Statut: ppublish

Résumé

In clinical practice, identifying abscesses in tonsillar infections is crucial for early therapeutic management. Diagnosis of a peritonsillar abscess is usually based on clinical symptoms. Complementary examination procedures, such as laboratory parameters and imaging, are available for confirmation. A retrospective analysis was carried out of data for 752 patients who presented with acute tonsillar infection and were hospitalized between January 2012 and February 2021. The data analyses involved evaluating the patient's clinical symptoms, inflammatory parameters, and previous medical history in relation to the predictive power of these factors for the presence of an abscess. Predictor analysis for the presence of an abscess showed significant values for trismus (OR 2.392; 95% CI, 1.305 to 4.383; p=0.005) and palatal arch protrusion (OR 29.679; 95% CI, 17.460 to 50.447; p=0.000). The inflammatory parameter C-reactive protein and the leukocyte count were not statistically significant as predictors. The presence of a tonsillar abscess can be diagnosed from the clinical presentation alone if the findings are clear. Further diagnostic procedures are indicated in case of inconclusive findings, and ultrasound should be the primary noninvasive method. Computed tomography is only required in selected cases. Inflammatory parameters can be assessed in order to monitor therapy, but do not predict the presence of an abscess. However, if defined action sequences are being considered, tonsillar abscesses can be differentiated at an early point.

Identifiants

pubmed: 37975394
doi: 10.26355/eurrev_202311_34349
pii:
doi:

Substances chimiques

C-Reactive Protein 9007-41-4

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

10690-10696

Auteurs

M Sievert (M)

Department of Otorhinolaryngology, Head and Neck Surgery, Friedrich Alexander University of Erlangen-Nuremberg, Germany. matti.sievert@uk-erlangen.de.

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Classifications MeSH