Mediastinitis: parotid abscess complication

Brito, Thiago Pires; Guimarães, Alexandre Caixeta; Oshima, Mariana Mari; Chone, Carlos Takahiro

doi:10.1016/j.bjorl.2013.03.001

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…Furthermore, previous studies reported wound healing problems, which were not observed in our study [9]. Severe complications such as jaw osteomyelitis, temporal lobe abscess [23] craniocervical necrotizing fasciitis [24], descending mediastinitis [25], airway obstruction, septicemia, and septic shock [11,25,26] did not occur during the follow-up of our patients.…”

Section: Discussionmentioning

confidence: 41%

Bi-Institutional Analysis of Microbiological Spectrum and Therapeutic Management of Parotid Abscesses

Mayer,

Esser,

Walker

et al. 2024

Preprint

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Background. A parotid abscess (PA) is a complication of an acute bacterial parotitis with a potentially life-threatening course. To date, data on the diagnosis and therapy of PA is sparse and mostly consists of case reports or case series. Therefore, this study aimed at comprehensively analyzing the microbiological spectrum and the therapeutic management in a bi-institutional setting. Methods. A retrospective clinical chart review was performed to identify all patients surgically treated for PA at two tertiary care centers in Germany. Data on demographics, clinical management and microbiological data including species identification, pathogenicity, type of antibiotic therapy, adjustment of antibiotics, antibiotic sensitivity testing, and smear test results were extracted. Intervention-related variables and etiology were analyzed for their statistical association with outcome variables. Results. Overall, 85 patients were included. Most patients (92.9%) underwent surgical incision. Around half of the patients (45.9%) were treated under local anesthesia. No facial nerve palsy was observed. The most frequently detected pathogens were Streptococci (n=23), followed by Staphylococcus aureus (n=6) including one case of methicillin-resistant Staphylococcus aureus. Most patients (68.2%) received an aminopenicillin +/- beta-lactamase inhibitor as empiric antibiotic therapy. In 6 cases the antibiotic therapy was modified after receiving the antibiogram. Four patients (5.2%) presented with recurrent PA. Etiology was idiopathic (42.4%) or obstructive, immunosuppressive, or tumorous (each 11.8%). Patients with a dental focus (p=0.007) had a longer duration of hospitalization. Chronic parotitis was associated with recurrence (p<0.001). Conclusion. The results show that the surgical therapy of PA under local anesthesia is safe. A dental examination should routinely be performed to rule out a dental focus. Obtaining a microbiological specimen in order to modify antibiotic therapy if necessary and a histopathological specimen to rule out a tumorous etiology is obligate.

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Section: Discussionmentioning

confidence: 41%

Bi-Institutional Analysis of Microbiological Spectrum and Therapeutic Management of Parotid Abscesses

Mayer,

Esser,

Walker

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…The long-term hospitalization caused by DNIs will be a huge burden not only on patients and their families, but also on the entire medical system [35]. Thus, it is essential to identify the clinical, laboratory, radiological, and pathogen factors that predispose DNI patients to life-threatening complications [35][36][37][38][39]. In this era of aging, some authors have found certain clinical differences in DNIs between elderly patients and adult patients [40].…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results

Chen,

Chin,

Chan

et al. 2023

Diagnostics

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Background: Deep neck infection (DNI) is a serious infectious disease, and descending mediastinitis is a fatal infection of the mediastinum. However, no study has applied artificial intelligence to assess progression to descending mediastinitis in DNI patients. Thus, we developed a model to assess the possible progression of DNI to descending mediastinitis. Methods: Between August 2017 and December 2022, 380 patients with DNI were enrolled; 75% of patients (n = 285) were assigned to the training group for validation, whereas the remaining 25% (n = 95) were assigned to the test group to determine the accuracy. The patients’ clinical and computed tomography (CT) parameters were analyzed via the k-nearest neighbor method. The predicted and actual progression of DNI patients to descending mediastinitis were compared. Results: In the training and test groups, there was no statistical significance (all p > 0.05) noted at clinical variables (age, gender, chief complaint period, white blood cells, C-reactive protein, diabetes mellitus, and blood sugar), deep neck space (parapharyngeal, submandibular, retropharyngeal, and multiple spaces involved, ≥3), tracheostomy performance, imaging parameters (maximum diameter of abscess and nearest distance from abscess to level of sternum notch), or progression to mediastinitis. The model had a predictive accuracy of 82.11% (78/95 patients), with sensitivity and specificity of 41.67% and 87.95%, respectively. Conclusions: Our model can assess the progression of DNI to descending mediastinitis depending on clinical and imaging parameters. It can be used to identify DNI patients who will benefit from prompt treatment.

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“…Complications of DNI can include esophageal perforation, pneumonia, internal jugular vein thrombosis (Lemierre’s Syndrome), carotid artery erosion, and airway compromise [ 25 , 26 , 27 ]. The mortality rate is relatively high whiles these complications occur [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings—Preliminary Data and a Pilot Study

Chen

Chin

2022

Diagnostics

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Background: Deep neck infection (DNI) can lead to airway obstruction. Rather than intubation, some patients need tracheostomy to secure the airway. However, no study has used deep learning (DL) artificial intelligence (AI) to predict the need for tracheostomy in DNI patients. Thus, the purpose of this study was to develop a DL framework to predict the need for tracheostomy in DNI patients. Methods: 392 patients with DNI were enrolled in this study between August 2016 and April 2022; 80% of the patients (n = 317) were randomly assigned to a training group for model validation, and the remaining 20% (n = 75) were assigned to the test group to determine model accuracy. The k-nearest neighbor method was applied to analyze the clinical and computed tomography (CT) data of the patients. The predictions of the model with regard to the need for tracheostomy were compared with actual decisions made by clinical experts. Results: No significant differences were observed in clinical or CT parameters between the training group and test groups. The DL model yielded a prediction accuracy of 78.66% (59/75 cases). The sensitivity and specificity values were 62.50% and 80.60%, respectively. Conclusions: We demonstrated a DL framework to predict the need for tracheostomy in DNI patients based on clinical and CT data. The model has potential for clinical application; in particular, it may assist less experienced clinicians to determine whether tracheostomy is necessary in cases of DNI.

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Mediastinitis: parotid abscess complication

Cited by 8 publications

References 2 publications

Bi-Institutional Analysis of Microbiological Spectrum and Therapeutic Management of Parotid Abscesses

Bi-Institutional Analysis of Microbiological Spectrum and Therapeutic Management of Parotid Abscesses

A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results

Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings—Preliminary Data and a Pilot Study

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