Bacteriology and resistance patterns of otitis media with effusion

Min, Hye Kyu; Kim, Seok Hyun; Park, Myung Jin; Kim, Sung Su; Kim, Sang Hoon; Yeo, Seung Geun

doi:10.1016/j.ijporl.2019.109652

Cited by 9 publications

(2 citation statements)

References 22 publications

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“…OME is not characterized by symptoms and signs of acute inflammation that would be expected in a typical acute bacterial infection caused by planktonic bacteria. The typical bacteria involved in OME are S. pneumoniae, M. catarrhalis and H. influenzae, but in most studies bacteria were cultured in less than half of the samples, ranging from 21 to 70% [7,11,12]. In our study, positive bacterial cultures were even less (10%).…”

Section: Discussioncontrasting

confidence: 49%

Bacterial aetiology of chronic otitis media with effusion in children - risk factors

Korona-Głowniak

Wisniewska

Juda

et al. 2020

Journal of Otolaryngology - Head & Neck Surgery

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Background Otitis media with effusion (OME) may occur spontaneously because of poor Eustachian tube function or as an inflammatory response following AOM. Bacterial involvement in OME has been widely reported, with various available methods to identify pathogens from middle ear effusion, including traditional culture methods and polymerase chain reaction (PCR). Objectives The primary goal of this study was to evaluate the bacteriological profile of middle ear effusion in OME. Risk factors of the bacterial OME aetiology were also identified. Methods Middle ear effusions (MEF) from 50 children, aged 2–8 years, diagnosed by ENT and undergoing routine tympanostomy tube placement were collected. MEF samples were streaked on standard microbiological media. Next, DNA was isolated from MEF samples and analysed with multiplex PCR for Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis and Alloiococcus otitidis. Results In multiplex PCR assay 37 (74%) of 50 children were positive for at least one of the four microorganisms. In 27.0% positive children multiple bacterial pathogens were identified. A. otitidis was the most frequently identified in positive MEF children (59.5%). By multiplex PCR, H. influenzae, S. pneumoniae and M. catarrhalis were detected in 24, 18 and 8% of OME patients, respectively. There was significant association between bilateral infection and H. influenzae aetiology of OME. Conclusions Overall we found OME predominantly a single otopathogen infection caused mainly by A. otitidis, which is difficult in identification using standard culture method, ahead to S. pneumoniae and H. influenzae. However, one third of MEF samples had multiple bacterial pathogens.

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Section: Discussioncontrasting

confidence: 49%

Bacterial aetiology of chronic otitis media with effusion in children - risk factors

Korona-Głowniak

Wisniewska

Juda

et al. 2020

Journal of Otolaryngology - Head & Neck Surgery

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“…Given the possible complications of untreated disease, many practitioners will frequently prescribe antibiotics despite equivocal findings on otoscopy 20 . The widespread use of antibiotics for otitis media has engendered drug resistant strains of the common causative pathogens 21 . As a result, oral cephalosporins, trimethoprim-sulfamethoxazole, and macrolides have lost their efficacy over time 22 .…”

Section: Introductionmentioning

confidence: 99%

Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach

Kashani

Młyńczak

Zarabanda

et al. 2021

Sci Rep

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Otitis media, a common disease marked by the presence of fluid within the middle ear space, imparts a significant global health and economic burden. Identifying an effusion through the tympanic membrane is critical to diagnostic success but remains challenging due to the inherent limitations of visible light otoscopy and user interpretation. Here we describe a powerful diagnostic approach to otitis media utilizing advancements in otoscopy and machine learning. We developed an otoscope that visualizes middle ear structures and fluid in the shortwave infrared region, holding several advantages over traditional approaches. Images were captured in vivo and then processed by a novel machine learning based algorithm. The model predicts the presence of effusions with greater accuracy than current techniques, offering specificity and sensitivity over 90%. This platform has the potential to reduce costs and resources associated with otitis media, especially as improvements are made in shortwave imaging and machine learning.

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Adenoid Morphology and Other Prognostic Factors for Otitis Media with Effusion in School Children

Dange

Bhat

Yadav

2021

Indian J Otolaryngol Head Neck Surg

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Bacteriology and resistance patterns of otitis media with effusion

Cited by 9 publications

References 22 publications

Bacterial aetiology of chronic otitis media with effusion in children - risk factors

Bacterial aetiology of chronic otitis media with effusion in children - risk factors

Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach

Adenoid Morphology and Other Prognostic Factors for Otitis Media with Effusion in School Children

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