The Efficacy of Mastoid Obliteration in Patients Who Recieved Canal Wall Up Tympanomastoidectomy for Chronic Otitis Media

Jung, Nam Yoon; Lee, Chang Bae; Jeong, Sung Hoon; Heo, Kyung Wook; Kang, Myung Koo

doi:10.35420/jcohns.2019.30.2.182

Cited by 1 publication

(1 citation statement)

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“…The treatment of COM involves both pharmaceutical and surgical interventions. In mild cases, pharmaceutical treatments typically involve antibiotics and dietary modifications [ 6 ]. Surgical treatments include canal-wall-down mastoidectomy (CWD) and tympanoplasty surgeries, with CWD being the most commonly used approach [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Hearing Recovery Prediction for Patients with Chronic Otitis Media Who Underwent Canal-Wall-Down Mastoidectomy

Chae,

Yoon,

Lee

et al. 2024

JCM

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Background: Chronic otitis media affects approximately 2% of the global population, causing significant hearing loss and diminishing the quality of life. However, there is a lack of studies focusing on outcome prediction for otitis media patients undergoing canal-wall-down mastoidectomy. Methods: This study proposes a recovery prediction model for chronic otitis media patients undergoing canal-wall-down mastoidectomy, utilizing data from 298 patients treated at Korea University Ansan Hospital between March 2007 and August 2020. Various machine learning techniques, including logistic regression, decision tree, random forest, support vector machine (SVM), extreme gradient boosting (XGBoost), and light gradient boosting machine (light GBM), were employed. Results: The light GBM model achieved a predictive value (PPV) of 0.6945, the decision tree algorithm showed a sensitivity of 0.7574 and an F1 score of 0.6751, and the light GBM algorithm demonstrated the highest AUC-ROC values of 0.7749 for each model. XGBoost had the most efficient PR-AUC curve, with a value of 0.7196. Conclusions: This study presents the first predictive model for chronic otitis media patients undergoing canal-wall-down mastoidectomy. The findings underscore the potential of machine learning techniques in predicting hearing recovery outcomes in this population, offering valuable insights for personalized treatment strategies and improving patient care.

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Section: Introductionmentioning

confidence: 99%