Dataset Information

Practice variations in voice treatment selection following vocal fold mucosal resection.

ABSTRACT: To characterize initial voice treatment selection following vocal fold mucosal resection in a Medicare population.Retrospective analysis of a large, nationally representative Medicare claims database.Patients with > 12 months of continuous Medicare coverage who underwent a leukoplakia- or cancer-related vocal fold mucosal resection (index) procedure during calendar years 2004 to 2009 were studied. The primary outcome of interest was receipt of initial voice treatment (thyroplasty, vocal fold injection, or speech therapy) following the index procedure. We evaluated the cumulative incidence of each postindex treatment type, treating the other treatment types as competing risks, and further evaluated postindex treatment utilization using the proportional hazards model for the subdistribution of a competing risk. Patient age, sex, and Medicaid eligibility were used as predictors.A total of 2,041 patients underwent 2,427 index procedures during the study period. In 14% of cases, an initial voice treatment event was identified. Women were significantly less likely to receive surgical or behavioral treatment compared to men. From age 65 to 75 years, the likelihood of undergoing surgical treatment increased significantly with each 5-year age increase; after age 75 years, the likelihood of undergoing either surgical or behavioral treatment decreased significantly every 5 years. Patients with low socioeconomic status were significantly less likely to undergo speech therapy.The majority of Medicare patients do not undergo voice treatment following vocal fold mucosal resection. Further, the treatments analyzed here appear disproportionally utilized based on patient sex, age, and socioeconomic status. Additional research is needed to determine whether these observations reflect clinically explainable differences or disparities in care.2c. Laryngoscope, 126:2505-2512, 2016.

SUBMITTER: Moore JE

PROVIDER: S-EPMC5018919 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Practice variations in voice treatment selection following vocal fold mucosal resection.

Moore Jaime E JE Rathouz Paul J PJ Havlena Jeffrey A JA Zhao Qianqian Q Dailey Seth H SH Smith Maureen A MA Greenberg Caprice C CC Welham Nathan V NV

The Laryngoscope 20160312 11

<h4>Objectives/hypothesis</h4>To characterize initial voice treatment selection following vocal fold mucosal resection in a Medicare population.<h4>Study design</h4>Retrospective analysis of a large, nationally representative Medicare claims database.<h4>Methods</h4>Patients with > 12 months of continuous Medicare coverage who underwent a leukoplakia- or cancer-related vocal fold mucosal resection (index) procedure during calendar years 2004 to 2009 were studied. The primary outcome of interest ...[more]

PMID: 26972900

Similar Datasets

Project description:BackgroundDysphonia influences the quality of life by interfering with communication. However, a laryngoscopic examination is expensive and not readily accessible in primary care units. Experienced laryngologists are required to achieve an accurate diagnosis.ObjectiveThis study sought to detect various vocal fold diseases through pathological voice recognition using artificial intelligence.MethodsWe collected 189 normal voice samples and 552 samples of individuals with voice disorders, including vocal atrophy (n=224), unilateral vocal paralysis (n=50), organic vocal fold lesions (n=248), and adductor spasmodic dysphonia (n=30). The 741 samples were divided into 2 sets: 593 samples as the training set and 148 samples as the testing set. A convolutional neural network approach was applied to train the model, and findings were compared with those of human specialists.ResultsThe convolutional neural network model achieved a sensitivity of 0.66, a specificity of 0.91, and an overall accuracy of 66.9% for distinguishing normal voice, vocal atrophy, unilateral vocal paralysis, organic vocal fold lesions, and adductor spasmodic dysphonia. Compared with the accuracy of human specialists, the overall accuracy rates were 60.1% and 56.1% for the 2 laryngologists and 51.4% and 43.2% for the 2 general ear, nose, and throat doctors.ConclusionsVoice alone could be used for common vocal fold disease recognition through a deep learning approach after training with our Mandarin pathological voice database. This approach involving artificial intelligence could be clinically useful for screening general vocal fold disease using the voice. The approach includes a quick survey and a general health examination. It can be applied during telemedicine in areas with primary care units lacking laryngoscopic abilities. It could support physicians when prescreening cases by allowing for invasive examinations to be performed only for cases involving problems with automatic recognition or listening and for professional analyses of other clinical examination results that reveal doubts about the presence of pathologies.

Dataset Information

Practice variations in voice treatment selection following vocal fold mucosal resection.

Publications

Practice variations in voice treatment selection following vocal fold mucosal resection.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets