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dc.contributor.advisorSarah Poissant
dc.contributor.authorRoditi, Rachel E.
dc.contributor.authorPoissant, Sarah F.
dc.contributor.authorBero, Eva M.
dc.contributor.authorLee, Daniel J.
dc.date2022-08-11T08:10:55.000
dc.date.accessioned2022-08-23T17:24:46Z
dc.date.available2022-08-23T17:24:46Z
dc.date.issued2009-06-01
dc.date.submitted2016-03-25
dc.identifier.citationRoditi RE, Poissant SF, Bero EM, Lee DJ. A predictive model of cochlear implant performance in postlingually deafened adults. Otol Neurotol. 2009 Jun;30(4):449-54. doi:10.1097/MAO.0b013e31819d3480. PubMed PMID: 19415041.
dc.identifier.issn1537-4505
dc.identifier.doi10.1097/MAO.0b013e31819d3480
dc.identifier.pmid19415041
dc.identifier.urihttp://hdl.handle.net/20.500.14038/49261
dc.description<p>Rachel Roditi participated in this study as a medical student as part of the Senior Scholars research program at the University of Massachusetts Medical School.</p>
dc.description.abstractOBJECTIVE: To develop a predictive model of cochlear implant (CI) performance in postlingually deafened adults that includes contemporary speech perception testing and the hearing history of both ears. STUDY DESIGN: Retrospective clinical study. Multivariate predictors of speech perception after CI surgery included duration of any degree of hearing loss (HL), duration of severe-to-profound HL, age at implantation, and preoperative Hearing in Noise Test (HINT) sentences in quiet and HINT sentences in noise scores. Consonant-nucleus-consonant (CNC) scores served as the dependent variable. To develop the model, we performed a stepwise multiple regression analysis. SETTING: Tertiary referral center. PATIENTS: Adult patients with postlingual severe-to-profound HL who received a multichannel CI. Mean follow-up was 28 months. Fifty-five patients were included in the initial bivariate analysis. INTERVENTION(S): Multichannel cochlear implantation. MAIN OUTCOME MEASURES(S): Predicted and measured postoperative CNC scores. RESULTS: The regression analysis resulted in a model that accounted for 60% of the variance in postoperative CNC scores. The formula is (pred)CNC score = 76.05 + (-0.08 x DurHL(CI ear)) + (0.38 x pre-HINT sentences in quiet) + (0.04 x long sev-prof HL(either ear)). Duration of HL was in months. The mean difference between predicted and measured postoperative CNC scores was 1.7 percentage points (SD, 16.3). CONCLUSION: The University of Massachusetts CI formula uses HINT sentence scores and the hearing history of both ears to predict the variance in postoperative monosyllabic word scores. This model compares favorably with previous studies that relied on Central Institute for the Deaf sentence scores and uses patient data collected by most centers in the United States.
dc.language.isoen_US
dc.publisherLippincott Williams & Wilkins
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=19415041&dopt=Abstract">Link to article in PubMed</a>
dc.relation.urlhttp://dx.doi.org/10.1097/MAO.0b013e31819d3480
dc.subjectAdult
dc.subjectAged
dc.subjectAged, 80 and over
dc.subjectCochlear Implantation
dc.subjectCochlear Implants
dc.subjectFemale
dc.subjectHearing Loss
dc.subjectHumans
dc.subjectMale
dc.subjectMiddle Aged
dc.subjectModels, Statistical
dc.subjectPersons With Hearing Impairments
dc.subjectPredictive Value of Tests
dc.subjectRetrospective Studies
dc.subjectSpeech
dc.subjectAdult
dc.subjectAged
dc.subjectAged
dc.subject80 and over
dc.subjectCochlear Implantation
dc.subjectCochlear Implants
dc.subjectFemale
dc.subjectHearing Loss
dc.subjectHumans
dc.subjectMale
dc.subjectMiddle Aged
dc.subjectModels
dc.subjectStatistical
dc.subjectPersons With Hearing Impairments
dc.subjectPredictive Value of Tests
dc.subjectRetrospective Studies
dc.subjectSpeech
dc.subjectOtolaryngology
dc.subjectSpeech and Hearing Science
dc.titleA predictive model of cochlear implant performance in postlingually deafened adults
dc.typeJournal Article
dc.source.journaltitleOtology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
dc.source.volume30
dc.source.issue4
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/ssp/205
dc.identifier.contextkey8383239
html.description.abstract<p>OBJECTIVE: To develop a predictive model of cochlear implant (CI) performance in postlingually deafened adults that includes contemporary speech perception testing and the hearing history of both ears.</p> <p>STUDY DESIGN: Retrospective clinical study. Multivariate predictors of speech perception after CI surgery included duration of any degree of hearing loss (HL), duration of severe-to-profound HL, age at implantation, and preoperative Hearing in Noise Test (HINT) sentences in quiet and HINT sentences in noise scores. Consonant-nucleus-consonant (CNC) scores served as the dependent variable. To develop the model, we performed a stepwise multiple regression analysis.</p> <p>SETTING: Tertiary referral center.</p> <p>PATIENTS: Adult patients with postlingual severe-to-profound HL who received a multichannel CI. Mean follow-up was 28 months. Fifty-five patients were included in the initial bivariate analysis.</p> <p>INTERVENTION(S): Multichannel cochlear implantation.</p> <p>MAIN OUTCOME MEASURES(S): Predicted and measured postoperative CNC scores.</p> <p>RESULTS: The regression analysis resulted in a model that accounted for 60% of the variance in postoperative CNC scores. The formula is (pred)CNC score = 76.05 + (-0.08 x DurHL(CI ear)) + (0.38 x pre-HINT sentences in quiet) + (0.04 x long sev-prof HL(either ear)). Duration of HL was in months. The mean difference between predicted and measured postoperative CNC scores was 1.7 percentage points (SD, 16.3).</p> <p>CONCLUSION: The University of Massachusetts CI formula uses HINT sentence scores and the hearing history of both ears to predict the variance in postoperative monosyllabic word scores. This model compares favorably with previous studies that relied on Central Institute for the Deaf sentence scores and uses patient data collected by most centers in the United States.</p>
dc.identifier.submissionpathssp/205
dc.contributor.departmentSenior Scholars Program
dc.contributor.departmentDepartment of Otolaryngology
dc.source.pages449-54


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