Prognosis Tool Based on a Modified Children's Implant Profile for Use in Pediatric Cochlear Implant Candidacy Evaluation.
| Autor: | Graham O'Brien, Lynne C., Valim, Clarissa, Neault, Marilyn, Kammerer, Betsy, Clark, Terrell, Johnston, Jennifer, Culver, Stacey, Jing Zhou, Kenna, Margaret A., Licameli, Greg R. |
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PROGNOSIS
COMMUNICATION methodology DEAFNESS EAR surgery PATIENT selection ACADEMIC medical centers COCHLEAR implants STATISTICAL correlation DECISION making EXPERIMENTAL design HEALTH care teams LONGITUDINAL method RESEARCH methodology HEALTH outcome assessment REGRESSION analysis STATISTICAL sampling SPEECH evaluation STATISTICS LOGISTIC regression analysis RETROSPECTIVE studies RESEARCH methodology evaluation DATA analysis software DESCRIPTIVE statistics |
| Zdroj: | Annals of Otology, Rhinology & Laryngology; Feb2012, Vol. 121 Issue 2, p73-84, 12p, 1 Black and White Photograph, 2 Diagrams, 5 Charts |
| Abstrakt: | Objectives: We developed a prediction tool to assist in evaluation of pediatric candidates for cochlear implantation (CI) and to help plan for preoperative and postoperative support. Methods: Between 1995 and 2005,277 patients underwent CI at Children's Hospital Boston. Of these 277 patients, 250 had at least 2 years of post-CI follow-up and adequate pre-CI information for rating by our prediction tool. Of the 250, 106 were randomly selected for inclusion. The patients were divided into group A (auditory/oral communicator); group B (auditory/oral communicator with visual assistance), group C (visual/manual communicator with auditory/oral skills assistance), and group D (will not derive communicative benefit from implant). Predictions were performed with clinical assessment and two statistical techniques: regression modeling and classification and regression tree (CART) analysis. Results: Among patients who became auditory/oral communicators (group A), clinical assessment predicted that outcome accurately 65% of the time, CART analysis had intermediate sensitivity (79%), and regression modeling was the most sensitive (95%). Groups B through D were predicted 45% of the time by regression modeling, 90% of the time by clinical assessment, and 100% of the time by CART analysis. Conclusions: A combination of speech-language, medical, and educational constructs can provide a reliable prediction of the communication outcome. Our goal for the prognosis tool is to make it part of the overall candidacy process in supporting decision-making about CI and planning for post-CI therapy. [ABSTRACT FROM AUTHOR] |
| Databáze: | Complementary Index |
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