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Analyzing cost-effectiveness of neural-monitoring in recurrent laryngeal nerve recovery course in thyroid surgery
- Wang, Tie;
- Kim, Hoon Yub;
- Wu, Che-Wei;
- Rausei, Stefano;
- Sun, Hui;
- 외 2명
WEB OF SCIENCE
28SCOPUS
29초록
Purpose: The increasing use of intraoperative neural monitoring (IONM) of the recurrent laryngeal nerve (RLN) during thyroid surgery imposes an evaluation of cost-effectiveness. Methods: The analysis estimated the cost versus utility of different alternatives that simulate nerve injury course and the consequences for the following cohorts of patients: (1) no RLN injury, or vocal fold palsy (VCP) recovery within 1 month (2), 2 months (3), 6 months (4), and after 12 months (5). In the model applied, the average simulated cohort consisted of a young female patient, 40 years old, employed, daily voice user, who underwent elective, conventional total thyroidectomy via cervical incision using a standardized intermittent IONM technique, for an operable benign, bilateral, diffuse, multinodular, non-toxic, non-retrosternal goiter. Results: IONM was cost-ineffective when parameters such as the rates of transient vocal fold palsy (VCP) reached 38.5%. IONM was cost-effective if the rate of VCP was 33.6% at 1 month, 22.9% at 2 months, 9.8% at 6 months, and 3.8% at 12 months, independent of phono-surgery. The described scenario is cost-effective only in a highvolume setting. Conclusions: This study used simulation economic modeling to assess clinical and cost-effectiveness utility of IONM implementation. In light of the limitations of a simulation-based study, we conclusively assumed that IONM is cost-effective for permeant RLN injuries.
키워드
- 제목
- Analyzing cost-effectiveness of neural-monitoring in recurrent laryngeal nerve recovery course in thyroid surgery
- 저자
- Wang, Tie; Kim, Hoon Yub; Wu, Che-Wei; Rausei, Stefano; Sun, Hui; Pergolizzi, Francesca Pia; Dionigi, Gianlorenzo
- 발행일
- 2017-12
- 유형
- Article
- 권
- 48
- 페이지
- 180 ~ 188
- 언어
- ENG
- 출판사
- Surgical Association
- 발행국가
- 네덜란드
- 분량
- 9 페이지
- ISSN
- E 1743-9159
P 1743-9191