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Use of Decision Analysis and Economic Evaluation in Breast Reconstruction: A Systematic Review

BACKGROUND: Decision analysis allows clinicians to compare different strategies in the context of uncertainty, through explicit and quantitative measures such as quality of life outcomes and costing data. This is especially important in breast reconstruction, where multiple strategies can be offered...

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Autores principales: Bouhadana, Gabriel, Safran, Tyler, Al-Halabi, Becher, Davison, Peter G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7209866/
https://www.ncbi.nlm.nih.gov/pubmed/32440446
http://dx.doi.org/10.1097/GOX.0000000000002786
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author Bouhadana, Gabriel
Safran, Tyler
Al-Halabi, Becher
Davison, Peter G.
author_facet Bouhadana, Gabriel
Safran, Tyler
Al-Halabi, Becher
Davison, Peter G.
author_sort Bouhadana, Gabriel
collection PubMed
description BACKGROUND: Decision analysis allows clinicians to compare different strategies in the context of uncertainty, through explicit and quantitative measures such as quality of life outcomes and costing data. This is especially important in breast reconstruction, where multiple strategies can be offered to patients. This systematic review aims to appraise and review the different decision analytic models used in breast reconstruction. METHODS: A search of English articles in PubMed, Ovid, and Embase databases was performed. All articles regardless of date of publishing were considered. Two reviewers independently assessed each article, based on strict inclusion criteria. RESULTS: Out of 442 articles identified, 27 fit within the inclusion criteria. These were then grouped according to aspects of breast reconstruction, with implant-based reconstruction (n = 13) being the most commonly reported. Decision analysis (n = 19) and/or economic analyses (n = 27) were employed to discuss reconstructive options. The most common outcome was cost (n = 27). The decision analysis models compared and contrasted surgical strategies, management options, and novel adjuncts. CONCLUSIONS: Decision analysis in breast reconstruction is growing exponentially.The most common model used was a simple decision tree. Models published were of high quality but could be improved with a more in-depth sensitivity analysis. It is essential for surgeons to familiarize themselves with the concept of decision analysis to better tackle complicated decisions, due to its intrinsic advantage of being able to weigh risks and benefits of multiple strategies while using probabilistic models.
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spelling pubmed-72098662020-05-21 Use of Decision Analysis and Economic Evaluation in Breast Reconstruction: A Systematic Review Bouhadana, Gabriel Safran, Tyler Al-Halabi, Becher Davison, Peter G. Plast Reconstr Surg Glob Open Original Article BACKGROUND: Decision analysis allows clinicians to compare different strategies in the context of uncertainty, through explicit and quantitative measures such as quality of life outcomes and costing data. This is especially important in breast reconstruction, where multiple strategies can be offered to patients. This systematic review aims to appraise and review the different decision analytic models used in breast reconstruction. METHODS: A search of English articles in PubMed, Ovid, and Embase databases was performed. All articles regardless of date of publishing were considered. Two reviewers independently assessed each article, based on strict inclusion criteria. RESULTS: Out of 442 articles identified, 27 fit within the inclusion criteria. These were then grouped according to aspects of breast reconstruction, with implant-based reconstruction (n = 13) being the most commonly reported. Decision analysis (n = 19) and/or economic analyses (n = 27) were employed to discuss reconstructive options. The most common outcome was cost (n = 27). The decision analysis models compared and contrasted surgical strategies, management options, and novel adjuncts. CONCLUSIONS: Decision analysis in breast reconstruction is growing exponentially.The most common model used was a simple decision tree. Models published were of high quality but could be improved with a more in-depth sensitivity analysis. It is essential for surgeons to familiarize themselves with the concept of decision analysis to better tackle complicated decisions, due to its intrinsic advantage of being able to weigh risks and benefits of multiple strategies while using probabilistic models. Wolters Kluwer Health 2020-04-27 /pmc/articles/PMC7209866/ /pubmed/32440446 http://dx.doi.org/10.1097/GOX.0000000000002786 Text en Copyright © 2020 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The American Society of Plastic Surgeons. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (http://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Original Article
Bouhadana, Gabriel
Safran, Tyler
Al-Halabi, Becher
Davison, Peter G.
Use of Decision Analysis and Economic Evaluation in Breast Reconstruction: A Systematic Review
title Use of Decision Analysis and Economic Evaluation in Breast Reconstruction: A Systematic Review
title_full Use of Decision Analysis and Economic Evaluation in Breast Reconstruction: A Systematic Review
title_fullStr Use of Decision Analysis and Economic Evaluation in Breast Reconstruction: A Systematic Review
title_full_unstemmed Use of Decision Analysis and Economic Evaluation in Breast Reconstruction: A Systematic Review
title_short Use of Decision Analysis and Economic Evaluation in Breast Reconstruction: A Systematic Review
title_sort use of decision analysis and economic evaluation in breast reconstruction: a systematic review
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7209866/
https://www.ncbi.nlm.nih.gov/pubmed/32440446
http://dx.doi.org/10.1097/GOX.0000000000002786
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