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A Predictive Model for Determining Permanent Implant Size During 2-Stage Implant Breast Reconstruction

BACKGROUND: Two-stage tissue expander (TE)/permanent implant (PI) breast reconstruction remains the most commonly performed technique in breast reconstruction. Predictions for the PI size preoperatively impact on the number and range of implants made available at TE exchange. This study aims to iden...

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Autores principales: Gabrick, Kyle S., Markov, Nickolay P., Chouairi, Fouad, Wu, Robin, Persing, Sarah M., Abraham, Paul, Avraham, Tomer, Alperovich, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5999420/
https://www.ncbi.nlm.nih.gov/pubmed/29922567
http://dx.doi.org/10.1097/GOX.0000000000001790
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author Gabrick, Kyle S.
Markov, Nickolay P.
Chouairi, Fouad
Wu, Robin
Persing, Sarah M.
Abraham, Paul
Avraham, Tomer
Alperovich, Michael
author_facet Gabrick, Kyle S.
Markov, Nickolay P.
Chouairi, Fouad
Wu, Robin
Persing, Sarah M.
Abraham, Paul
Avraham, Tomer
Alperovich, Michael
author_sort Gabrick, Kyle S.
collection PubMed
description BACKGROUND: Two-stage tissue expander (TE)/permanent implant (PI) breast reconstruction remains the most commonly performed technique in breast reconstruction. Predictions for the PI size preoperatively impact on the number and range of implants made available at TE exchange. This study aims to identify critical preoperative variables and create a predictive model for PI size. METHODS: Patients who underwent 2-stage implant breast reconstruction from 2011 to 2017 were included in the study. Linear and multivariate regression analyses were used to identify significant preoperative variables for PI volume. RESULTS: During the study period, 826 patients underwent 2-stage TE/PI breast reconstruction. Complete records were available for 226 breasts. Initial TE fill ranged from 0% to 102% with a mean final fill of 100.6% of TE volume. The majority of PIs were smooth round (98.2%), silicone (90%) implants. In a multivariate analysis, significant variables for predicting PI size were TE final fill volume (P < 0.0001), TE size (P = 0.03), and a history of preoperative radiation (P = 0.001). Relationships between these 3 variables were utilized to form a predictive model with a regression coefficient of R(2) = 0.914. CONCLUSIONS: Significant variables for predicting PI volume were TE final fill volume, TE size, and a history of preoperative radiation. The ability to more accurately predict the PI volume can improve surgical planning, reduce consignment inventory, and simplify operating room workflow.
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spelling pubmed-59994202018-06-19 A Predictive Model for Determining Permanent Implant Size During 2-Stage Implant Breast Reconstruction Gabrick, Kyle S. Markov, Nickolay P. Chouairi, Fouad Wu, Robin Persing, Sarah M. Abraham, Paul Avraham, Tomer Alperovich, Michael Plast Reconstr Surg Glob Open Original Article BACKGROUND: Two-stage tissue expander (TE)/permanent implant (PI) breast reconstruction remains the most commonly performed technique in breast reconstruction. Predictions for the PI size preoperatively impact on the number and range of implants made available at TE exchange. This study aims to identify critical preoperative variables and create a predictive model for PI size. METHODS: Patients who underwent 2-stage implant breast reconstruction from 2011 to 2017 were included in the study. Linear and multivariate regression analyses were used to identify significant preoperative variables for PI volume. RESULTS: During the study period, 826 patients underwent 2-stage TE/PI breast reconstruction. Complete records were available for 226 breasts. Initial TE fill ranged from 0% to 102% with a mean final fill of 100.6% of TE volume. The majority of PIs were smooth round (98.2%), silicone (90%) implants. In a multivariate analysis, significant variables for predicting PI size were TE final fill volume (P < 0.0001), TE size (P = 0.03), and a history of preoperative radiation (P = 0.001). Relationships between these 3 variables were utilized to form a predictive model with a regression coefficient of R(2) = 0.914. CONCLUSIONS: Significant variables for predicting PI volume were TE final fill volume, TE size, and a history of preoperative radiation. The ability to more accurately predict the PI volume can improve surgical planning, reduce consignment inventory, and simplify operating room workflow. Wolters Kluwer Health 2018-05-21 /pmc/articles/PMC5999420/ /pubmed/29922567 http://dx.doi.org/10.1097/GOX.0000000000001790 Text en Copyright © 2018 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
Gabrick, Kyle S.
Markov, Nickolay P.
Chouairi, Fouad
Wu, Robin
Persing, Sarah M.
Abraham, Paul
Avraham, Tomer
Alperovich, Michael
A Predictive Model for Determining Permanent Implant Size During 2-Stage Implant Breast Reconstruction
title A Predictive Model for Determining Permanent Implant Size During 2-Stage Implant Breast Reconstruction
title_full A Predictive Model for Determining Permanent Implant Size During 2-Stage Implant Breast Reconstruction
title_fullStr A Predictive Model for Determining Permanent Implant Size During 2-Stage Implant Breast Reconstruction
title_full_unstemmed A Predictive Model for Determining Permanent Implant Size During 2-Stage Implant Breast Reconstruction
title_short A Predictive Model for Determining Permanent Implant Size During 2-Stage Implant Breast Reconstruction
title_sort predictive model for determining permanent implant size during 2-stage implant breast reconstruction
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5999420/
https://www.ncbi.nlm.nih.gov/pubmed/29922567
http://dx.doi.org/10.1097/GOX.0000000000001790
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