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Background <p>Breast reduction surgery is currently the most critical treatment for macromastia. Preoperatively, patients are not only concerned about the surgical outcomes and potential complications but also about the amount of breast tissue that can be resected. Previous studies have proposed formulas to predict the amount of resected breast tissue; however, these studies often suffer from limited variables and small sample sizes, leading to less accurate predictions. Therefore, this study aims to develop a more accurate predictive model for breast tissue resection in breast reduction surgery by incorporating a greater number of variables, providing better clinical guidance.</p> Methods <p>Retrospective data from 360 patients (711 sides) who underwent vertical-scar reduction mammoplasty were collected. The data included preoperative patient information such as age, height, weight, BMI, and breast surface measurements, along with the accurately measured weight of breast tissue resected from each side during surgery. Statistical analysis was performed on the data, and multiple linear regression was used to determine the relationship between these variables and the weight of the resected breast tissue.</p> Results <p>The formula established is as follows: Breast tissue resection weight (g) = (15 × Sternal Notch-to-Nipple + 10 × Clavicle-to-Nipple + 23 × Nipple-to-Inframammary Fold + 10 × BMI + 6 × Inter-Nipple distance + 8 × Midline-to-Nipple)−1023.</p> Conclusions <p>This predictive model for breast tissue resection weight in vertical-scar reduction mammoplasty represents an empirical summary of clinical data. Implementing this model can aid surgeons in preoperatively estimating resection weight, ultimately improving surgical outcomes and increasing patient satisfaction.</p> Level of Evidence IV <p>This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors <a href="http://www.springer.com/00266">www.springer.com/00266</a>.</p>

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A Predictive Model for Breast Tissue Resection Weight in Vertical-Scar Reduction Mammoplasty

  • 期刊论文

  • 发表日期：

    2025年2月7日

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  • Haoran Li,
  • Yan Lin,
  • Zhengyao Li,
  • Tongtong Zhang,
  • Leijuan Gan,
  • Dali Mu

摘要

Background

Breast reduction surgery is currently the most critical treatment for macromastia. Preoperatively, patients are not only concerned about the surgical outcomes and potential complications but also about the amount of breast tissue that can be resected. Previous studies have proposed formulas to predict the amount of resected breast tissue; however, these studies often suffer from limited variables and small sample sizes, leading to less accurate predictions. Therefore, this study aims to develop a more accurate predictive model for breast tissue resection in breast reduction surgery by incorporating a greater number of variables, providing better clinical guidance.

Methods

Retrospective data from 360 patients (711 sides) who underwent vertical-scar reduction mammoplasty were collected. The data included preoperative patient information such as age, height, weight, BMI, and breast surface measurements, along with the accurately measured weight of breast tissue resected from each side during surgery. Statistical analysis was performed on the data, and multiple linear regression was used to determine the relationship between these variables and the weight of the resected breast tissue.

Results

The formula established is as follows: Breast tissue resection weight (g) = (15 × Sternal Notch-to-Nipple + 10 × Clavicle-to-Nipple + 23 × Nipple-to-Inframammary Fold + 10 × BMI + 6 × Inter-Nipple distance + 8 × Midline-to-Nipple)−1023.

Conclusions

This predictive model for breast tissue resection weight in vertical-scar reduction mammoplasty represents an empirical summary of clinical data. Implementing this model can aid surgeons in preoperatively estimating resection weight, ultimately improving surgical outcomes and increasing patient satisfaction.

Level of Evidence IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.