Mahrhofer Maximilian, Wallner Christoph, Reichert Raphael, Fierdel Frederic, Nolli Mattia, Sidiq Maiwand, Schoeller Thomas, Weitgasser Laurenz
Department of Plastic and Reconstructive Surgery, Marienhospital Stuttgart, Teaching Hospital of the Eberhard Karls University Tuebingen, Boeheimstraße 37, 70199, Stuttgart, Germany.
Department of Plastic Surgery and Hand Surgery, Burn Center, BG University Hospital Bergmannsheil Bochum, Ruhr-University Bochum, Bochum, Germany.
Updates Surg. 2024 Dec;76(8):2943-2952. doi: 10.1007/s13304-024-01980-7. Epub 2024 Sep 7.
Various surgical approaches and pedicles have been described to ensure safe and satisfactory results in reduction mammaplasty. Although different breasts require different techniques, complications are common. This study aims to assess the incidence of complications following primary bilateral reduction mammaplasties across a diverse range of pedicle methods within one of the largest single-center cohorts to date, utilizing machine learning methodologies. A retrospective review of primary bilateral reduction mammaplasties at a single surgical center between January 2016 and March 2020 was performed. Patient medical records and surgical details were reviewed. Complications were compared among three different pedicles. Binary recursive partitioning (CART) machine learning was employed to identify risk factors. In total, 1021 patients (2142 breasts) met the inclusion criteria. The superomedial pedicle was the most frequently utilized (48.0%), with an overall complication rate of 21%. While pedicle-based subgroups demonstrated significant demographic variance, overall complication rates differed most between the inferior (24.9%) and the superomedial pedicle (17.7%). Statistical analysis identified resection weight as the sole significant independent risk factor (OR 1.001, p = 0.007). The machine learning model revealed that total resection weights exceeding 1700 g significantly increased the risk of overall complications, while a sternal notch to nipple (SNN)-distance > 36.5 cm correlated with complications involving the nipple-areola complex (NAC). Higher resection weights are associated with elevated complication rates. Preoperative assessment utilizing SNN-distance can aid in predicting NAC complications.
为确保缩乳术能取得安全且令人满意的效果，人们描述了各种手术方法和蒂部。尽管不同的乳房需要不同的技术，但并发症很常见。本研究旨在利用机器学习方法，在迄今为止最大的单中心队列之一中，评估采用多种蒂部方法进行一期双侧缩乳术后并发症的发生率。对2016年1月至2020年3月期间在单一手术中心进行的一期双侧缩乳术进行回顾性研究。查阅了患者的病历和手术细节。比较了三种不同蒂部的并发症情况。采用二元递归划分（CART）机器学习来识别风险因素。共有1021例患者（2142侧乳房）符合纳入标准。上内侧蒂部是最常用的（48.0%），总体并发症发生率为21%。虽然基于蒂部的亚组显示出显著的人口统计学差异，但总体并发症发生率在下方蒂部（24.9%）和上内侧蒂部（17.7%）之间差异最大。统计分析确定切除重量是唯一显著的独立风险因素（OR 1.001，p = 0.007）。机器学习模型显示，总切除重量超过1700 g会显著增加总体并发症的风险，而胸骨切迹至乳头（SNN）距离> 36.5 cm与乳头乳晕复合体（NAC）相关的并发症有关。较高的切除重量与较高的并发症发生率相关。利用SNN距离进行术前评估有助于预测NAC并发症。
