From the Division of Plastic Surgery, Department of Surgery, the Department of Preventive Medicine and Community Health, and the School of Medicine, University of Texas Medical Branch; and Howland Plastic Surgery.
Plast Reconstr Surg. 2019 Jul;144(1):18e-27e. doi: 10.1097/PRS.0000000000005712.
Reduction mammaplasty is a highly effective procedure for treatment of symptomatic macromastia. Prediction of resection weight is important for the surgeon and the patient, but none of the current prediction models is widely accepted. Insurance carriers are arbitrarily using resection weight to determine medical necessity, despite published literature supporting that resection weight does not correlate with symptomatic relief. What is the most accurate method of predicting resection weight and what is its role in breast reduction surgery?
The authors conducted a retrospective review of patients who underwent reduction mammaplasty at a single institution from 2012 to 2017. A senior biostatistician performed multiple regression analysis to identify predictors of resection weight, and linear regression models were created to compare each of the established prediction scales to actual resected weight. Patient outcomes were evaluated.
Three-hundred fourteen patients were included. A new prediction model was created. The Galveston scale performed the best (R = 0.73; p < 0.001), whereas the Schnur scale performed the worst (R = 0.43; p < 0.001). The Appel and Descamps scales had variable performance in different subcategories of body mass index and menopausal status (p < 0.01). Internal validation confirmed the Galveston scale's best predictive value; 38.6 percent and 28.9 percent of actual breast resection weights were below Schnur prediction and 500-g minimum, respectively, yet 97 percent of patients reported symptomatic improvement or relief.
The authors recommend a patient-specific and surgeon-specific approach for prediction of resection weight in breast reduction. The Galveston scale fits the best for older patients with higher body mass indices and breasts requiring large resections. Medical necessity decisions should be based on patient symptoms, physical examination, and the physician's clinical judgment.
CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, IV.
缩乳术是治疗有症状的巨乳症的一种非常有效的方法。切除重量的预测对医生和患者都很重要,但目前没有一种预测模型被广泛接受。尽管有文献支持切除重量与症状缓解无关,但保险公司仍任意使用切除重量来确定医疗必要性。预测切除重量最准确的方法是什么,它在乳房缩小手术中的作用是什么?
作者对 2012 年至 2017 年在一家机构接受缩乳术的患者进行了回顾性研究。一名资深生物统计学家进行了多元回归分析,以确定切除重量的预测因子,并建立了线性回归模型,以比较每个已建立的预测量表与实际切除重量。评估患者的结局。
共纳入 314 例患者。创建了一个新的预测模型。Galveston 量表的表现最好(R = 0.73;p < 0.001),而 Schnur 量表的表现最差(R = 0.43;p < 0.001)。Appel 和 Descamps 量表在不同的体重指数和绝经状态亚组中的表现不同(p < 0.01)。内部验证证实了 Galveston 量表的最佳预测值;Schnur 预测和 500 克最小切除重量分别有 38.6%和 28.9%的实际乳房切除重量低于实际切除重量,但 97%的患者报告症状改善或缓解。
作者建议采用患者特异性和术者特异性方法预测乳房缩小术中的切除重量。Galveston 量表最适合年龄较大、体重指数较高且需要大切除的患者。医疗必要性的决策应基于患者的症状、体格检查和医生的临床判断。
临床问题/证据水平:诊断,IV。