From the Departments of Plastic, Burn, and Wound Surgery.
General Surgery, University of Kansas Medical Center, Kansas City, KS.
Ann Plast Surg. 2023 Aug 1;91(2):282-286. doi: 10.1097/SAP.0000000000003621.
Necrosis of the nipple-areolar complex (NAC) is the Achilles heel of nipple-sparing mastectomy (NSM), and it can be difficult to assess which patients are at risk of this complication (Ann Surg Oncol 2014;21(1):100-106).
To develop and validate a model that accurately predicts NAC necrosis in a prospective cohort.
Data were collected from a retrospectively reviewed cohort of patients who underwent NSM and immediate breast reconstruction between January 2015 and July 2019 at our institution, a high -volume, tertiary academic center. Preoperative clinical characteristics, operative variables, and postoperative complications were collected and linked to NAC outcomes. These results were utilized to train a random-forest classification model to predict necrosis. Our model was then validated in a prospective cohort of patients undergoing NSM with immediate breast reconstruction between June 2020 and June 2021.
Model predictions of NAC necrosis in the prospective cohort achieved an accuracy of 97% (95% confidence interval [CI], 0.89-0.99; P = 0.009). This was consistent with the accuracy of predictions in the retrospective cohort (0.97; 95% CI, 0.95-0.99). A high degree of specificity (0.98; 95% CI, 0.90-1.0) and negative predictive value (0.98; 95% CI, 0.90-1.0) were also achieved prospectively. Implant weight was the most predictive of increased risk, with weights greater than 400 g most strongly associated with NAC ischemia.
Our machine learning model prospectively predicted cases of NAC necrosis with a high degree of accuracy. An important predictor was implant weight, a modifiable risk factor that could be adjusted to mitigate the risk of NAC necrosis and associated postoperative complications.
乳头乳晕复合体（NAC）坏死是保乳乳房切除术（NSM）的致命弱点，评估哪些患者有发生这种并发症的风险具有一定难度（Ann Surg Oncol 2014;21(1):100-106）。
开发和验证一种可准确预测前瞻性队列中 NAC 坏死的模型。
数据来自于我院（一家高容量的三级学术中心）于 2015 年 1 月至 2019 年 7 月期间接受 NSM 和即刻乳房重建的回顾性队列患者。收集了术前临床特征、手术变量和术后并发症，并将其与 NAC 结果相关联。利用这些结果训练随机森林分类模型来预测坏死。然后，在 2020 年 6 月至 2021 年 6 月期间接受 NSM 加即刻乳房重建的前瞻性队列患者中验证了我们的模型。
前瞻性队列中 NAC 坏死的模型预测准确率为 97%（95%置信区间 [CI]，0.89-0.99；P=0.009）。这与回顾性队列中的预测准确率（0.97；95% CI，0.95-0.99）一致。高特异性（0.98；95% CI，0.90-1.0）和阴性预测值（0.98；95% CI，0.90-1.0）也在前瞻性队列中得到了证实。植入物重量是预测风险增加的最主要因素，重量大于 400g 与 NAC 缺血的相关性最强。
我们的机器学习模型前瞻性地预测了 NAC 坏死病例，准确率很高。一个重要的预测因素是植入物重量，这是一个可调节的风险因素，可以通过调整来降低 NAC 坏死和相关术后并发症的风险。
