The Traffic Light Planning Algorithm for Breast Augmentation Mastopexy: A Case Series Analysis on Optimizing Early Ptosis

Background Breast augmentation is one of the most commonly performed aesthetic procedures, yet cases involving breast ptosis often require a more individualized approach. Determining the optimal strategy for combining augmentation with lift procedures can be challenging, particularly for surgeons early in their aesthetic practice. This study introduces the Traffic Light Planning Algorithm as a structured and objective framework to assess breast anatomy, guide implant positioning, and determine the need for adjunctive lift procedures. The algorithm categorizes patients into three groups (green, amber, and red) to help select the most appropriate management plan. This paper focuses on the amber zone, representing cases with early ptosis, which often present the greatest challenge in achieving optimal correction. Objectives The primary aim of this study was to evaluate the safety and patient satisfaction outcomes associated with the Traffic Light Planning Algorithm over five years (2018-2023). The secondary objective was to conduct a focused retrospective case series analysis of the "amber zone" over 12 months (December 2022-December 2023). This subset analysis aimed to assess whether Mentor MemoryGel Xtra implants (Mentor Worldwide LLC, Irvine, CA, USA) could correct early ptosis without adjunctive mastopexy and to provide supporting evidence for the validity of the algorithm. A representative case report is also included to illustrate the practical application of this approach. Methods A comprehensive description of the algorithm is provided. Over the five-year study period, patient satisfaction, complication rates, and revision rates were analyzed. The 12-month subset analysis further explored patient satisfaction and various patient and surgical factors through cross-tabulation. This retrospective study design was appropriate for assessing real-world outcomes related to patient satisfaction and complications associated with this new structured planning algorithm. The Likert scale was used as an effective tool for evaluating patient satisfaction. Results During the study period, 9,000 breast consultations and 3,000 implant surgeries were performed by the senior author (MM). Overall, 93% of patients reported high satisfaction with their outcomes, with low revision rates of 2% for primary augmentations and 6% for augmentation mastopexies. This method proved particularly effective for cases of early ptosis, providing a structured and reproducible decision-making framework. The subset case series of 59 patients over a 12-month period showed high satisfaction levels: 54 (91.5%) were "very happy," 3 (5.1%) were "happy," 1 (1.7%) was "satisfied," and 1 (1.7%) was "not happy." Complications were reported in four cases (6.8%), including bilateral bottoming out, two cases of implant loss due to infection, and one case of unilateral capsular contracture. The remaining 55 patients (93.2%) experienced no postoperative complications. Conclusions The algorithm provides a reliable and objective framework for planning breast augmentation, particularly valuable for surgeons new to aesthetic practice. By promoting personalized, tissue-based surgical planning, this approach helps minimize complications and enhance patient satisfaction. The case series further demonstrates that applying the algorithm, together with the use of Mentor MemoryGel Xtra implants, can offer an effective strategy for avoiding mastopexy in appropriately selected amber zone patients.