Prospective assessment of the quality of life and nipple sensation after gender-affirming chest surgery

Background: In the existing literature, assessing transgender patients' quality of life after surgery, especially using standardized surveys, is rare. The nipple sensation regarding the operating technique has neither been studied in a prospective nor standardized way. Methods: For one year, we prospectively assessed transgender patients operated on for a gender-affirming chest surgery in our unit. Each patient answered the BREAST-Q (c) survey and the BODY-Q (c) chest module survey before and six months after the surgery. In addition, a measure of nipple sensation was performed using Semmes-Weinstein monofilaments pre-and post-operatively to compare surgical techniques. Results: Fifty-one patients (102 breasts) were included in our study. The average age was 23.1 years and the average BMI was 24.8 kg/m(2). Twenty-one patients (45%) had double incision and free nipple graft mastectomy, 14 (27%) patients had double incision and inferior pedicle mastectomy, while the 14 (27%) other patients had a semi-circular technique. Our study shows an improvement in all the scores of the surveys after surgery (p < 0.0001). Patients with double incision and inferior pedicle mastectomies rated a significantly higher satisfaction with nipples (p = 0.013) and significantly better sexual well-being (p = 0.007) than other techniques. In addition, preservation of nipple sensation was shown in patients operated by semi-circular technique (p < 0.001) and inferior pedicle technique (p < 0.0001). Conclusions: Our prospective study confirms the significant improvement in the quality of life of transgender patients after chest gender-affirming surgery. Double incision with inferior pedicle seems to provide better satisfaction with nipples, higher sexual well-being, and preservation of nipple sensation. (c) 2024 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.