Quantitative analysis of pressure levels in manual lymphatic drainage across stages of breast cancer-related lymphedema: implications for optimized treatment protocols.

Abstract

To quantify the pressure levels necessary for effective Manual Lymphatic Drainage (MLD) in managing Breast Cancer-Related Lymphedema (BCRL) across various stages, and to contribute to the development of standardized protocols for MLD therapy. The study included 42 patients with BCRL (Stages I-III) and 14 certified lymphedema therapists. Forearms and upper arm circumferences were measured pre and post a 21-day MLD intervention. A tactile sensor system recorded the applied pressure during treatment. The data were preprocessed and statistically analyzed to assess pressure patterns and their stage-specific impacts on lymphedema. The mean age of the patients was 52.4years, and that of the therapists was 39.1years. A statistically significant reduction in arm circumference was observed post-MLD treatment (P < 0.05). The pressure applied varied across stages: I forearm 16.5-20.1mmHg, I upper arm 16.1-20.7mmHg; II forearm 16.6-19.8mmHg, II upper arm 19.7-23.8mmHg; III forearm 29.3-34.3mmHg, III upper arm 29.7-34.3mmHg. No statistically significant difference was found between forearm and upper arm treatment pressures within Stages I (P = 0.283) and III (P = 0.08), while Stage II exhibited a significant difference (P < 0.001). Across the same treatment area, pressures for Stages I and II in the forearm were significantly lower than those in Stage III (P < 0.001). The treatment pressure differences between forearm stages I and II were not statistically significant (P > 0.05). Differences in upper arm treatment pressures across Stages I, II, and III were also statistically significant (P < 0.001). The study provides quantitative evidence on the pressure ranges needed for MLD across different stages of BCRL. It highlights the importance for stage-specific pressure adjustments to optimize treatment outcomes. These findings contribute to the existing body of knowledge on MLD and offer valuable data that could inform the development of rehabilitation technologies, including intelligent robots and visualization systems, as well as enhance therapist training programs.