Changes in shoulder muscle activity pattern on surface electromyography after breast cancer surgery.

BACKGROUND AND OBJECTIVES: Alterations in muscle activation and restricted shoulder mobility, which are common in breast cancer patients, have been found to affect upper limb function. The purpose of this study was to determine muscle activity patterns, and to compare the prevalence of abnormal patterns among the type of breast surgery. METHODS: In total, 274 breast cancer patients were recruited after surgery. Type of breast surgery was divided into mastectomy without reconstruction (Mastectomy), reconstruction with tissue expander/implant (TEI), latissimus dorsi (LD) flap, or transverse rectus abdominis flap (TRAM). Activities of shoulder muscles were measured using surface electromyography. Experimental analysis was conducted using a Gaussian filter smoothing method with regression. RESULTS: Patients demonstrated different patterns of muscle activation, such as normal, lower muscle electrical activity, and tightness. After adjusting for BMI and breast surgery, the odds of lower muscle electrical activity and tightness in the TRAM are 40.2% and 38.4% less than in the Mastectomy only group. The prevalence of abnormal patterns was significantly greater in the ALND than SLNB in all except TRAM. CONCLUSIONS: Alterations in muscle activity patterns differed by breast surgery and reconstruction type. For breast cancer patients with ALND, TRAM may be the best choice for maintaining upper limb function.

A Personalized and Learning Approach for Identifying Drugs with Adverse Events.

PURPOSE: Adverse drug events (ADEs) are associated with high health and financial costs and have increased as more elderly patients treated with multiple medications emerge in an aging society. It has thus become challenging for physicians to identify drugs causing adverse events. This study proposes a novel approach that can improve clinical decision making with recommendations on ADE causative drugs based on patient information, drug information, and previous ADE cases. MATERIALS AND METHODS: We introduce a personalized and learning approach for detecting drugs with a specific adverse event, where recommendations tailored to each patient are generated using data mining techniques. Recommendations could be improved by learning the associations of patients and ADEs as more ADE cases are accumulated through iterations. After consulting the system-generated recommendations, a physician can alter prescriptions accordingly and report feedback, enabling the system to evolve with actual causal relationships. RESULTS: A prototype system is developed using ADE cases reported over 1.5 years and recommendations obtained from decision tree analysis are validated by physicians. Two representative cases demonstrate that the personalized recommendations could contribute to more prompt and accurate responses to ADEs. CONCLUSION: The current system where the information of individual drugs exists but is not organized in such a way that facilitates the extraction of relevant information together can be complemented with the proposed approach to enhance the treatment of patients with ADEs. Our illustrative results show the promise of the proposed system and further studies are expected to validate its performance with quantitative measures.