Patient satisfaction of breast reconstructive surgery following mastectomy in Brunei.

OBJECTIVE: To evaluate the impact of Breast Reconstructive Surgery (BRS) on patients' satisfaction and quality of life following mastectomy for breast cancer. METHODS: A multi-method design study comprising quantitative and qualitative research was conducted between October to December 2019. The quantitative component consisted of a cross-sectional study using the Breast-Q questionnaire and the qualitative component involved in-depth interviews with eligible patients (N = 16) who underwent BRS following mastectomy for breast cancer. Quantitative analysis was performed including Fisher's exact test and One-way Analysis of Variance where a p-value of <0.05 was regarded as statistically significant. Qualitative data was thematically analysed using Braun and Clarke's thematic analysis. RESULTS: 14 out of 16 eligible patients participated in the study. We observed the lowest-scale score was for 'satisfaction with nipples' (mean score 32.7), followed by 'physical well-being: abdomen' (mean score 69.5). Despite a median score of 70 for 'satisfaction with breasts', patients were satisfied with the overall outcome of BRS (median score 80.5). Thematic analysis yielded three themes i.e. "I feel beautiful again" that described patients' satisfaction with aesthetic outcome with autologous reconstruction; "Striving for normality" that indicated BRS established back a sense of normality and improve their self-confidence and lastly, "I was well taken care of" highlighted the importance of providing 'well-informed' care to ensure overall satisfaction of their BRS journey. CONCLUSION: The uptake of BRS remains low since its availability in 2012, despite an overall increase in breast cancer cases in Brunei annually. Patients who underwent BRS have shown an increase BREAST-Q scores in breast satisfaction, psychosocial and sexual well-being after breast cancer treatment. Delivering high-quality patient-centred services and providing adequate information can influence the level of satisfaction for overall outcome. BRS should be considered as an important healthcare priority in Brunei and routinely be offered in the management of breast cancer.

Machine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students.

BACKGROUND: Since the onset of the COVID-19 pandemic in early 2020, the importance of timely and effective assessment of mental well-being has increased dramatically. Machine learning (ML) algorithms and artificial intelligence (AI) techniques can be harnessed for early detection, prognostication and prediction of negative psychological well-being states. METHODS: We used data from a large, multi-site cross-sectional survey consisting of 17 universities in Southeast Asia. This research work models mental well-being and reports on the performance of various machine learning algorithms, including generalized linear models, k-nearest neighbor, naïve Bayes, neural networks, random forest, recursive partitioning, bagging, and boosting. RESULTS: Random Forest and adaptive boosting algorithms achieved the highest accuracy for identifying negative mental well-being traits. The top five most salient features associated with predicting poor mental well-being include the number of sports activities per week, body mass index, grade point average (GPA), sedentary hours, and age. CONCLUSIONS: Based on the reported results, several specific recommendations and suggested future work are discussed. These findings may be useful to provide cost-effective support and modernize mental well-being assessment and monitoring at the individual and university level.