3D Body shape for regional and appendicular body composition estimation.

Body composition is correlated to bone mineral density, muscle strength, and physical performance. This is important for diagnosing conditions like sarcopenia, which is defined as the age-associated decrease in muscle mass leading to decreased mobile function, increased frailty, and imbalance. Existing methods for body composition measurement either suffer from inaccurate results or require expensive equipment such as Dual-energy x-ray absorptiometry (DXA). Although DXA measures lean mass and not muscle mass, previous studies have considered extremity lean mass as appendicular skeletal muscle mass (ASMM) approximation. In this study, we develop a new shape descriptor to predict regional body composition (in particular, regional lean mass) from 3D body shapes. In addition, we propose a neural network for ASMM assessment which is calculated by lean mass. We evaluate the effectiveness by comparing adjusted R-Squared values and Root Mean Square Error (RMSE). In our experiment, the regression models utilizing level circumference as the training feature outperforms all regional anthropometric measurements and lowers the average RMSE by about 21%. For ASMM, the proposed neural network, which combines shape features and demographic features, surpasses all other traditional regression models and reaches the lowest RMSE at 1.85 kg. Compared to the vanilla linear regression model, our approach improves the RMSE by 17%. The experimental results suggest that the 3D body shape has the potential to be used to predict body composition, and in particular lean mass, for the whole body as well as specific regions of the body.

Predictors of morbidity in revisional bariatric surgery and bariatric emergencies at an MBSAQIP-accredited community hospital.

INTRODUCTION: Bariatric surgery revisions and emergencies are associated with higher morbidity and mortality compared to primary bariatric surgery. No formal outcome benchmarks exist that distinguish MBSAQIP-accredited centers in the community from unaccredited institutions. METHODS: A retrospective chart review was conducted on 53 bariatric surgery revisions and 61 bariatric surgical emergencies by a single surgeon at a high-volume community hospital accredited program from 2018 to 2020. Primary outcomes were complications or deaths occurring within 30-days of the index procedure. Secondary outcomes included operative time, leaks, surgical site occurrences (SSOs), and deep surgical site infections. RESULTS: There were no significant differences in the demographic characteristics of the study groups. Mean operative time was significantly longer for revisions as compared to emergency operations (149.5 vs. 89.4 min). Emergencies had higher surgical site infection (5.7% vs. 21.3%, p < 0.05) and surgical site occurrence (SSO) (1.9% vs. 29.5%, p < 0.05) rates compared to revisions. Logistic regression analysis identified several factors to be predictive of increased risk of morbidity: pre-operative albumin < 3.5 g/dL (p < 0.05), recent bariatric procedure within the last 30 days (p < 0.05), prior revisional bariatric surgery (p < 0.05), prior duodenal switch (p < 0.05), and pre-operative COPD (p < 0.05). CONCLUSION: Bariatric surgery revisions and emergencies have similar morbidity and mortality, far exceeding those of the primary operation. Outcomes comparable to those reported by urban academic centers can be achieved in community hospital MBSAQIP-accredited centers.

Robotic Roux en Y gastric bypass can be safe and cost-effective in a rural setting: clinical outcomes from a community hospital bariatric program.

Robotic Roux en Y gastric bypass (R-RYGB) is becoming more common due to the shifting trend toward robotic gastrointestinal surgery. The goal of this study is to determine if R-RYGB can be safely implemented at a robotic bariatric surgery program in a community hospital with similar results to laparoscopic RYGB (L-RYGB) in a cost-effective manner. A total of 50 R-RYGB procedures were performed with the Xi and the X da Vinci systems and compared with 50 L-RYGB cases by a single surgeon from October 2018 to January 2020 at an acute-care community hospital in a rural setting with a high-volume MBSAQIP-accredited program. A retrospective chart review was conducted with IRB approval and statistical analysis of 30-day morbidity, mortality, re-interventions, and resolution of co-morbidities, with financial analysis of cost reduction. Both groups were similar in age, gender, ASA class, co-morbidities, and body mass index (BMI). There was no mortality or anastomotic leak. The 30-day morbidity for R-RYGB was 10.0% with a re-operation rate of 4.0%. There were no conversions to open, and the mean hospital length of stay was 2.22 ± 1.19 days. There were no statistically significant differences between R-RYGB and L-RYGB with respect to any measured outcome, including intraoperative time (121.94 vs. 113.52, respectively; p = 0.1495). However, when incidences and percentages were used, R-RYGB had improved performance for most of the outcomes measuring safety. There was an average cost reduction of $816.90 per case (total saving of $40,845.00 for 50 cases) in the R-RYGB by transitioning from a hybrid approach to a totally robotic approach. R-RYGB appears to be as safe as L-RYGB and can be performed in a rural community hospital while maintaining a low complication rate, achieving a high co-morbidity resolution rate, and saving costs with a totally robotic approach.