Explanations for unsuccessful weight loss among bariatric surgery candidates.

BACKGROUND: Our objective was to analyze subjective explanations for unsuccessful weight loss among bariatric surgery candidates. METHODS: This was a retrospective analysis of 909 bariatric surgery candidates (78.2% female, average body mass index [BMI] 47.3) at a university center from 2001 to April 2007 who answered an open-ended question about why they were unable to lose weight. We generated a coding scheme for answers to the question and established inter-rater reliability of the coding process. Associations with demographic parameters and initial BMI were tested. RESULTS: The most common categories of answers were nonspecific explanations related to diet (25.3%), physical activity (21.0%), or motivation (19.7%), followed by diet-related motivation (12.7%) and medical conditions or medications affecting physical activity (12.7%). Categories related to time, financial cost, social support, physical environment, and knowledge occurred in less than 4% each. Men were more likely than women to cite a medical condition or medication affecting physical activity (19.2% vs 10.8%, P = 0.002, odds ratio [OR] = 1.96, 95% confidence interval [CI] = 1.28-2.99) but less likely to cite diet-related motivation (7.1% vs 14.2%, P = 0.008, OR = 0.46, 95% CI = 0.26-0.82). CONCLUSIONS: Our findings suggest that addressing diet, physical activity, and motivation in a comprehensive approach would meet the stated needs of obese patients. Raising patient awareness of under-recognized barriers to weight loss, such as the physical environment and lack of social support, should also be considered. Lastly, anticipating gender-specific attributions may facilitate tailoring of interventions.

Learning curve for robotic-assisted laparoscopic colorectal surgery.

BACKGROUND: Robotic-assisted laparoscopic surgery (RALS) is evolving as an important surgical approach in the field of colorectal surgery. We aimed to evaluate the learning curve for RALS procedures involving resections of the rectum and rectosigmoid. METHODS: A series of 50 consecutive RALS procedures were performed between August 2008 and September 2009. Data were entered into a retrospective database and later abstracted for analysis. The surgical procedures included abdominoperineal resection (APR), anterior rectosigmoidectomy (AR), low anterior resection (LAR), and rectopexy (RP). Demographic data and intraoperative parameters including docking time (DT), surgeon console time (SCT), and total operative time (OT) were analyzed. The learning curve was evaluated using the cumulative sum (CUSUM) method. RESULTS: The procedures performed for 50 patients (54% male) included 25 AR (50%), 15 LAR (30%), 6 APR (12%), and 4 RP (8%). The mean age of the patients was 54.4 years, the mean BMI was 27.8 kg/m(2), and the median American Society of Anesthesiologists (ASA) classification was 2. The series had a mean DT of 14 min, a mean SCT of 115.1 min, and a mean OT of 246.1 min. The DT and SCT accounted for 6.3% and 46.8% of the OT, respectively. The SCT learning curve was analyzed. The CUSUM(SCT) learning curve was best modeled as a parabola, with equation CUSUM(SCT) in minutes equal to 0.73 × case number(2) - 31.54 × case number - 107.72 (R = 0.93). The learning curve consisted of three unique phases: phase 1 (the initial 15 cases), phase 2 (the middle 10 cases), and phase 3 (the subsequent cases). Phase 1 represented the initial learning curve, which spanned 15 cases. The phase 2 plateau represented increased competence with the robotic technology. Phase 3 was achieved after 25 cases and represented the mastery phase in which more challenging cases were managed. CONCLUSIONS: The three phases identified with CUSUM analysis of surgeon console time represented characteristic stages of the learning curve for robotic colorectal procedures. The data suggest that the learning phase was achieved after 15 to 25 cases.