Background Bariatric surgery is the most effective obesity treatment. Weight loss varies among patients, and not everyone achieves desired outcome. Identification of predictive factors for weight loss after bariatric surgery resulted in several prediction tools proposed. We aimed to validate the performance of available prediction models for weight reduction 1 year after surgical treatment. Materials and Methods The retrospective analysis included patients after Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) who completed 1-year follow-up. Postoperative body mass index (BMI) predicted by 12 models was calculated for each patient. The correlation between predicted and observed BMI was assessed using linear regression. Accuracy was evaluated by squared Pearson’s correlation coefficient (R2). Goodness-of-fit was assessed by standard error of estimate (SE) and paired sample t test between estimated and observed BMI. Results Out of 760 patients enrolled, 509 (67.00%) were women with median age 42 years. Of patients, 65.92% underwent SG and 34.08% had RYGB. Median BMI decreased from 45.19 to 32.53kg/m2 after 1 year. EWL amounted to 62.97%. All models presented significant relationship between predicted and observed BMI in linear regression (correlation coefficient between 0.29 and 1.22). The best predictive model explained 24% variation of weight reduction (adjusted R2=0.24). Majority of models overestimated outcome with SE 5.03 to 5.13kg/m2. Conclusion Although predicted BMI had reasonable correlation with observed values, none of evaluated models presented acceptable accuracy. All models tend to overestimate the outcome. Accurate tool for weight loss prediction should be developed to enhance patient’s assessment. Graphical abstract

Background: In recent years the interest of Polish medical students in choosing a career in surgical subspecialties has been declining. In the face of growing demand for surgical procedures it seems essential to find the reasons responsible for that situation. The aim of the study was to evaluate the level of interest in pursuing surgical careers among Polish medical students and to identify factors that may influence their decision. Material and methods: The anonymous questionnaire was distributed electronically among students from 11 different Polish medical universities. Results: We surveyed 595 individuals (190 male and 405 female). 48% of them declared the interest in choosing surgical subspecialty as a career. The percentage of students who considered it before medical school was higher and reached almost 65%. Slight or no interest in surgery as a field of study (OR=20.6), self-assessment of surgical predispositions as unsatisfactory (OR=14.3), feeling unable to enter and accomplish surgical specialty (OR=5.2), being discouraged by partner (OR=3.4), negative past experience with surgical environment (OR=3.2), not having a surgeon as a mentor (OR=2.6), no authorship of journal articles or congress presentations (OR=1.9) and first contact with operating theatre >2nd year of study (OR=1.9) were found to be the independent risk factors of giving up on surgical career. Conclusions: Most medical students are interested in pursuing surgical specialty at some time during their education. However, being discouraged by lifestyle issues, surgical training quality or experiencing gender discrimination they often resign from such career path.

Purpose Bariatric surgery has proven to be the most efficient treatment for obesity and type 2 diabetes mellitus (T2DM). Despite detailed qualification, desirable outcome after an intervention is not achieved by every patient. Various risk prediction models of diabetes remission after metabolic surgery have been established to facilitate the decision-making process. The purpose of the study is to validate the performance of available risk prediction scores for diabetes remission a year after surgical treatment and to determine the optimal model. Methods A retrospective analysis comprised 252 patients who underwent Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) between 2009 and 2017 and completed 1-year follow-up. The literature review revealed 5 models, which were subsequently explored in our study. Each score relationship with diabetes remission was assessed using logistic regression. Discrimination was evaluated by area under the receiver operating characteristic (AUROC) curve, whereas calibration by the Hosmer–Lemeshow test and predicted versus observed remission ratio. Results One year after surgery, 68.7% partial and 21.8% complete diabetes remission and 53.4% excessive weight loss were observed. DiaBetter demonstrated the best predictive performance (AUROC 0.81; 95% confidence interval (CI) 0.71–0.90; p-value > 0.05 in the Hosmer–Lemeshow test; predicted-to-observed ratio 1.09). The majority of models showed acceptable discrimination power. In calibration, only the DiaBetter score did not lose goodness-of-fit in all analyzed groups. Conclusion The DiaBetter score seems to be the most appropriate tool to predict diabetes remission after metabolic surgery since it presents adequate accuracy and is convenient to use in clinical practice. There are no accurate models to predict T2DM remission in a patient with advanced diabetes.

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