Outcome of Sleeve Gastrectomy Versus Roux-en-Y Gastric Bypass for Patients with Super Obesity (Body Mass Index > 50 kg/m2)

PURPOSE

With increasing BMI, the complexity of treating patients with obesity rises. The focus of this study is to investigate the effects of sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB) on perioperative morbidity and remission of comorbidities at 3 years in patients with a BMI > 50 kg/m².

MATERIALS AND METHODS

A retrospective multicenter analysis of a prospectively maintained database was performed to enroll patients with a 3-year follow-up after SG or RYGB between 2005 and 2019 and a BMI of > 50 kg/m² preoperatively. Patients' BMI and comorbidity status were recorded preoperatively.

RESULTS

We analyzed data from 2939 patients who had at least a preoperative BMI > 50 kg/m². A total of 1278 patients underwent RYGB surgery, and 1661 underwent SG. The distribution of sex, BMI, hypertension, reflux, and sleep apnea was significant between the two groups. Three years after surgery, the percent excess weight loss (%EWL) was 62.21% in RYGB and 55.87% in SG (p < 0.001). The change in hypertension (p < 0.001) and reflux (p < 0.001) was significantly in favor of RYGB. The change in diabetes mellitus was not significant between the two groups (p > 5%). There was a minimal difference in sleep apnea in favor of SG (p < 0.001). Mortality and overall complication rates were not significant in either group.

CONCLUSION

Both procedures positively affected comorbidities, BMI, and %EWL in patients with super obesity 3 years after surgery. In some categories, RYGB was better than SG. Nevertheless, the decision between the two methods remains a matter of the surgeon's experience and the patient's general condition.

Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients

The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitus, and/or arterial hypertension, using conventional or office-based, laboratory-based blood biomarkers and carotid/femoral ultrasound image-based phenotypes. Two kinds of data (CVD risk factors and presence of CVD-defined as stroke, or myocardial infarction, or coronary artery syndrome, or peripheral artery disease, or coronary heart disease) as ground truth, were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3 years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) to study their effect on the ML classifiers. Three kinds of ML classifiers (Random Forest, Support Vector Machine, and Linear Discriminant Analysis) were applied in a two-fold cross-validation framework using the data augmented by synthetic minority over-sampling technique (SMOTE) strategy. The performance of the ML classifiers was recorded. In this cohort with overall 46 CVD risk factors (covariates) implemented in an online cardiovascular framework, that requires calculation time less than 1 s per patient, a mean accuracy and area-under-the-curve (AUC) of 98.40% and 0.98 (p < 0.0001) for CVD presence detection at visit 1, and 98.39% and 0.98 (p < 0.0001) at visit 2, respectively. The performance of the cardiovascular framework was significantly better than the classical CVD risk score. The ML paradigm proved to be powerful for CVD prediction in individuals at medium to high cardiovascular risk.