Development of an Interactive Outcome Estimation Tool for Laparoscopic Roux-en-Y Gastric Bypass in Mexico Based on a Cohort of 1002 Patients

Comparative Analysis of Various Weight Loss Success Criteria Models After Bariatric Metabolic Surgery in Korean Morbid Obese Patients

Purpose

To identify weight loss prediction models by validating previous models using weight loss success criteria.

Materials and Methods

Patients with morbid obesity from 4 hospitals were retrospectively analyzed between Jan 2019 and 2022. Preoperative demographics, postoperative data, and 1-year follow-up weight loss outcomes were compared between 2 groups who underwent laparoscopic sleeve gastrectomy (LSG) and laparoscopic Roux-en-Y gastric bypass (LRYGB). Additionally, the predictive factors for the success of excess weight loss (EWL) (>50%) and total weight loss (TWL) (>25%) were analyzed.

Results

Of the 162 patients, 137 were enrolled during the study period, 75 underwent LSG, and 62 underwent LRYGB. The >50% EWL and >25% TWL 1 year after surgery were 61.3% and 43.1%, respectively. Diabetes mellitus medication use was reduced in 94.8% of patients with type 2 diabetes mellitus. Male sex and body mass index (BMI) were independent risk factors for successful weight loss (SWL) or >50% EWL (odds ratio [OR] for BMI 0.830, 95% confidence interval [CI] 0.764-0.902), whereas achieving >25% TWL was not affected by sex or BMI (OR for BMI 1.010, 95% CI 0.957-1.065). External validation of the prediction models showed an acceptable range of accuracy (adjusted R2 66.5-71.3%).

Conclusion

LSG and LRYGB are feasible and effective bariatric procedures for SWL in Korean patients with morbid obesity. The TWL model was a more appropriate criterion than EWL, and weight loss prediction models may help assess the 1-year outcomes of bariatric surgery.

Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study

BACKGROUND

Weight loss trajectories after bariatric surgery vary widely between individuals, and predicting weight loss before the operation remains challenging. We aimed to develop a model using machine learning to provide individual preoperative prediction of 5-year weight loss trajectories after surgery.

METHODS

In this multinational retrospective observational study we enrolled adult participants (aged ≥18 years) from ten prospective cohorts (including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese Subjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse Obesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and SM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year follow-up after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band. Patients with a previous history of bariatric surgery or large delays between scheduled and actual visits were excluded. The training cohort comprised patients from two centres in France (ABOS and BAREVAL). The primary outcome was BMI at 5 years. A model was developed using least absolute shrinkage and selection operator to select variables and the classification and regression trees algorithm to build interpretable regression trees. The performances of the model were assessed through the median absolute deviation (MAD) and root mean squared error (RMSE) of BMI.

FINDINGS

10 231 patients from 12 centres in ten countries were included in the analysis, corresponding to 30 602 patient-years. Among participants in all 12 cohorts, 7701 (75·3%) were female, 2530 (24·7%) were male. Among 434 baseline attributes available in the training cohort, seven variables were selected: height, weight, intervention type, age, diabetes status, diabetes duration, and smoking status. At 5 years, across external testing cohorts the overall mean MAD BMI was 2·8 kg/m2 (95% CI 2·6-3·0) and mean RMSE BMI was 4·7 kg/m2 (4·4-5·0), and the mean difference between predicted and observed BMI was -0·3 kg/m2 (SD 4·7). This model is incorporated in an easy to use and interpretable web-based prediction tool to help inform clinical decision before surgery.

INTERPRETATION

We developed a machine learning-based model, which is internationally validated, for predicting individual 5-year weight loss trajectories after three common bariatric interventions.

FUNDING

SOPHIA Innovative Medicines Initiative 2 Joint Undertaking, supported by the EU's Horizon 2020 research and innovation programme, the European Federation of Pharmaceutical Industries and Associations, Type 1 Diabetes Exchange, and the Juvenile Diabetes Research Foundation and Obesity Action Coalition; Métropole Européenne de Lille; Agence Nationale de la Recherche; Institut national de recherche en sciences et technologies du numérique through the Artificial Intelligence chair Apprenf; Université de Lille Nord Europe's I-SITE EXPAND as part of the Bandits For Health project; Laboratoire d'excellence European Genomic Institute for Diabetes; Soutien aux Travaux Interdisciplinaires, Multi-établissements et Exploratoires programme by Conseil Régional Hauts-de-France (volet partenarial phase 2, project PERSO-SURG).

Preoperative Prediction of Body Mass Index of Patients with Type 2 Diabetes at 1 Year After Laparoscopic Sleeve Gastrectomy: Cross-Sectional Study

Background: Very few models predict weight loss among type 2 diabetes mellitus (T2D) patients after laparoscopic sleeve gastrectomy (LSG). This retrospective study undertook such a task. Materials and Methods: We identified all patients >18 years old with T2D who underwent primary LSG at our institution and had complete data. The training set comprised 107 patients operated upon during the period April 2011 to June 2014; the validation set comprised 134 patients operated upon during the successive chronological period, July 2014 to December 2015. Sex, age, presurgery BMI, T2D duration, number of T2D medications, insulin use, hypertension, and dyslipidemia were utilized as independent predictors of 1-year BMI. We employed regression analysis, and assessed the goodness of fit and "Residuals versus Fits" plot. Paired sample t-tests compared the observed and predicted BMI at 1 year. Results: The model comprised preoperative BMI (β = 0.757, P = 0.026) + age (β = 0.142, P < 0.0001) with adjusted R² of 0.581 (P < 0.0001), and goodness of fit showed an unbiased model with accurate prediction. The equation was: BMI value 1 year after LSG = 1.777 + 0.614 × presurgery BMI (kg/m²) +0.106 × age (years). For validation, the equation exhibited an adjusted R² 0.550 (P < 0.0001), and the goodness of fit indicated an unbiased model. The BMI predicted by the model fell within -3.78 BMI points to +2.42 points of the observed 1-year BMI. Pairwise difference between the mean 1-year observed and predicted BMI was not significant (-0.41 kg/m², P = 0.225). Conclusions: This predictive model estimates the BMI 1 year after LSG. The model comprises preoperative BMI and age. It allows the forecast of patients' BMI after surgery, hence setting realistic expectations which are critical for patient satisfaction after bariatric surgery. An attainable target motivates the patient to achieve it.

Development and validation of a scoring system for pre-surgical and early post-surgical prediction of bariatric surgery unsuccess at 2 years

Bariatric surgery (BS) is an effective treatment for morbid obesity. However, a simple and easy-to-use tool for the prediction of BS unsuccess is still lacking. Baseline and follow-up data from 300 consecutive patients who underwent BS were retrospectively collected. Supervised regression and machine-learning techniques were used for model development, in which BS unsuccess at 2 years was defined as a percentage of excess-weight-loss (%EWL) < 50%. Model performances were also assessed considering the percentage of total-weight-loss (%TWL) as the reference parameter. Two scoring systems (NAG-score and ENAG-score) were developed. NAG-score, comprising only pre-surgical data, was structured on a 4.5-point-scale (2 points for neck circumference ≥ 44 cm, 1.5 for age ≥ 50 years, and 1 for fasting glucose ≥ 118 mg/dL). ENAG-score, including also early post-operative data, was structured on a 7-point-scale (3 points for %EWL at 6 months ≤ 45%, 1.5 for neck circumference ≥ 44 cm, 1 for age ≥ 50 years, and 1.5 for fasting glucose ≥ 118 mg/dL). A 3-class-clustering was proposed for clinical application. In conclusion, our study proposed two scoring systems for pre-surgical and early post-surgical prediction of 2-year BS weight-loss, which may be useful to guide the pre-operative assessment, the appropriate balance of patients' expectations, and the post-operative care.

Is It Possible to Predict Weight Loss After Bariatric Surgery?-External Validation of Predictive Models

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

Supplementary Information

The online version contains supplementary material available at 10.1007/s11695-021-05341-w.

Weight Loss as the First-Line Therapy in Patients with Severe Obesity and Obstructive Sleep Apnea Syndrome: the Role of Laparoscopic Sleeve Gastrectomy

PURPOSE

The objective of this study is to investigate the effects of laparoscopic sleeve gastrectomy (LSG) on the polysomnographic parameters related to OSAS.

MATERIALS AND METHODS

We conducted this 3-year prospective cohort study in a tertiary care center between December 2016 and December 2019. In total, we enrolled 31 patients with severe obesity who underwent full-night polysomnography (PSG) before LSG. Later, the patients were re-evaluated by full-night PSG 12 months after the surgery.

RESULTS

The mean age of the patients was 44.1 ± 9.6 years. The mean body mass index (BMI) decreased significantly from a mean value of 49.8 ± 8.5 kg/m² at baseline to 33.2 ± 8.2 kg/m² and a percent BMI (%BMI) reduction of 33.8 ± 10.4% and a percent total weight loss (%TWL) of 35.4 ± 10.8% was achieved on the same day of the postsurgical PSG (p < 0.001). There was a remarkable improvement in the AHI (baseline: 36.1 ± 27.1, 12 months after the surgery: 10.3 ± 11.8; difference: 25.8 ± 22.8 events per hour) (p < 0.001). Importantly, there was a decrease in the percentage of non-rapid eye movement (NREM) 2 (p < 0.001), whereas NREM 3 and REM stages witnessed a significant increase (p = 0.001 and p < 0.001, respectively) after the surgery.

CONCLUSION

The results of this study showed that weight loss after LSG yields improvement not only in AHI but also in many polysomnographic parameters such as sleep quality and desaturation indices.