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Reference centile curves for total weight loss in Asian bariatric patients undergoing gastric bypass. (A) Post-estimation adjusted predictions for female patients from quantile regression models with full-factorial interaction terms between post-operative month, type of procedure, and sex. (B) Post-estimation adjusted predictions for male patients from quantile regression models with full-factorial interaction terms between post-operative month, type of procedure, and sex. Note that more detailed (further adjusted for Asian ethnicity and initial pre-operative BMI), printable A4 size versions of the centile charts are provided in the Supplementary Appendix
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Background
Following bariatric surgery, accurate charting of weight loss and regain is crucial. Various preoperative factors affect postoperative weight loss, including age, sex, ethnicity, and surgical type. These are not considered by current weight loss metrics, limiting comparison of weight loss outcomes between patients or centers and across t...
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Objectives
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Purpose
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Citations
... Although bariatric surgery is effective in producing long-term weight loss, there is wide variability in weight loss among individual patients. In our centre, centile charts are used for the monitoring of weight trajectories postoperatively and to facilitate realistic and personalised goal setting [52]. CP practitioners were trained to ensure that weight loss progress was appropriate. ...
Background: A collaborative prescribing (CP) practice model, established by the endocrinologists, pharmacists, and advanced practice nurses, aims to provide for the postoperative monitoring and medical and nutritional management of stable patients after bariatric surgery. Method: Under the CP agreement, endocrinologists refer patients who have undergone bariatric surgery with stable medical conditions to CP practitioners, comprising senior pharmacists and advanced practice nurses. CP practitioners review the patient’s weight loss progress, blood test results and vitals, the sufficiency of micronutrient repletion, adherence to supplements and medications, and chronic disease control. CP practitioners can prescribe and adjust the medications and supplements, in accordance with a clinical evaluation and standard guidance. Patients who require immediate attention due to complications or red flags are referred to the primary endocrinologist for further management. Results: From 5 May 2020 to 30 September 2023, CP practitioners provided 672 consultations. At least 68% and 80% of patients achieved appropriate weight loss post-surgery during the acute and maintenance phases, respectively. Less than 10% of the patients presented with anaemia and iron deficiency, and vitamin B12, folate and vitamin D deficiency. More than 80% of patients achieved a HbA1c of less than 7%. Conclusions: The CP practice framework provides a sustainable and viable model to facilitate optimal outcomes after bariatric surgery.
... Two advantages of percentile-based assessments are dynamic monitoring and setting high and low extreme bonds [14]. These charts identify individuals whose weight loss patterns are beyond the normal range and provide guidance for managing expectations and evaluating outcomes [9,15]. Few studies investigated percentile charts after surgery in bariatric patients [13,14,16]. ...
Purpose
Using a single percentile chart provides us with high standards for the evaluation and accurate investigation of sufficient weight loss after bariatric surgery, counseling, and treating patients in an evidence-based way. Creating percentile charts of weight loss for gastric bypass is the aim of this study.
Materials and Methods
This retrospective study was based on data from patients who underwent RYGB or OAGB from February 2008 to February 2020. The lambda-mu-sigma (LMS) method was used to estimate the reduction in body mass index (BMI) and six other metrics measured throughout post-operative follow-up. Percentile charts for various metrics have been presented for the first 2 years’ post-surgery. We applied a bootstrap sampling method to evaluate percentile validity.
Results
We recruited 2579 and 1943 patients who underwent OAGB (75% female) and RYGB (84% female) and were between the ages of 18 and 70 years. The preoperative BMI of patients in the OAGB group was higher than in the RYGB group. Concerning RYGB weight reduction results, the maximum percentage of excess weight loss (%EWL) occurs 18 months after surgery and is steady at 24 months. Far above 50%, EWL is achieved after 6 months. OAGB weight loss follows the same trend as RYGB; at 6 months, the %EWL values are slightly higher than RYGB.
Conclusions
We present the first bariatric weight loss percentile chart for OAGB. It allows evaluation of sufficient and insufficient weight loss at any post-operative point in a visual aspect. Furthermore, it predicts prospective outcomes and guides patient monitoring.
Graphical Abstract
... (3 years), 6.2±1.2 (4 years) and 6.2±1.2 (5 years). (A detailed breakdown of clinical outcomes after each type of bariatric surgery has been described previously.)[24][25][26] ...
Objectives
Postoperative outcomes vary considerably across bariatric patients and may be related to psychosocial factors. In this study, we examined whether a patient’s family support predicts postsurgical weight loss and the remission of type 2 diabetes mellitus (T2DM).
Design
Retrospective cohort study in Singapore.
Setting
Participants were recruited from a public hospital in Singapore.
Participants
Between 2008 and 2018, 359 patients completed a presurgical questionnaire before undergoing gastric bypass or sleeve gastrectomy.
Outcome measures
As part of the questionnaire, patients described their family support in terms of structure (marital status, number of family members in the household) and function (marriage satisfaction, family emotional support, family practical support). Linear mixed-effects and Cox proportional-hazard models were used to examine whether these family support variables predicted percent total weight loss or T2DM remission up to 5 years postsurgery. T2DM remission was defined as glycated haemoglobin (HbA1c) <6.0% without medications.
Results
Participants had a mean preoperative body mass index of 42.6±7.7 kg/m ² and HbA1c (%) of 6.82±1.67. Marital satisfaction was found to be a significant predictor of postsurgical weight trajectories. Namely, patients who reported higher marital satisfaction were more likely to sustain weight loss than patients who reported lower marital satisfaction (β=0.92, SE=0.37, p=0.02). Family support did not significantly predict T2DM remission.
Conclusions
Given the link between marital support and long-term weight outcomes, providers could consider asking patients about their spousal relationships during presurgical counselling.
Trial registration number
NCT04303611 .
... Several studies have showed that bariatric weight loss results are normally distributed, implying grades of treatment response [4][5][6][7][8]. Weight regain should therefore be defined by a scale of grades, rather than with a single threshold. ...
... Once the side effects subside, or the diet is no longer maintained, the exceptional, above average weight loss will correct to the normal, average result. Weight loss overshoot can only be differentiated from average early results with the use of centile graphs of bariatric weight loss, published in recent years (similar to well-known growth charts for infants and children used in pediatrics) [4][5][6][7][8]13]. ...
... Thresholds for average and below average weight loss should therefore be established first, not arbitrarily (or by consensus), but based on evidence, and on different points in time. This has been done before, with the mentioned centile graphs of bariatric outcome, based on standard deviations (SD) in normally distributed weight loss results [5][6][7][8]13]. In general, normally distributed results between − 1SD and + 1SD are (by definition) average. ...
Introduction
There is a need for a standardized, evidence-based classification of post-bariatric weight-regain, to investigate and compare revision procedures and to advice and treat patients in an evidence-based way.
Methods
We used standard deviations (SD) of the highest (1–2 years) and latest (> 2 years) percentage total weight loss (%TWL) results after primary bariatric surgery from the Dutch Audit for Treatment of Obesity (DATO) bariatric registry as benchmarks for (above) average (≥ − 1SD), poor (− 1SD to − 2SD) and insufficient (< − 2SD) weight loss. Weight regain maintaining (above) average weight loss was called grade 1, weight regain towards poor weight loss grade 2, towards insufficient weight loss grade 3, with subgrades 2a/3a for below average weight loss from the start, and 2b/3b for weight regain from (above) average to below average weight loss. Patient characteristics and diabetes improvement/impairment were compared. Sensitivity and specificity of 14 existing weight regain criteria were calculated.
Results
We analyzed 93,465 results from 38,830 patients (77.1% gastric bypass, 22.5% sleeve gastrectomy). The − 1SD thresholds for early and late weight loss approximated 25%TWL and 20%TWL, the − 2SD threshold for late weight loss 10%TWL. Weight regain could be analyzed for 18,403 patients (2.5–5.2 years follow-up). They regained mean 6.7 kg (5.4%TWL), with 66.8% grade 1 weight regain, 7.2% grade 2a, 7.4% grade 2b, 2.1% grade 3a, and 0.6% grade 3b. There were significant differences in comorbidities, gender, age, weight regain, diabetes impairment, and diabetes improvement across grades. Weight regain criteria from literature were extremely divers. None had high sensitivity.
Conclusion
The DATO classification for post-bariatric weight regain combines the extent of weight regain with evidence-based endpoints of weight loss. It differentiated weight regain maintaining (above) average weight loss, two intermediate grades, gradual weight regain with below average weight loss from the start (primary non-response) and steep weight regain towards insufficient weight loss (secondary non-response). The classification is superior to existing criteria and well supported by evidence.
Graphical Abstract
... There are several alternatives for selecting the most appropriate metric when the researchers wanted to draw percentile charts for WL after BS. These choices vary from crude BMI [10] to more complicated ones like %EWL [11,12], excess BMI and total WL [13], and %AWL [14]. Among these studies, only van de Laar et al. [15] explore a wider range of metrics and conclude the %AWL is the best choice for drawing percentile charts among them. ...
... Another Spanish cohort study developed a percentiles chart without using any smoothing model or considering initial BMI, one of the most important factors in post-operative WL [6]. An Asian cohort study offered percentile charts to follow WL after BS using quantile regression models [13]. This could cause heterogeneity among participants or samples. ...
Introduction:
It could be valuable for surgeons and patients to use one chart in different groups and evaluate weight loss during the post-surgery period.
Methods:
This retrospective study used the Iran National Obesity Surgery Database. patients with clinically severe obesity aged 18-70 undergoing sleeve gastrectomy participated in this study. Body mass Index (BMI) reduction and 5 other metrics measured over the study period were modeled using LMS (Lambda-mu-sigma) method. Our data were split into the train (70%) and test (30%) sets.
Results:
In this study, 1,258 patients (75% female) met the eligibility criteria to participate. Mean age and initial BMI were 36.87 ± 10.51 and 42.74 (40.37-46.36), respectively. Percentile charts for various metrics have been presented for the first 2 years after surgery.
Conclusions:
For sleeve surgery, all metrics are acceptable for clinical applications. Using the statistical view, BMI reduction is the most acceptable metric according to the lowest bias values and its variation between all the metrics.
... Laparoscopic sleeve gastrectomy (LSG) is the most popular bariatric procedure worldwide [5][6][7]. Factors affecting weight loss after LSG include preoperative body mass index (BMI), diet, exercise, personality, social stress, and family support [8][9][10][11][12][13][14][15][16][17]. However, genetic background seems to be the most significant influencing factor, accounting for an average of 40-75% and as high as 90% of the causes of obesity [18][19][20][21][22][23]. ...
Background
Single-nucleotide polymorphisms (SNPs) associated with obesity predict laparoscopic Roux-en-Y gastric bypass (LRYGB) and biliopancreatic diversion with duodenal switch (BPD/DS) for weight loss with good efficiency. However, prediction of weight loss after laparoscopic sleeve gastrectomy using SNPs has not been well investigated.Objectives
To predict weight loss after laparoscopic sleeve gastrectomy using obesity-related SNPs and clinical variants in Chinese patients with body mass index (BMI) ≥ 32.5 kg/m2.Methods
We detected 29 SNPs. Binary logistic regression was used to screen SNPs and clinical variables with predictive value. Receiver operating characteristic (ROC) curves were plotted for clinical variables, SNPs, and their combination, and areas under the ROC curve (AUC) were compared. Internal and external validation tests were performed.Resultsrs12535708, rs651821, and rs5082 were constructed as the genetic risk score (GRS). Preoperative BMI was constructed as the clinical risk score (CRS). Preoperative BMI and SNPs were constructed as the cumulative genetic risk score (CGRS). ROC curves of GRS, CRS, and CGRS showed that the optimal cutoffs were 0.831 (AUC = 0.840; sensitivity, 92.96%; specificity, 64.29%), 43.46 kg/m2 (AUC = 0.830; sensitivity, 76.06%; specificity, 85.71%), and 0.921 (AUC = 0.931; sensitivity, 77.46%; specificity, 92.86%), respectively. The AUC of CGRS was significantly greater than that of CRS (P < 0.05) and greater than GRS without statistical significance.Conclusion
In Chinese patients with BMI ≥ 32.5 kg/m2, GRS and CRS could predict weight loss success. However, CGRS was superior to GRS or CRS alone.Graphical abstract
... We included only conversion surgery that converts an index procedure to a different type of procedure for IWL and WR. We excluded subjects who were lost to follow-up or those with incomplete data for at least the first 18 months from the primary bariatric surgery as our previously published data showed that maximum weight loss was generally achieved between the first 9 to 18 months after primary bariatric surgery [7]. The SOS study similarly observed maximal weight loss after 1 to 2 years after primary bariatric surgery [2]. ...
PurposeBariatric surgery is the most effective and durable treatment option for clinically severe obesity. Unfortunately, some degree of weight regain (WR) is common after nadir weight is achieved. Pharmacotherapy and revision surgery are potential options to treat this phenomenon. We aim to determine the efficacy of both approaches in patients with WR versus insufficient weight loss (IWL).Materials and Methods
We retrospectively reviewed a prospectively collected database of patients who underwent bariatric surgery from 2008 to 2018 with IWL or WR.ResultsOf 422 patients with WR or IWL after bariatric surgery, 150 patients were placed on pharmacotherapy and 27 underwent revisional surgeries. Mean age of patients was 41.4 years and mean BMI was 42.1 kg/m2. The most common conversion surgery was LSG to RYGB. % Total weight loss (TWL) was higher in IWL group (23.8% ± 11.0) compared to WR group (17.2% ± 7.9) in revisional surgery (p = 0.02). The converse was observed for pharmacotherapy, with %TWL 1.9% in the WR group compared to 0.7% in the IWL group (p = 0.0067).Conclusion
Patients with IWL or WR had modest weight loss with adjunctive use of pharmacotherapy after primary bariatric surgery. Conversely, revisional surgery is an effective treatment for both IWL and WR.Graphical abstract
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 $\ge$18 years) from ten prospective cohorts (including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese Subjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse Obesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and SM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year followup after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band. Patients with a previous history of bariatric surgery or large delays between scheduled and actual visits were excluded. The training cohort comprised patients from two centres in France (ABOS and BAREVAL). The primary outcome was BMI at 5 years. A model was developed using least absolute shrinkage and selection operator to select variables and the classification and regression trees algorithm to build interpretable regression trees. The performances of the model were assessed through the median absolute deviation (MAD) and root mean squared error (RMSE) of BMI. Findings10 231 patients from 12 centres in ten countries were included in the analysis, corresponding to 30 602 patient-years. Among participants in all 12 cohorts, 7701 (75$\bullet$3%) were female, 2530 (24$\bullet$7%) were male. Among 434 baseline attributes available in the training cohort, seven variables were selected: height, weight, intervention type, age, diabetes status, diabetes duration, and smoking status. At 5 years, across external testing cohorts the overall mean MAD BMI was 2$\bullet$8 kg/m${}^2$ (95% CI 2$\bullet$6-3$\bullet$0) and mean RMSE BMI was 4$\bullet$7 kg/m${}^2$ (4$\bullet$4-5$\bullet$0), and the mean difference between predicted and observed BMI was-0$\bullet$3 kg/m${}^2$ (SD 4$\bullet$7). This model is incorporated in an easy to use and interpretable web-based prediction tool to help inform clinical decision before surgery. InterpretationWe developed a machine learning-based model, which is internationally validated, for predicting individual 5-year weight loss trajectories after three common bariatric interventions.
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).
Bariatric surgery is the most effective treatment for severe obesity; however, on an individual level, weight loss is highly variable. Improvements in weight-related comorbidities following bariatric surgery are related to the amount of weight loss achieved. Therefore, it is important to maximize weight loss. Cases of insufficient weight loss after bariatric surgery include those who have never achieved a good weight loss response from surgery (primary suboptimal responder) and those who had a good initial weight loss and then experienced weight regain (secondary suboptimal responder). The etiology and biology and, therefore, treatment for these patients are different and remain one of the most challenging problems facing bariatric surgery as they are associated with failure of comorbidities resolution or recurrence of their weight-related comorbidities. Attempts have been made to determine preoperative characteristics of patients that predict postoperative weight trajectories and also the underlying driving biology. This chapter aims to summarize the current literature surrounding suboptimal weight loss and weight regain and current treatment options.
