Bariatric Times

DEC 2017

A peer-reviewed, evidence-based journal that promotes clinical development and metabolic insights in total bariatric patient care for the healthcare professional

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14 Original Research Bariatric Times • December 2017 INTRODUCTION Long-term, sustained, high-quality outcomes resulting from bariatric surgery are well documented. 1–5 On average, patients undergoing bariatric surgery observe substantial weight loss and remission of or improvements in obesity-related comorbidities, such as type 2 diabetes mellitus (T2DM), dyslipidemia, hyperlipidemia, and hypertension. 2, 6–10 Despite good results in the mean, there is still high variability in outcomes. 2 Specifically, the Longitudinal Assessment of Bariatric Surgery (LABS) study 11 found that one quarter of the Roux-en-Y gastric bypass (RYGB) patients lost less than 25 percent of their body weight and one quarter lost more than 38 percent of their body weight. Similar variability was reported for laparoscopic adjustable gastric band (LAGB) patients. In fact, this variability is well documented by the surgical literature, which quantifies intervention "failures" in terms of percentage of study participants who did not observe adequate weight loss or remission of pre-existing morbidities. 12–14 In addition to variability of long-term outcomes, different surgeries own different levels of risk 15–21 and yield substantial differences in mean percent weight loss. 15,22–25 With the great wealth of existing available bariatric surgery data, it is attractive to personalize predictions that identify which surgery is optimal for a patient based on balancing risks against long-term success. There have been a number of attempts to develop predictive models by establishing pre-operative predictors of long-term success within each surgical procedure. 24,26–31 Known variables include pre-operative age, body mass index (BMI), sex, waist circumference, hemoglobin A1c (HbA1c) levels, and psychosocial characteristics. Unfortunately, to date, standard regression has not yielded strong pre-operative predictions. Of particular note was that the LABS study, which included over 100 pre- operative carefully measured variables, did not find strong pre-operative predictors of three-year weight loss (R 2 =0.14). However, the inclusion of genetic factors to predictively model long-term percent weight loss was promising. 32 The inclusion in a regression model of a single nucleotide polymorphisms (SNP) associated to p ercent weight loss after RYGB surgery to clinical factors (age, sex, pre- operative BMI, and T2DM status) yielded an area under the curve (AUC) of 0.633. The regression model d eveloped with clinical factors alone generated an AUC of 0.620. Recently, neural network algorithms have been successfully applied to genetic information obtained from bariatric surgery to rank the importance of clinical and genomic factors involved in diabetes remission after bariatric surgery. 3 3 With only pre- operative variables combined with HbA1c and insulin levels, the investigators achieved an AUC of 0.81. Adding genomic information improved the AUC 0.92. In real-world clinical settings, the simplest pre-operative measures remain baseline anthropometrics, demographic information, and comorbidity status. Here, we rely on clinical data to develop and test a neural network algorithm to answer the following questions: 1. How well can we pre-operatively predict long-term weight loss success from routinely collected clinical measures? 2. How much do the predictions improve if we include short-term weight loss information (<1 month)? 3. What are the most important variables that contribute to long- term outcomes? Neural networks are machine learning self-adaptive models that can adjust themselves to the high volumes of input data without specification of functional or distributional form providing a particularly well-suited methodology for integrating and combining data obtained from different surgeries and surgeons. In addition, a sensitivity analysis arising from network development computes importance of each predictor or rank ordering. 33 METHODS Patients. Patients (N=478) underwent eight different surgeries (including revisional surgeries) at a s ingle private practice institution between January 2010 and April 2014. Demographic data and comorbid conditions, which included T2DM, hypertension (HTN), sleep apnea (SA), a nd gastroesophageal reflux disease (GERD), were collected in six of the eight surgeries. Comorbidities were only recorded if the patient was taking medication or was formally diagnosed with sleep apnea. Data was gathered retrospectively on a prospectively kept database. The retrospective data collection was reviewed and approved by the commercial Institutional Review Board, Quorum Review IRB. From this master database, we retained data, which included a measured body weight obtained at the first long-term clinical visit past one year. Hereafter, we define any long- term measurement as those obtained at the first clinical visit after one year post surgery. Subject characteristics of this reference database appear in Table 1. Description of surgeries performed. Subject numbers in the eight surgeries performed appear in Table 1. Loop duodenal switch, or laparoscopic single anastomosis duodenal switch, uses the laparoscopic vertical sleeve gastrectomy as the first step of the surgery but does not divide the distal intestine, instead bringing up a loop of intestine to connect it below the pylorus. Regular duodenal switch, or laparoscopic Roux-en-Y duodenal switch, uses the laparoscopic vertical sleeve gastrectomy as the first step of the surgery and then divides the intestine and reconnects it below the pylorus. Sleeve or laparoscopic vertical sleeve gastrectomy provides volume reduction of the stomach by removing a great portion of the stomach using staples. Gastric bypass, or laparoscopic RYGB, partitions the stomach into two halves. Then the intestines are reconnected to the upper half while bypassing the lower half. In order to do this, the intestines are also divided, by DIANA M. THOMAS, PhD; PATRICK KUIPER, MS; HINALI ZAVERI, MD; AMIT SURVE, MD; and DANIEL R. COTTAM, MD Bariatric Times. 2017;14(12):14–17. Neural Networks to Predict Long-term Bariatric Surgery Outcomes ABSTRACT The objective was to predict longer term weight loss success from pre-operative and short-term surgery data using machine learning. Eight neural networks that predict long-term weight status one year after surgery were trained and tested. Four neural networks classified weight loss success and continuous outcomes from solely pre- o perative routinely collected clinical variables. The remaining neural networks predicted long-term outcomes from a combination of pre-operative variables and weight-related short-term data obtained in less than one month post-surgery. Patients (N=478) underwent eight different surgeries (including revisional surgeries) at a single private practice institution between January 2010 and April 2014. Demographic data and comorbid conditions data were gathered retrospectively on a prospectively kept database. Neural networks that classified weight loss success yielded an area under the curve of 0.77 to 0.78 pre-operatively, which improved to 0.82 with inclusion of short- term data. The continuous long-term weight was also well predicted with R 2 values in three of the four networks ranging from 0.68 to 0.72. These results improve on previously obtained predictions that relied on linear regression. Machine learning algorithms like neural networks might provide a feasible and scalable method to amplify long-term predictive accuracy from pre-operative and short-term observations to inform and guide patient-surgeon decisions. KEYWORDS Artificial neural networks, pre-operative predictors, post-operative predictors, mathematical modeling

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