Machine Learning Model in Obesity to Predict Weight Loss One Year after Bariatric Surgery: A Pilot Study.

Autores de INCLIVA

• Esther Benito Casado

  Autor

• Ana Palanca Palanca

  Autor

• 

  Sergio Martinez Hervas

  Autor

• Miguel Civera Andres

  Autor

• Blanca Alabadí Pardiñes

  Autor

• 

  Laura Piqueras Ruiz

  Autor

• 

  Jose Tomas Real Collado

  Autor

Participantes ajenos a INCLIVA

• Nadal, E
• Ródenas-Navarro, AM
• Ortega, J
• Ródenas, JJ

Grupos y Plataformas de I+D+i

• Grupo de Investigación en Receptores Nucleares en Patología Cardiometabólicas

  

  Established Researcher

  Laura Piqueras Ruiz

• Grupo de Investigación sobre Riesgo Cardiometabólico y Diabetes

  

  Leading Researcher

  Sergio Martinez Hervas

• Unidad de Composición Corporal

  Responsable de Plataforma

  Miguel Civera Andres

Abstract

Roux-en-Y gastric bypass (RYGB) is a treatment for severe obesity. However, many patients have insufficient total weight loss (TWL) after RYGB. Although multiple factors have been involved, their influence is incompletely known. The aim of this exploratory study was to evaluate the feasibility and reliability of the use of machine learning (ML) techniques to estimate the success in weight loss after RYGP, based on clinical, anthropometric and biochemical data, in order to identify morbidly obese patients with poor weight responses. We retrospectively analyzed 118 patients, who underwent RYGB at the Hospital Clínico Universitario of Valencia (Spain) between 2013 and 2017. We applied a ML approach using local linear embedding (LLE) as a tool for the evaluation and classification of the main parameters in conjunction with evolutionary algorithms for the optimization and adjustment of the parameter model. The variables associated with one-year postoperative %TWL were obstructive sleep apnea, osteoarthritis, insulin treatment, preoperative weight, insulin resistance index, apolipoprotein A, uric acid, complement component 3, and vitamin B12. The model correctly classified 71.4% of subjects with TWL < 30% although 36.4% with TWL = 30% were incorrectly classified as "unsuccessful procedures". The ML-model processed moderate discriminatory precision in the validation set. Thus, in severe obesity, ML-models can be useful to assist in the selection of patients before bariatric surgery.

Keywords

• RYGB; bariatric surgery; locally linear embedding; machine learning; obesity; predictive model; total weight loss