Weight Loss Trajectories After Bariatric Surgery for Obesity: Mathematical Model and Proof-of-Concept Study

Abstract

Background Obesity surgery has proven its effectiveness in weight loss. However, after a loss phase of about 12 to 18 months, between 20% and 40% of patients regain weight. Prediction of weight evolution is therefore useful for early detection of weight regain. Objective This proof-of-concept study aimed to analyze the postoperative weight trajectories and to identify “curve families” for early prediction of weight regain. Methods This was a monocentric retrospective study with calculation of the weight trajectory of patients having undergone gastric bypass surgery. Data on 795 patients after a 2-year follow-up allowed modeling of weight trajectories according to a hierarchical cluster analysis (HCA) tending to minimize the intragroup distance according to Ward. Clinical judgement was used to finalize the identification of clinically relevant representative trajectories. This modeling was validated on a group of 381 patients for whom the observed weight at 18 months was compared to the predicted weight. Results Two successive HCA produced 14 representative trajectories, distributed among 4 clinically relevant families: Of the 14 weight trajectories, 6 decreased systematically over time or decreased and then stagnated; 4 decreased, increased, and then decreased again; 2 decreased and then increased; and 2 stagnated at first and then began to decrease. A comparison of observed weight and that estimated by modeling made it possible to correctly classify 98% of persons with excess weight loss (EWL) >50% and more than 58% of persons with EWL between 25% and 50%. In the category of persons with EWL >50%, weight data over the first 6 months were adequate to correctly predict the observed result. Conclusions This modeling allowed correct classification of persons with EWL >50% and could identify early after surgery the patients with potentially less that optimal weight loss. Further studies are needed to validate this model in other populations, with other types of surgery, and with other medical-surgical teams.

Current Applications of Artificial Intelligence in Bariatric Surgery

“…In the recent paper by Dimeglio et al, a hierarchical cluster analysis was used to identify four profiles of weight trajectories associated with clinical expertise. Interestingly, the authors reported that patients who were the most successful were those who lost weight regularly, and patients who lost the least had difficulties in the initial phase or had a secondary weight regain [52].…”

Primer reporte ecuatoriano de cirugía bariátrica en ancianos

“…Los resultados sugieren que la cirugía bariátrica es un tratamiento eficaz para la obesidad y sus comorbilidades en este grupo etario, con una baja tasa de complicaciones, demostrando resultados similares a los estudios realizados en otros países 33 . Los pacientes adultos mayores que se sometieron a bypass gástrico o gastrecto-mía en manga mostraron una reducción de peso estadísticamente significativa, obteniendo valores de %TWL y %EWL concordantes con aquellos descritos en otros estudios: ≥ 20% para el primero 19 , y ≥ 50% para el segundo, de acuerdo con los criterios de Reinhold 21,23 , tanto a uno como a dos años de seguimiento. Las complicaciones descritas en este análisis son en su mayoría leves, de manera similar a aquellas reportadas en estudios de adultos jóvenes 14,34 , demostrando la seguridad de la cirugía bariátrica en adultos mayores, así como lo han determinado otros artículos publicados 35,36 ; apoyando así la afirmación de que una edad avanzada por sí sola no debe considerarse una contraindicación para el tratamiento quirúrgico de la obesidad.…”

The development of machine learning in bariatric surgery

“…Johnston's Patient Prediction Model was able to allocate patients a probability of cessation of antihyperglycemic medications after bariatric surgery based on the preoperative factors above with high internal and external validity (10). To date, it has been difficult to use standard regression to predict long-term success in bariatric surgery patients (24). However, Weerakoon's model was able to predict the final weight of patients with 85% accuracy and monthly weight changes with 75% accuracy (23).…”

“…Additionally, monthly weight prediction can allow for more consistent follow-up with patients, which is one of the main predictors of persistent weight loss (23, 27). DiMeglio's model was able to predict weight loss trajectories in a subset of patients with very high accuracy (24). If used during follow-up visits, this model can allow for the early identification of suboptimal weight trajectory and can allow for early and improved second-line physical, psychological, and nutritional management (24).…”