Key result
Adapted machine learning model predicts postoperative BMI in adolescents and young adults within 3.7 kg/m².
Why the study?
Metabolic-bariatric surgery treats severe obesity in adolescents, but predicting postoperative weight loss is challenging. Researchers aimed to adapt and test an adult machine learning 5-year weight loss trajectory calculator in adolescents.
Can a machine learning calculator accurately predict 5-year weight trajectories after bariatric surgery in adolescents and young adults?
Cohort (n=2,255)
Yes
Can a machine learning calculator accurately predict 5-year weight trajectories after bariatric surgery in adolescents and young adults?
A machine learning calculator adapted from adults can predict 5-year postoperative weight loss trajectories in adolescents undergoing bariatric surgery with acceptable accuracy, though accuracy decreases over time.
Answers are grounded in this paper and cite their sources.
May support preoperative counseling in adolescents; leaves open prospective validation before clinical adoption.
Background Metabolic-bariatric surgery is an efficient therapy in selected adolescents with severe obesity. However, predicting the postoperative weight loss is challenging. A machine learning calculator predicting 5-year weight loss trajectory has been developed in adults, based on seven preoperative features. The aim of the present study was to adapt and test it in adolescents. Methods Retrospective cohort study in patients aged 12–20 years undergoing Roux-en-Y gastric bypass (RYGB), sleeve gastrectomy (SG), or adjustable gastric band (AGB) in France and Sweden between 2001 and 2022. Primary outcome was the accuracy of 5-year BMI prediction, expressed as the median absolute deviation (MAD) between predicted and observed BMI. The model developed in adults, was trained in a subset of 80% of randomly selected adolescents, and secondly tested in the remaining 20%. Results We enrolled a total of 2255 patients (1705 female [75.6%], 12–20 years [median 19]). Five-year follow-up data were available for 59% of French and 38% of Swedish patients. The median (IQR) 5-year total weight loss was 30.2% (23.9–38.6) for RYGB, 23.4% (13.7–32.8) for SG, and 13.4% (0.0–30.1) for AGB. The adapted model predicted the observed 5-year BMI with a MAD of 3.7 kg/m² (95% CI [3.3–3.9]). The accuracy of the model was maximal for bypass (3.2 kg/m² [3.0–3.7]), good for SG (3.9 kg/m² [3.1–5.0]), and lower for AGB (7.3 kg/m² [5.5–8.4]), and accuracy decreased with time and in adolescents under 19 years. Age, height, weight, and type of intervention influenced 5-year weight loss. Type 2 diabetes influenced weight loss until 2 years after surgery, but not later. Conclusion The model had an acceptable accuracy for adolescents to predict 5-year postoperative weight loss trajectory. Accuracy decreased over time and was influenced by type of intervention and age. This calculator is available online: https://bariatric-weight-trajectory-prediction.univ-lille.fr/ .
No takes yet. Share an insight, caveat, or question.
Gueorguieva et al. (2026) conducted a cohort in Severe obesity (n=2,255). Machine learning prediction model vs. Observed BMI was evaluated on Accuracy of 5-year BMI prediction (median absolute deviation between predicted and observed BMI) (MAD 3.7 kg/m², 95% CI 3.3-3.9). The adapted machine learning model predicted 5-year postoperative BMI in adolescents and young adults with a median absolute deviation of 3.7 kg/m² compared to observed values.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: