Development and validation of an interpretable machine learning-based
  calculator for predicting 5-year weight trajectories after bariatric surgery:
  a multinational retrospective cohort SOPHIA study
Patrick Saux Pierre Bauvin Violeta Raverdy Julien Teigny Hélène Verkindt Tomy Soumphonphakdy Maxence Debert Anne Jacobs Daan Jacobs Valerie Monpellier Phong Ching Lee Chin Hong Lim Johanna C Andersson-Assarsson Lena Carlsson Per-Arne Svensson Florence Galtier Guelareh Dezfoulian Mihaela Moldovanu Severine Andrieux Julien Couster Marie Lepage Erminia Lembo Ornella Verrastro Maud Robert Paulina Salminen Geltrude Mingrone Ralph Peterli Ricardo V Cohen Carlos Zerrweck David Nocca Carel W Le Roux Robert Caiazzo Philippe Preux François Pattou
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.