Table 3.
The overview of parameters used in each prediction model and characteristics of training cohorts
| Model | Year | Number of patients | Procedure | Follow-up (in years) | Equation |
|---|---|---|---|---|---|
| Baltasar_1 | 2009 | 135 | Duodenal switch | 3 | EBMI=0.33*IBMI+14 |
| Baltasar_2 | 2011 | 7410 | Any bariatric procedure | 3 | PBMI=IBMI*0.4+11.75 |
| Baltasar_3 | 2011 | 2083 | RYGB | 3 | PBMI=IBMI*0.4+10.23 |
| Baltasar_4 | 2011 | 128 | SG | 3 | PBMI=IBMI*0.4+10.88 |
| Wood | 2014 | 2608 | RYGB | 0.5-2 | 50th%tileBMI=36.71+0.7308*(IBMI—50)+0.02551*(age— 50)−0.906*(time—6)+0.04298*(time—6)2−0.00052*(time—6)3−0.00527*(IBMI—50)*(time—6)+0.001542*(age—50)*(time—6) |
| Wise | 2016 | 647 | RYGB | 1 | EBMIL=6.4*female gender-6.7*black race-1.2*BMIo-3.7*HTN-6*DM |
| Goulard | 2016 | 197 | SG | 1 | BMI=-3.597+0.621*BMI+0.135*age |
| Seyssel | 2018 | 444 | RYGB | 1 | WL=0.4*preoperative weight-0.21*age |
| Cottam_1 | 2018 | 371 | SG | 1 | %EWL=140.9-0.731*DM-1.53*HTN-0.304*age-1.22*BMI-12.5*HTN*DM |
| Cottam_2 | 2018 | 371 | SG | 1 | BMI reduction=0.73-0.0581*age+0.343*BMI-2.31*HTN*DM |
| Janik | 2019 | 211 | SG | 1 | InBMI=2.111+0.005*age+0.023*preoperative BMI+0.116*female gender |
| Velázquez-Fernández | 2019 | 1002 | RYGB | 1 | WL=-23.058+0.396*initial weight+0.035*days to visit+3.175*no sleep apnea |
Abbreviations: EBMI expected body mass index, IBMI initial body mass index, PBMI predicted body mass index, RYGB Roux-en-Y gastric bypass, SG sleeve gastrectomy, BMI body mass index, EBMIL excess body mass index loss, BMIo, initial body mass index, HTN hypertension, DM diabetes mellitus, WL weight loss