BACKGROUND : Although bariatric surgery is safe, some patients fear serious complications. OBJECTIVE : This retrospective study used the 2015 Metabolic and Bariatric Surgery Accreditation Quality Improvement Project (MBSAQIP) database to evaluate patient outcomes for gastric bypass (GB) and sleeve gastrectomy and to develop a risk prediction model for serious adverse events (SAEs) and readmission rates 30 days after surgery. METHODS : MBSAQIP national patient database. METHODS : We created separate exploratory multivariable logistic regression models for SAEs and readmissions. We then externally validated both models using the 2016 MBSAQIP Participant Use Data File. RESULTS : Significant predictors of SAEs were preoperative body mass index (adjusted odds ratio [AOR] 1.07, P < .0001); GB surgery (AOR 2.08, P < .0001); cardiovascular disease (AOR 1.43, P < .0001); smoking (AOR 1.12, P = .04); diabetes (AOR 1.15, P = .0001); hypertension (AOR 1.17, P < .0001); limited ambulation (AOR 1.48, P < .0001); sleep apnea (AOR 1.12, P = .001); history of pulmonary embolism (AOR 2.81, P < .0001); and steroid use (AOR 1.40, P = .001). Significant predictors of readmissions were GB surgery (AOR 1.81, P < .0001); female sex (AOR 1.26, P < .0001); diabetes (AOR 1.08, P = .04); hypertension (AOR 1.11, P = .004); preoperative body mass index (AOR 1.05, P < .0001); sleep apnea (AOR 1.11, P = .002); history of pulmonary embolism (AOR 2.35, P < .0001); cardiovascular disease (AOR 1.61, P < .0001); smoking (AOR 1.14, P = .01); and limited ambulation (AOR 1.55, P < .0001). External validation supported these covariates, with similar model discriminative power. CONCLUSIONS : Our exploratory regression models may be used by clinicians to counsel patients about surgical risks, although future external validation should occur in non-North American populations.