Liqun Yu

Objective: Weight regain (WR) after Roux‐en‐Y gastric bypass surgery (RYGB) starts to occur 2 years after surgery, ultimately affecting at least 25% of patients. A limited number of studies have evaluated the impact of antiobesity medications (AOMs) on this phenomenon.Methods: This study reviewed the electronic medical records of 1,196 patients who underwent RYGB between 2004 and 2015. WR was evaluated by comparing each patient’s weight during subsequent postoperative office visits to nadir weight (lowest weight after RYGB, n = 760), taking into consideration the interval during which WR occurred. Patients who were prescribed AOMs and came to follow‐up visits were classified as adherent users, whereas those who missed their follow‐up visits were considered nonadherent. This study used a linear mixed model, Cox regression, and generalized equation estimator to determine the impact of AOMs on WR trajectory, hazard ratio for time to event, and odds ratio for repeated event occurrence, respectively.Results: Despite the lack of a unified protocol for using AOMs, the three statistical models converged to show that phentermine and topiramate, used individually or in combination, can significantly reduce WR after RYGB.Conclusions: Phentermine and topiramate are effective in mitigating WR after RYGB. Further studies are needed to help ascertain optimal use of AOMs after bariatric surgery.

Objective: Weight recidivism following Roux‐en‐Y gastric bypass (RYGB) is common and is associated with recurrence of comorbidities. Studies with long‐term follow‐up of recidivism quantified by weight regain (WR) are lacking. A retrospective review of all RYGB at our center from 2004 to 2015 was performed to examine the effects of race and type 2 diabetes on WR following RYGB.Methods: Multivariable linear mixed models were used for the effects of time and race on weight, WR relative to nadir weight (WR/nadir), and WR relative to maximal weight loss, and Cox regressions were used for low, moderate, and high WR/nadir.Results: A total of 1,395 participants were identified. The sample was limited to African American (AA), Caucasian American (CA), and Hispanic American (HA) participants. The effects of time (P < 0.0001), race (P < 0.0001), and race × time interaction (P = 0.0008) on weight trajectory were significant. AA had significantly more WR than CA (P < 0.01). AA and HA had a higher hazard ratio for having low, moderate, and rapid WR/nadir.Conclusions: Racial disparities after RYGB include WR and particularly affect AA. Understanding the etiologic factors that contribute to these disparities is important to optimize the long‐term clinical outcomes of bariatric surgery.

Summary: Traditional linear programming algorithms for quantile regression, for example, the simplex method and the interior point method, work well for data of small to moderate sizes. However, these methods are difficult to generalize to high‐dimensional big data for which penalization is usually necessary. Further, the massive size of contemporary big data calls for the development of large‐scale algorithms on distributed computing platforms. The traditional linear programming algorithms are intrinsically sequential and not suitable for such frameworks. In this paper, we discuss how to use the popular ADMM algorithm to solve large‐scale penalized quantile regression problems. The ADMM algorithm can be easily parallelized and implemented in modern distributed frameworks. Simulation results demonstrate that the ADMM is as accurate as traditional LP algorithms while faster even in the nonparallel case.