Despite its overall excellent outcomes, weight loss after Roux-en-Y gastric bypass (RYGB) is highly variable. We conducted this study to identify clinical predictors of weight loss after RYGB. We reviewed charts from 300 consecutive patients who underwent RYGB from August 1999 to November 2002. Data collected included patient demographics, medical comorbidities, and diet history. Of the 20 variables selected for univariate analysis, 9 with univariate P values ≤ 0.15 were entered into a multivariable regression analysis. Using backward selection, covariates with P < 0.05 were retained. Potential confounders were added back into the model and assessed for effect on all model variables. Complete records were available for 246 of the 300 patients (82%). The patient characteristics were 75% female, 93% white, mean age of 45 years, and mean initial BMI of 52.3 kg/m2. One year after surgery, patients lost an average of 64.8% of their excess weight (s.d. = 20.5%). The multivariable regression analysis revealed that limited physical activity, higher initial BMI, lower educational level, diabetes, and decreased attendance at postoperative appointments had an adverse effect on weight loss after RYGB. A model including these five factors accounts for 41% of the observed variability in weight loss (adjusted r2 = 0.41). In this cohort, higher initial BMI and limited physical activity were the strongest predictors of decreased excess weight loss following RYGB. Limited physical activity may be particularly important because it represents an opportunity for potentially meaningful pre- and postsurgical intervention to maximize weight loss following RYGB.

BACKGROUND Belantamab mafodotin (belamaf) has demonstrated clinically meaningful antimyeloma activity in patients with heavily pretreated multiple myeloma. However, the drug is highly active against dividing cells, which contributes to off-target adverse events, particularly ocular toxicity. Investigators or providers routinely use changes in best corrected visual acuity (BCVA) and corneal examination findings to determine Keratopathy Visual Acuity (KVA) grade to inform belamaf dose modification. We sought to model and translate the complexity of the ophthalmologic examination using the KVA scale into a mobile application that is easily applicable to patients presenting with ocular toxicities. OBJECTIVE We aimed to develop a semiautomated mobile app to facilitate the grading of ocular events in clinical trials involving belamaf. METHODS The paper process was semiautomated by creating a library of finite-state automaton (FSA) models to represent all permutations of KVA grade changes between baseline and current BCVA readings. The transition states in the FSA models operated independently of eye measurement units (eg, Snellen, logMAR, decimal) and provided a uniform approach to determining KVA grade changes. Together with the FSA, the complex decision tree for determining the grade change based on corneal examination findings was converted into logical statements that provide accurate and efficient computation of the overall KVA grade. First, a web-based user interface, conforming to clinical practice settings, was developed to simplify the input of key criteria for overall KVA grading. Subsequently, a mobile app was developed that included additional guided steps to assist in clinical decision-making. RESULTS The app underwent a robust Good Clinical Practice validation process in alignment with GSK standard operating procedures, and the outcomes were reviewed by key stakeholders, our medical lead for belamaf, and the systems integration team. The time to compute a patient’s overall KVA grade using the Belamaf Eye Exam (BEE) app was reduced from a 20- to 30-minute process to less than 1 to 2 minutes using our semiautomated KVA computational framework. The BEE app was well received, with most investigators surveyed selecting “satisfied” or “highly satisfied” for its accuracy and time efficiency. Based on current US Food and Drug Administration and EU standards, the BEE app is considered a nondevice clinical decision support software. CONCLUSIONS We conclude that our semiautomated approach provides for an accurate, simplified method of assessment of patients’ corneal status that reduces the number of potential errors and more quickly delivers information critical for potential belamaf dose modifications related to ocular changes. The app is currently available on the Apple iOS and Android platforms for use by investigators of the DREAMM clinical trials. Moreover, this mobile app could extend to the clinic as a tool to support healthcare providers in making informed belamaf treatment decisions for their patients.