Even modest prediction accuracy of genomic models can have large clinical utility.

Whole Genome Prediction (WGP) jointly fits thousands of SNPs into a regression model to yield estimates for the contribution of markers to the overall variance of a particular trait, and for their associations with that trait. To date, WGP has offered only modest prediction accuracy, but in some cases even modest prediction accuracy may be useful. We provide an illustration of this using a theoretical simulation that used WGP to predict weight loss after bariatric surgery with moderate accuracy (R (2) = 0.07) to assess the clinical utility of WGP despite these limitations. Prevention of Type 2 Diabetes (T2DM) post-surgery was considered the major outcome. Treating only patients above predefined threshold of predicted weight loss in our simulation, in the realistic context of finite resources for the surgery, significantly reduced lifetime risk of T2DM in the treatable population by selecting those most likely to succeed. Thus, our example illustrates how WGP may be clinically useful in some situations, and even with moderate accuracy, may provide a clear path for turning personalized medicine from theory to reality.

A patient-centered electronic tool for weight loss outcomes after Roux-en-Y gastric bypass.

BACKGROUND. Current patient education and informed consent regarding weight loss expectations for bariatric surgery candidates are largely based on averages from large patient cohorts. The variation in weight loss outcomes illustrates the need for establishing more realistic weight loss goals for individual patients. This study was designed to develop a simple web-based tool which provides patient-specific weight loss expectations. METHODS. Postoperative weight measurements after Roux-en-Y gastric bypass (RYGB) were collected and analyzed with patient characteristics known to influence weight loss outcomes. Quantile regression was used to create expected weight loss curves (25th, 50th, and 75th %tile) for the 24 months after RYGB. The resulting equations were validated and used to develop web-based tool for predicting weight loss outcomes. RESULTS. Weight loss data from 2986 patients (2608 in the primary cohort and 378 in the validation cohort) were included. Preoperative body mass index (BMI) and age were found to have a high correlation with weight loss accomplishment (P < 0.0001 for each). An electronic tool was created that provides easy access to patient-specific, 24-month weight loss trajectories based on initial BMI and age. CONCLUSIONS. This validated, patient-centered electronic tool will assist patients and providers in patient teaching, informed consent, and postoperative weight loss management.