STRATEGIES TO PREEMPTIVELY GENOTYPE PATIENTS FOR INDIVIDUALIZED MEDICINE

Purpose:  To individualize the application of genetic testing by developing and validating risk prediction models that identify patients most likely to benefit from genotype-tailored therapies.

 

Methods:   We developed a model to select patients for genotyping based on predicted use of genetic variants in future prescribing episodes and examined its performance in an independent validation set.  We retrieved electronic medical records (EMR) from a retrospective cohort of patients at Vanderbilt University Medical Center who met ‘medical home' criteria between 6/30/2005-5/31/2010. Patients were included if they were ≥ 18 years of age and had completed three clinical visits in a two year period with a primary care provider or specialist. The primary outcome was the two-year risk of an incident prescription of clopidogrel, a statin, or warfarin, all of which can be tailored to pharmacogenomic variants. 

   Using demographics and clinical data, we developed a Cox regression model to estimate risk for being prescribed a target medication within two years of the medical home date.  A modified version of the model was implemented within an EMR-based clinical decision support tool, and risk was calculated prior to every planned patient encounter.  A high-risk, subset of patients (>28.5% risk in two years) was identified and sampled for preemptive genotyping between 6/1/2010 and 3/31/2013.  We summarize the extent to which the genotyped patients in this validation set were enriched with those eventually prescribed a target medication compared to other, simpler sampling approaches (random sampling; high age and body mass index (BMI) based sampling).

 

Results: The model exhibited little to no overfitting for discriminating between patients prescribed a target medication and others in the original training dataset.  Applying encounter inclusion criteria, 18,950 patients qualified for the validation set in clinics where the decision support tool was deployed.  Of the 1673 patients who were selected for preemptive genotyping by the model, we calculated that 48% (95% confidence interval: 44%-52%) were prescribed a target medication, while two identically sized cohorts defined by random sampling or age and BMI based sampling yielded target prescription rates of 19% (95% CI: 16%-22%) and 34% (95% CI: 30%-37%) respectively.

 

Conclusions: Model-driven patient selection within an EMR increases the efficiency of large-scale genotyping and use of genetic information to direct prescribing.