Retrospective evaluation of a machine learning model to facilitate pharmacogenetic testing.

OBJECTIVES: To describe the epidemiology of nine medications with pharmacogenetic guidance (targeted medications) and frequency of pharmacogenetic testing; and to develop a retrospective machine learning (ML) model to predict prescription of targeted medications within 3 and 6 months of admission. METHODS AND ANALYSIS: For the epidemiology aim, the cohort included patients prescribed at least one targeted medication. Pharmacogenetic testing rates were determined before and after first targeted medication prescription. For the ML aim, the cohort included all inpatient admissions. Outcome was receipt of a targeted medication within 3 or 6 months. Models were trained using L2-regularised logistic regression and two gradient boosting machine frameworks (LightGBM and XGBoost). Data were temporally split into training (80%), validation (10%) and test (10%) sets. RESULTS: For the epidemiology cohort, 4520 patients were prescribed at least one targeted medication. Only 4.3% (n=194) had pharmacogenetic testing performed at any point, and only 1.0% (n=44) completed testing before the first prescription. For the ML cohort, 57 368 admissions were included. LightGBM was the best-performing ML model. For the prediction of prescription of a targeted medication at 3 and 6 months, area under the receiver operating characteristic curves was 0.926 (95% CI 0.911 to 0.939) and 0.922 (95% CI 0.911 to 0.932), respectively. Area under the precision recall curves was 0.477 (95% CI 0.462 to 0.495) and 0.450 (95% CI 0.411 to 0.494), respectively. CONCLUSION: Only 1% of patients receiving a targeted medication had pharmacogenetic testing before the medication order. We developed an ML model with acceptable performance to predict targeted medication administration with the goal of facilitating earlier pharmacogenomic testing.