Jonathan S Schildcrout1, Yaping Shi2, Ioana Danciu3, Erica Bowton3, Julie R Field3, Jill M Pulley3, Melissa A Basford3, William Gregg4, James D Cowan3, Frank E Harrell2, Dan M Roden5, Josh F Peterson4, Joshua C Denny4. 1. Department of Biostatistics, Vanderbilt University School of Medicine, 2525 West End Ave, Suite 1100, Nashville, TN 37203, USA; Department of Anesthesiology, Vanderbilt University School of Medicine, 1211 21st Avenue South, Nashville, TN 37212, USA. Electronic address: jonathan.schildcrout@vanderbilt.edu. 2. Department of Biostatistics, Vanderbilt University School of Medicine, 2525 West End Ave, Suite 1100, Nashville, TN 37203, USA. 3. Institute for Clinical and Translational Research, Vanderbilt University School of Medicine, 2525 West End Ave, Nashville, TN, 37203, USA. 4. Department of Biomedical Informatics, Vanderbilt University School of Medicine, 2525 West End Avenue, Suite 1475, Nashville, TN 37203, USA; Department of Medicine, Vanderbilt University School of Medicine, 1161 21st Avenue South, Nashville, TN 37232, USA. 5. Department of Medicine, Vanderbilt University School of Medicine, 1161 21st Avenue South, Nashville, TN 37232, USA; Department of Pharmacology, Vanderbilt University School of Medicine, 1285 Medical Research Building IV, Nashville, TN 37232-0575, USA.
Abstract
OBJECTIVES: We describe the development, implementation, and evaluation of a model to pre-emptively select patients for genotyping based on medication exposure risk. STUDY DESIGN AND SETTING: Using deidentified electronic health records, we derived a prognostic model for the prescription of statins, warfarin, or clopidogrel. The model was implemented into a clinical decision support (CDS) tool to recommend pre-emptive genotyping for patients exceeding a prescription risk threshold. We evaluated the rule on an independent validation cohort and on an implementation cohort, representing the population in which the CDS tool was deployed. RESULTS: The model exhibited moderate discrimination with area under the receiver operator characteristic curves ranging from 0.68 to 0.75 at 1 and 2 years after index dates. Risk estimates tended to underestimate true risk. The cumulative incidences of medication prescriptions at 1 and 2 years were 0.35 and 0.48, respectively, among 1,673 patients flagged by the model. The cumulative incidences in the same number of randomly sampled subjects were 0.12 and 0.19, and in patients over 50 years with the highest body mass indices, they were 0.22 and 0.34. CONCLUSION: We demonstrate that prognostic algorithms can guide pre-emptive pharmacogenetic testing toward those likely to benefit from it.
OBJECTIVES: We describe the development, implementation, and evaluation of a model to pre-emptively select patients for genotyping based on medication exposure risk. STUDY DESIGN AND SETTING: Using deidentified electronic health records, we derived a prognostic model for the prescription of statins, warfarin, or clopidogrel. The model was implemented into a clinical decision support (CDS) tool to recommend pre-emptive genotyping for patients exceeding a prescription risk threshold. We evaluated the rule on an independent validation cohort and on an implementation cohort, representing the population in which the CDS tool was deployed. RESULTS: The model exhibited moderate discrimination with area under the receiver operator characteristic curves ranging from 0.68 to 0.75 at 1 and 2 years after index dates. Risk estimates tended to underestimate true risk. The cumulative incidences of medication prescriptions at 1 and 2 years were 0.35 and 0.48, respectively, among 1,673 patients flagged by the model. The cumulative incidences in the same number of randomly sampled subjects were 0.12 and 0.19, and in patients over 50 years with the highest body mass indices, they were 0.22 and 0.34. CONCLUSION: We demonstrate that prognostic algorithms can guide pre-emptive pharmacogenetic testing toward those likely to benefit from it.
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