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A prognostic model based on readily available clinical data enriched a pre-emptive pharmacogenetic testing program.

Jonathan S Schildcrout¹, Yaping Shi², Ioana Danciu³

¹Department 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.

Journal of Clinical Epidemiology

|December 3, 2015

Summary

This summary is machine-generated.

A new prognostic model identifies patients likely to benefit from pre-emptive pharmacogenetic testing for statins, warfarin, or clopidogrel based on medication exposure risk.

Keywords:

Clopidogrel Computer decision support Electronic health records Precision medicine Statin Warfarin

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Area of Science:

Pharmacogenomics
Clinical Decision Support
Predictive Modeling

Background:

Pre-emptive pharmacogenetic testing can optimize drug selection and dosing.
Identifying high-risk patients for such testing is crucial for efficient resource allocation.

Purpose of the Study:

To develop and evaluate a prognostic model for pre-emptively selecting patients for genotyping.
To integrate this model into a clinical decision support (CDS) tool.

Main Methods:

A prognostic model was derived using deidentified electronic health records for statin, warfarin, or clopidogrel prescriptions.
The model was implemented in a CDS tool to flag patients exceeding a prescription risk threshold.
The model's performance was evaluated on independent validation and implementation cohorts.

Main Results:

The model showed moderate discrimination (AUC 0.68-0.75) for predicting medication prescriptions.
Patients flagged by the model had significantly higher cumulative incidences of medication prescriptions (0.35-0.48 at 2 years) compared to random sampling (0.12-0.19).
Risk estimates from the model tended to underestimate the true risk.

Conclusions:

Prognostic algorithms can effectively guide pre-emptive pharmacogenetic testing.
This approach directs testing towards patients most likely to benefit, improving clinical utility.