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

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.

Keywords: Clopidogrel; Computer decision support; Electronic health records; Precision medicine; Statin; Warfarin.

Figure 1

Medication-free survival: Kaplan-Meier based estimates of medication free survival. The left panel corresponds to baseline data obtained at the medical home date. The right panel corresponds to longitudinal data where the x-axis is the time since last participating clinic visit.

Figure 2

The Cox proportional hazards model estimates from the training dataset based on data from 2005 to 2010. We include a measure of variable importance (VI) that is defined as the likelihood ratio Chi-square statistic minus the degrees of freedom used to estimate the variable construct.

Figure 3

Time-dependent AUROC(t) calculated by applying the training data model to the training dataset across bootstrap replicates (black line), and the training data model to the baseline and longitudinal validation and implementation datasets.

Figure 4

Calibration plots for one- and two-year risk estimates. Estimates were calculated by applying the training set model to itself across bootstrap replicates and the resulting training set model to the validation and implementation datasets. Modified boxplots highlight the 1^st, 10^th, 25^th, 50^th, 75^th, 90^th, and 99^th percentiles of the predicted risk distributions.

Figure 5

Cumulative proportion of pre-emptively genotyped patients prescribed target medications over time since the medical home date.

Figure 6

Calibration plots for log-estimated standard error of the log-survivor function based on 25 bootstrap replicate. The y-axis shows the log of the estimated standard error from the original dataset, and the x-axis shows the bootstrapped based predictions of those values.