Objective: Scientists have concluded that genetic profiles cannot predict a large percentage of variation in response to citalopram, a common antidepressant. Using the same data, we examined if a different conclusion can be arrived at when the results are personalized to fit specific patients.
Methods: We used data available through the Sequenced Treatment Alternatives to Relieve Depression database. We created three boosted Classification and Regression Trees to identify 16 subgroups of patients, among whom anticipation of positive or negative response to citalopram was significantly different from 0.5 (P≤0.1).
Results: In a 10-fold cross-validation, this ensemble of trees made no predictions in 33% of cases. In the remaining 67% of cases, it accurately classified response to citalopram in 78% of cases.
Conclusion: For the majority of the patients, genetic markers can be used to guide selection of citalopram. The rules identified in this study can help personalize prescription of antidepressants.