Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data

Abstract

Major depressive disorder (MDD) is complex and multifactorial, posing a major challenge of tailoring the optimal medication for each patient. Current practice for MDD treatment mainly relies on trial and error, with an estimated 42–53% response rates for antidepressant use. Here, we sought to generate an accurate predictor of response to a panel of antidepressants and optimize treatment selection using a data-driven approach analyzing combinations of genetic, clinical, and demographic factors. We analyzed the response patterns of patients to three antidepressant medications in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, and employed state-of-the-art machine learning (ML) tools to generate a predictive algorithm. To validate our results, we assessed the algorithm’s capacity to predict individualized antidepressant responses on a separate set of 530 patients in STAR*D, consisting of 271 patients in a validation set and 259 patients in the final test set. This assessment yielded an average balanced accuracy rate of 72.3% (SD 8.1) and 70.1% (SD 6.8) across the different medications in the validation and test set, respectively (p < 0.01 for all models). To further validate our design scheme, we obtained data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) of patients treated with citalopram, and applied the algorithm’s citalopram model. This external validation yielded highly similar results for STAR*D and PGRN-AMPS test sets, with a balanced accuracy of 60.5% and 61.3%, respectively (both p’s < 0.01). These findings support the feasibility of using ML algorithms applied to large datasets with genetic, clinical, and demographic features to improve accuracy in antidepressant prescription.

A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan

“…Taliaz et al also completed additional statistical calculations of the models performance. The sensitivity, specificity, positive predictive value and negative predictive value were 68.7%, 71.4%, 71.7% and 69%, respectively [ 113 ]. In comparison after only 2 reviews, our recall rate (also known as sensitivity) was 71.4%, similar to Taliaz et al, results and our precision rate (also known as positive predictive value) was 97.4% which far exceeded Taliaz et al, results.…”

“…In 2021, de Nijs et al were able to create individualised models to predict 3- and 6-year symptomatic and global outcomes of patients with schizophrenia-spectrum disorders (mainly with established illness, but variable illness duration) based on patient-reportable data with a study size of 523 schizophrenia-spectrum patients [ 112 ]. Also in 2021, Taliaz et al used genetic, clinical and demographic data from patients with solely MDD in the STAR*D Report to create an ML algorithm that generated an accurate predictor of response to three antidepressant medications with an average balanced accuracy of 72.3% and 70.1% across the medications in validation and test sets, respectively [ 113 ]. They then obtained data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) of patients treated with citalopram (a selective serotonin reuptake inhibitor) [ 113 ].…”

“…Also in 2021, Taliaz et al used genetic, clinical and demographic data from patients with solely MDD in the STAR*D Report to create an ML algorithm that generated an accurate predictor of response to three antidepressant medications with an average balanced accuracy of 72.3% and 70.1% across the medications in validation and test sets, respectively [ 113 ]. They then obtained data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) of patients treated with citalopram (a selective serotonin reuptake inhibitor) [ 113 ]. This external validation yielded accuracy of 60.5% and 61.3% for the STAR*D and PGRN-AMPS , respectively [ 113 ].…”

“…They then obtained data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) of patients treated with citalopram (a selective serotonin reuptake inhibitor) [ 113 ]. This external validation yielded accuracy of 60.5% and 61.3% for the STAR*D and PGRN-AMPS , respectively [ 113 ].…”

Predicting the Treatment Outcomes of Antidepressants Using a Deep Neural Network of Deep Learning in Drug-Naïve Major Depressive Patients

“…Three large genome-wide association studies (GENDEP [ 11 ], STAR*D [ 12 ], and MARS [ 13 ]) have demonstrated an association between genetic variants across the whole genome and the effectiveness of antidepressants, but the small effect size involved in the antidepressant effect limits the clinical application of genetic biomarkers [ 14 ]. Taken together, although there are some known predictors associated with MDD and with the treatment response to antidepressants, establishing a predictive model is necessary to tailor the treatment outcomes of individual MDD patients [ 15 ].…”