Algorithm-informed treatment from EEG patterns improves outcomes for patients with major depressive disorder.

Abstract

Objective: Selecting the right medication for major depressive disorder (MDD) is challenging, and patients are often on several medications before an effective one is found. Using patient EEG patterns with computer models to select medications is a potential solution, however, it is not widely performed. Therefore, we evaluated a commercially available EEG data analysis system to help guide medication selection in a clinical setting.

Methods: Patients with MDD were recruited, and their physicians used their own judgment to select medications (Control; n = 115) or relied on computer-guided selection (PEER n = 165) of medications. Quick Inventory of Depressive Symptomatology (QIDS SR-16) scores were obtained from patients, before the start of the study (day 0) and again at ~90 and ~180 d. Patients in the PEER arm were classified into one of 4 groups depending on if the report was followed throughout (RF/RF), the first 90 days only (RF/RNF), the second 90 days only (RNF/RF), or not at all (RNF/RNF). Outcomes were then compared with controls whose physician performed the EEG and submitted data but did not receive the PEER report.

Results: Patients in the controls, RF/RF and RNF/RNF groups had fewer depressive symptoms at 90 and 180 days, but the response was significantly stronger for patients in the RF/RF group. Lower rates of suicidal ideation were also noted in the RF/RF group than the control group at 90 and 180 days of treatment.

Conclusion: Computational analysis of EEG patterns may augment physicians' skills at selecting medications for the patients.