Identifying first-line treatment failure for earlier referral to alternative treatments in depression

Project Abstract Because not all patients with major depressive disorder (MDD) respond to standard treatments, alternative therapies such as transcranial magnetic stimulation (TMS), ketamine, and vagus nerve stimulation (VNS) have been introduced. These alternative therapies require more provider-intensive monitoring and thus are typically

only recommended for those with “treatment resistant” depression (TRD). However, establishing treatment resistance clinically requires evidence of multiple treatment failures, and this is a costly and lengthy process that burdens patients. If we could identify patients prone to multiple treatment failures of first-line

therapies, we could refer to alternative treatments sooner. The most relevant component of the task of stratifying patients to alternative treatments is to separate partial response (PR) from true treatment failure (TF) in the patients who do not meet response criteria. The current proposal will use trajectory

classification methods to empirically identify a group of patients with consistent nonresponse to a range of first-line treatments (SSRIs, SNRIs, CBT), more accurately representing true treatment failure (TF). We will use the method in both combined clinical trial data (PReDICT, iSPOT-D, STAR*D) as well as treatment as

usual (TAU) data (MARS). Then to demonstrate that these patients are more likely to be treatment resistant, we will demonstrate that those with TF are less likely to respond to switch/augmentation of first-line treatments than partial responders (PR) for the trial data and examine an association with a measure of TRD collected

for the observational data. If there is a link between initial TF and TRD, this group could then be targeted for earlier referral to alternative treatments. The study will then demonstrate an analytic framework for future modeling of TF by estimating effect size for a previously derived genetic biomarker using proper weighting

methods. The current study will thus allow us to bridge the gap from well-controlled clinical trial data to future studies of more heterogeneous electronic health record (EHR) data and pave the way for possible real- world changes in recommendations for treatment resistant patients.