Dr. Vafi Salmasi, MD.

Anti-depressants vs Anti-convulsants

for Chronic Pain

Chronic pain is a major healthcare problem with an annual cost of above $600 billion. The quality of data available for treatments of chronic pain is not optimal. Generalizability of explanatory randomized controlled trial data is problematic as these trials exclude up to 90% of patients: leaving out real-world patients with serious medical and psychological comorbidities. Pragmatic trials embedded in patient care compare effectiveness of currently used treatments in real-world application leading to findings that generalize to broader range of patients. The changes in clinical practice and workflow necessary to integrate this type of research within patient care present pragmatic challenges. In this research, the overall objective is to overcome these challenges using an open-source learning health care system - CHOIR. CHOIR is currently used to track patients' clinical trajectory and treatment response across multiple academic sites resulting in over 25 publications characterizing chronic pain. Through the pilot studies, the investigators have already developed a point-of-care randomization for CHOIR that facilitates integration of research and patient care by allowing the physicians to randomize patients during clinic visits. The investigators have already demonstrated feasibility of the randomization and data collection platform in two ongoing pilot pragmatic clinical trials. The investigators are proposing to better integrate pragmatic research within clinical practice through conducting a randomized comparative effectiveness trial in 450 patients with chronic pain comparing effectiveness of anti-convulsants and anti-depressants (two most commonly used classes of medications for treatment of chronic pain). The investigators will also perform a qualitative interview with all physicians in our clinic to study the impact of integrating pragmatic research into clinical care. The investigators will use the data available in CHOIR as well as the real-world data generated from this clinical trial to build, validate and test a model to predict what clinical characteristics can predict response to either of these classes of medications. The proposed study is the first step to use flexible point-of-care randomization to compare effectiveness of different treatments in different subgroups of patients whenever equipoise exists. The prediction model will guide decision making process of clinicians choosing between these medications based on clinical characteristics of individual patients.