Prediction of Differential Pharmacologic Response in Chronic Pain Using Functional Neuroimaging Biomarkers and a Support Vector Machine Algorithm: An Exploratory Study. Issue 11 (22nd September 2021)

• Record Type: Journal Article
• Authors: Ichesco, Eric; Peltier, Scott J.; Mawla, Ishtiaq; Harper, Daniel E.; Pauer, Lynne; Harte, Steven E.; Clauw, Daniel J.; Harris, Richard E.
• Is Part Of: Arthritis & rheumatology. Volume 73:Issue 11(2021)
• Page Start: 2127
• Page End: 2137
• Publication Date: 2021-09-22
• Subjects: Arthritis -- Periodicals; Rheumatism -- Periodicals; 616.72
• DOI: 10.1002/art.41781
• Languages: English
• ISSNs: 2326-5191

Abstract

Abstract : Objective: There is increasing demand for prediction of chronic pain treatment outcomes using machine‐learning models, in order to improve suboptimal pain management. In this exploratory study, we used baseline brain functional connectivity patterns from chronic pain patients with fibromyalgia (FM) to predict whether a patient would respond differentially to either milnacipran or pregabalin, 2 drugs approved by the US Food and Drug Administration for the treatment of FM. Methods: FM patients participated in 2 separate double‐blind, placebo‐controlled crossover studies, one evaluating milnacipran (n = 15) and one evaluating pregabalin (n = 13). Functional magnetic resonance imaging during rest was performed before treatment to measure intrinsic functional brain connectivity in several brain regions involved in pain processing. A support vector machine algorithm was used to classify FM patients as responders, defined as those with a ≥20% improvement in clinical pain, to either milnacipran or pregabalin. Results: Connectivity patterns involving the posterior cingulate cortex (PCC) and dorsolateral prefrontal cortex (DLPFC) individually classified pregabalin responders versus milnacipran responders with 77% accuracy. Performance of this classification improved when both PCC and DLPFC connectivity patterns were combined, resulting in a 92% classification accuracy. These results were not related to confounding factors, including head motion, scanner sequence, or hardware status. Connectivity patterns failed to differentiate drug nonresponders across the 2 studies. Conclusion: Our findings indicate that brain functional connectivity patterns used in a machine‐learning framework differentially predict clinical response to pregabalin and milnacipran in patients with chronic pain. These findings highlight the promise of machine learning in pain prognosis and treatment prediction.