Prediction of Differential Pharmacologic Response in Chronic Pain Using Functional Neuroimaging Biomarkers and a Support Vector Machine Algorithm: An Exploratory Study.

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.

• Authors: Eric Ichesco, Scott J Peltier, Ishtiaq Mawla, Daniel E Harper, Lynne Pauer, Steven E Harte, Daniel J Clauw, Richard E Harris
• Journal: Arthritis & rheumatology (Hoboken, N.J.) (Arthritis Rheumatol) Vol. 73 Issue 11 Pg. 2127-2137 (11 2021) ISSN: 2326-5205 [Electronic] United States
• PMID: 33982890 (Publication Type: Clinical Trial, Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: © 2021 Pfizer. Arthritis & Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.

Chemical References

• Analgesics
• Biomarkers
• Pregabalin
• Milnacipran

Topics

• Adult
• Analgesics (therapeutic use)
• Biomarkers
• Brain (diagnostic imaging)
• Chronic Pain (diagnostic imaging, drug therapy)
• Cross-Over Studies
• Double-Blind Method
• Female
• Fibromyalgia (diagnostic imaging, drug therapy)
• Humans
• Magnetic Resonance Imaging
• Middle Aged
• Milnacipran (therapeutic use)
• Neuroimaging
• Pregabalin (therapeutic use)
• Support Vector Machine
• Treatment Outcome
• Young Adult