Machine learning–based prediction of clinical pain using multimodal neuroimaging and autonomic metrics. Issue 3 (March 2019)

• Record Type: Journal Article
• Authors: Lee, Jeungchan; Mawla, Ishtiaq; Kim, Jieun; Loggia, Marco L.; Ortiz, Ana; Jung, Changjin; Chan, Suk-Tak; Gerber, Jessica; Schmithorst, Vincent J.; Edwards, Robert R.; Wasan, Ajay D.; Berna, Chantal; Kong, Jian; Kaptchuk, Ted J.; Gollub, Randy L.; Rosen, Bruce R.; Napadow, Vitaly
• Is Part Of: Pain. Volume 160:Issue 3(2019)
• Publication Date: 2019-03
• Subjects: Support vector machine -- Low back pain -- Arterial spin labeling -- Primary somatosensory connectivity -- Heart rate variability; Pain -- Periodicals; Douleur -- Périodiques; Anesthésie -- Périodiques; Pain; Electronic journals; Periodicals; Electronic journals; 616.0472
• DOI: 10.1097/j.pain.0000000000001417
• Languages: English
• ISSNs: 0304-3959

Abstract

Abstract : Abstract: Although self-report pain ratings are the gold standard in clinical pain assessment, they are inherently subjective in nature and significantly influenced by multidimensional contextual variables. Although objective biomarkers for pain could substantially aid pain diagnosis and development of novel therapies, reliable markers for clinical pain have been elusive. In this study, individualized physical maneuvers were used to exacerbate clinical pain in patients with chronic low back pain (N = 53), thereby experimentally producing lower and higher pain states. Multivariate machine-learning models were then built from brain imaging (resting-state blood-oxygenation-level-dependent and arterial spin labeling functional imaging) and autonomic activity (heart rate variability) features to predict within-patient clinical pain intensity states (ie, lower vs higher pain) and were then applied to predict between-patient clinical pain ratings with independent training and testing data sets. Within-patient classification between lower and higher clinical pain intensity states showed best performance (accuracy = 92.45%, area under the curve = 0.97) when all 3 multimodal parameters were combined. Between-patient prediction of clinical pain intensity using independent training and testing data sets also demonstrated significant prediction across pain ratings using the combined model (Pearson's r = 0.63). Classification of increased pain was weighted by elevated cerebral blood flow in the thalamus, and prefrontal and posterior cingulate cortices, and increased primary somatosensory connectivity to frontoinsular cortex. Our machine-learning approach introduces a model with putative biomarkers for clinical pain and multiple clinical applications alongside self-report, from pain assessment in noncommunicative patients to identification of objective pain endophenotypes that can be used in future longitudinal research aimed at discovery of new approaches to combat chronic pain. Abstract : Supplemental Digital Content is Available in the Text.A machine learning–based model used multimodal neuroimaging and autonomic metrics obtained from patients with chronic pain to accurately classify and predict different clinical pain intensity states.