A multivariate machine-learning model combining multimodal neuroimaging and autonomic metrics accurately predicted within-patient clinical pain intensity states (accuracy 92.45%, AUC 0.97).
Observational (n=53)
Does a multivariate machine-learning model using brain imaging and autonomic activity accurately predict clinical pain intensity in patients with chronic low back pain?
A machine-learning model combining brain imaging and heart rate variability can accurately predict clinical pain intensity states in patients with chronic low back pain, offering a potential objective biomarker for pain assessment.
Effect estimate: AUC 0.97
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.
Lee et al. (Wed,) conducted a observational in Chronic low back pain (n=53). Multivariate machine-learning models using brain imaging and autonomic activity was evaluated on Within-patient classification between lower and higher clinical pain intensity states (AUC 0.97). A multivariate machine-learning model combining multimodal neuroimaging and autonomic metrics accurately predicted within-patient clinical pain intensity states (accuracy 92.45%, AUC 0.97).