Key result
EMG-based prediction model achieves ~80% accuracy in distinguishing chronic pain patients from healthy volunteers.
Why the study?
Most studies on objective assessment of central hyperexcitability focus on population differences and lack tools for individual assessment in chronic low back and neck pain.
Can a prediction model based on EMG signals from the nociceptive withdrawal reflex accurately differentiate between healthy volunteers and patients with chronic low back and neck pain?
Case-Control (n=280)
No
Can a prediction model based on EMG signals from the nociceptive withdrawal reflex accurately differentiate between healthy volunteers and patients with chronic low back and neck pain?
A statistical prediction model using EMG signals from the nociceptive withdrawal reflex can classify chronic low back and neck pain patients with up to 80% accuracy, offering a potential tool for individualized assessment of central hyperexcitability.
May aid phenotyping of central hyperexcitability; leaves open diagnostic value pending prospective validation.
BACKGROUND: The nociceptive withdrawal reflex (NWR) has been proven to be a valuable tool in the objective assessment of central hyperexcitability in the nociceptive system at spinal level that is present in some chronic pain disorders, particularly chronic low back and neck pain. However, most of the studies on objective assessment of central hyperexcitability focus on population differences between patients and healthy individuals and do not provide tools for individual assessment. In this study, a prediction model was developed to objectively assess central hyperexcitability in individuals. The method is based on statistical properties of the EMG signals associated with the nociceptive withdrawal reflex. The model also supports individualized assessment of patients, including an estimation of the confidence of the predicted result. RESULTS: up to 80% classification rates were achieved when differentiating between healthy volunteers and chronic low back and neck pain patients. EMG signals recorded after stimulation of the anterolateral and heel regions and of the sole of the foot presented the best prediction rates. CONCLUSIONS: A prediction model was proposed and successfully tested as a new approach for objective assessment of central hyperexcitability in the nociceptive system, based on statistical properties of EMG signals recorded after eliciting the NWR. Therefore, the present statistical prediction model constitutes a first step towards potential applications in clinical practice.
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Manresa et al. (2013) conducted a case-control in Chronic low back and neck pain (n=280). Nociceptive withdrawal reflex (NWR) assessment vs. Healthy subjects was evaluated on Classification rate (accuracy) differentiating between healthy volunteers and chronic pain patients. A probabilistic prediction model based on statistical properties of EMG signals from the nociceptive withdrawal reflex achieved an 80% classification rate in differentiating between healthy volunteers and patients with chronic low back and neck pain.
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