Prediction of persistent post-surgery pain by preoperative cold pain sensitivity: biomarker development with machine-learning-derived analysis

• Published in: British journal of anaesthesia : BJA Vol. 119; no. 4; pp. 821 - 829
• Main Authors: Lötsch, J., Ultsch, A., Kalso, E.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-10-2017; Oxford University Press
• Subjects: Biomarkers; Breast Neoplasms - complications; Breast Neoplasms - surgery; cold induced pain; Cold Temperature; Female; Finland; human experimental pain; Humans; Pain - diagnosis; Pain - etiology; Pain, Postoperative - diagnosis; Pain, Postoperative - etiology; Post surgery pain; Predictive Value of Tests; Preoperative Care - methods; Reproducibility of Results; Risk Factors; Sensitivity and Specificity; Supervised Machine Learning; Finland; human experimental pain; cold induced pain; Post surgery pain; supervised machine-learning
• ISSN: 0007-0912; 1471-6771
• DOI: 10.1093/bja/aex236

To prevent persistent post-surgery pain, early identification of patients at high risk is a clinical need. Supervised machine-learning techniques were used to test how accurately the patients’ performance in a preoperatively performed tonic cold pain test could predict persistent post-surgery pain.
We analysed 763 patients from a cohort of 900 women who were treated for breast cancer, of whom 61 patients had developed signs of persistent pain during three yr of follow-up. Preoperatively, all patients underwent a cold pain test (immersion of the hand into a water bath at 2–4 °C). The patients rated the pain intensity using a numerical ratings scale (NRS) from 0 to 10. Supervised machine-learning techniques were used to construct a classifier that could predict patients at risk of persistent pain.
Whether or not a patient rated the pain intensity at NRS=10 within less than 45 s during the cold water immersion test provided a negative predictive value of 94.4% to assign a patient to the “persistent pain” group. If NRS=10 was never reached during the cold test, the predictive value for not developing persistent pain was almost 97%. However, a low negative predictive value of 10% implied a high false positive rate.
Results provide a robust exclusion of persistent pain in women with an accuracy of 94.4%. Moreover, results provide further support for the hypothesis that the endogenous pain inhibitory system may play an important role in the process of pain becoming persistent.