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

 
: Lötsch, J.; Ultsch, A.; Kalso, E.

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Fulltext (PDF; )

British journal of anaesthesia : BJA 119 (2017), No.4, pp.821-829
ISSN: 0007-0912
ISSN: 1471-6771
European Commission EC
FP7-HEALTH; 602919; GLORIA
Understanding chronic pain and new druggable targets: Focus on glial-opioid receptor interface
English
Journal Article, Electronic Publication
Fraunhofer IME ()

Abstract
Background. 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.
Methods. 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 degrees 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.
Results. 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.
Conclusion. 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.

: http://publica.fraunhofer.de/documents/N-480435.html