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

  • 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 °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. Conclusions: 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.

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Author:Jörn LötschORCiDGND, Alfred Ultsch, Eija Kalso
URN:urn:nbn:de:hebis:30:3-500783
DOI:https://doi.org/10.1093/bja/aex236
ISSN:0007-0912
ISSN:1471-6771
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/29121286
Parent Title (English):British journal of anaesthesia
Publisher:Oxford Univ. Press
Place of publication:Oxford [u. a.]
Contributor(s):Lesley Colvin
Document Type:Article
Language:English
Year of Completion:2017
Date of first Publication:2017/12/13
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2019/05/09
Tag:Post surgery pain; cold induced pain; human experimental pain; supervised machine-learning
Volume:119
Issue:4
Page Number:9
First Page:821
Last Page:829
Note:
© The Author 2017. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
HeBIS-PPN:452460344
Institutes:Medizin / Medizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (English):License LogoCreative Commons - Namensnennung-Nicht kommerziell 4.0