Literature DB >> 18079062

Prediction of chronic post-operative pain: pre-operative DNIC testing identifies patients at risk.

David Yarnitsky1, Yonathan Crispel, Elon Eisenberg, Yelena Granovsky, Alon Ben-Nun, Elliot Sprecher, Lael-Anson Best, Michal Granot.   

Abstract

Surgical and medical procedures, mainly those associated with nerve injuries, may lead to chronic persistent pain. Currently, one cannot predict which patients undergoing such procedures are 'at risk' to develop chronic pain. We hypothesized that the endogenous analgesia system is key to determining the pattern of handling noxious events, and therefore testing diffuse noxious inhibitory control (DNIC) will predict susceptibility to develop chronic post-thoracotomy pain (CPTP). Pre-operative psychophysical tests, including DNIC assessment (pain reduction during exposure to another noxious stimulus at remote body area), were conducted in 62 patients, who were followed 29.0+/-16.9 weeks after thoracotomy. Logistic regression revealed that pre-operatively assessed DNIC efficiency and acute post-operative pain intensity were two independent predictors for CPTP. Efficient DNIC predicted lower risk of CPTP, with OR 0.52 (0.33-0.77 95% CI, p=0.0024), i.e., a 10-point numerical pain scale (NPS) reduction halves the chance to develop chronic pain. Higher acute pain intensity indicated OR of 1.80 (1.28-2.77, p=0.0024) predicting nearly a double chance to develop chronic pain for each 10-point increase. The other psychophysical measures, pain thresholds and supra-threshold pain magnitudes, did not predict CPTP. For prediction of acute post-operative pain intensity, DNIC efficiency was not found significant. Effectiveness of the endogenous analgesia system obtained at a pain-free state, therefore, seems to reflect the individual's ability to tackle noxious events, identifying patients 'at risk' to develop post-intervention chronic pain. Applying this diagnostic approach before procedures that might generate pain may allow individually tailored pain prevention and management, which may substantially reduce suffering.

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Year:  2008        PMID: 18079062     DOI: 10.1016/j.pain.2007.10.033

Source DB:  PubMed          Journal:  Pain        ISSN: 0304-3959            Impact factor:   6.961


  199 in total

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Journal:  Eur J Pain       Date:  2019-01-09       Impact factor: 3.931

Review 4.  Conditioned pain modulation: a predictor for development and treatment of neuropathic pain.

Authors:  Yelena Granovsky
Journal:  Curr Pain Headache Rep       Date:  2013-09

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Journal:  J Pain       Date:  2014-03       Impact factor: 5.820

6.  Investigation of central pain processing in postoperative shoulder pain and disability.

Authors:  Carolina Valencia; Roger B Fillingim; Mark Bishop; Samuel S Wu; Thomas W Wright; Michael Moser; Kevin Farmer; Steven Z George
Journal:  Clin J Pain       Date:  2014-09       Impact factor: 3.442

Review 7.  Pain Modulation: From Conditioned Pain Modulation to Placebo and Nocebo Effects in Experimental and Clinical Pain.

Authors:  Janie Damien; Luana Colloca; Carmen-Édith Bellei-Rodriguez; Serge Marchand
Journal:  Int Rev Neurobiol       Date:  2018-08-14       Impact factor: 3.230

Review 8.  Psychological screening/phenotyping as predictors for spinal cord stimulation.

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Journal:  Curr Pain Headache Rep       Date:  2013-01

9.  Effect of estrogen depletion on pain sensitivity in aromatase inhibitor-treated women with early-stage breast cancer.

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Journal:  J Pain       Date:  2014-01-22       Impact factor: 5.820

10.  Lack of predictive power of trait fear and anxiety for conditioned pain modulation (CPM).

Authors:  Claudia Horn-Hofmann; Janosch A Priebe; Jörg Schaller; Rüdiger Görlitz; Stefan Lautenbacher
Journal:  Exp Brain Res       Date:  2016-08-26       Impact factor: 1.972

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