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Clinical, nociceptive and psychological profiling to predict acute pain after total knee arthroplasty

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BACKGROUND: Pre-operative identification of high-pain responders for acute pain after total knee arthroplasty (TKA) could lead to targeted analgesic trials and individualized analgesic strategies to improve recovery and potentially reduce the risk of persistent post-surgical pain. The aim of this study was to use simple clinical tests and questionnaires to identify predictive nociceptive and psychological factors for acute post-TKA pain.

METHODS: Sixty consecutive TKA patients were included in a prospective descriptive study of pain during a 5-m walk-test 24 h post-operatively as the primary outcome. Predictive variables collected prior to surgery included demographics, nociceptive testing (pressure pain threshold (PPT), cold pressor tolerance, electrical pain threshold and tolerance) and psychological profile (pain catastrophizing scale (PCS) and hospital anxiety and depression scale). The prediction of acute post-TKA pain was assessed by univariate analysis, logistic regressions and ROC curves.

RESULTS: Reduced PPT on the arm and increased PCS were predictive variables for moderate/severe post-TKA pain 24 h after surgery (P = 0.007 and P = 0.026, respectively, R-squared 0.21) in the logistic regression model. Odds ratios were 0.67 for a 50 kPa increased PPT and 1.36 for a 5 point increase in PCS. A predictive model with cut-off values of PPT ≤ 245 kPa and PCS≥ 8 point had a sensitivity of 71.4 and a specificity of 62.5.

CONCLUSION: Pre-operative widespread pressure pain hypersensitivity and pain catastrophizing are predictive of moderate severe post-TKA pain. If validated in a larger population, the clinically applicable tests should be considered in future interventions aiming to minimize post-operative pain in high-risk patients.

Original languageEnglish
JournalActa Anaesthesiologica Scandinavica
Volume61
Issue number6
Pages (from-to)676-687
Number of pages12
ISSN0001-5172
DOIs
Publication statusPublished - Jul 2017

    Research areas

  • Journal Article

ID: 50558961