Correspondence  |   January 2018
Preoperative Prediction of Chronic Postsurgical Pain after Thoracotomy: Need for Adequately Sized Population-based Samples
Author Notes
  • Parc de Salut MAR, Institut Municipal d’Investigació Mèdica, Universitat Autònoma de Barcelona, Barcelona, Spain (A.M.). amontes@parcdesalutmar.cat
  • (Accepted for publication September 28, 2017.)
    (Accepted for publication September 28, 2017.)×
Article Information
Correspondence
Correspondence   |   January 2018
Preoperative Prediction of Chronic Postsurgical Pain after Thoracotomy: Need for Adequately Sized Population-based Samples
Anesthesiology 1 2018, Vol.128, 224. doi:10.1097/ALN.0000000000001943
Anesthesiology 1 2018, Vol.128, 224. doi:10.1097/ALN.0000000000001943
The article by Bayman et al.,1  evaluating predictors of chronic pain 6 months after thoracic surgery, provides important evidence of the high incidence and severity of chronic postsurgical pain (CPSP) after both thoracotomy and video-assisted thoracic surgery. However, we are surprised the authors found that none of the preoperative factors studied (demographics, psychosocial variables, pain, or quantitative sensory testing) were associated with the emergence of CPSP in this setting, unlike other postsurgical settings.
In a prospective multicenter cohort study published in this journal in 2015,2  we enrolled 503 patients scheduled for thoracotomy (part of a mixed surgical cohort of 2,929) and confirmed CPSP by physical examination at 4 months. We found an incidence of CPSP at 6 months that was similar to the rate of 33% reported by Bayman et al.,1  and we were able to build a preoperative risk model that identified more than 73% of the CPSP cases. Risk was based on six preoperative variables: (1) surgical procedure, (2) age, (3) physical health (Short Form Health Survey-12), (4) mental health (Short Form Health Survey-12), (5) preoperative pain in the surgical field, and (6) preoperative pain in another area. Moderate or intense postoperative pain at 24 h did not substantially improve the model’s predictive value (unpublished analysis).
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