The incidence of chronic post-surgical pain (CPSP) after various common operations is 10% to 50%. Identification of patients at risk of developing chronic pain, and the management and prevention of CPSP remains inadequate. The aim of this study was to develop an easily applicable risk index for the detection of high-risk patients that takes into account the multifactorial aetiology of CPSP. A comprehensive item pool was derived from a systematic literature search. Items that turned out significant in bivariate analyses were then analysed multivariately, using logistic regression analyses. The items that yielded significant predictors in the multivariate analyses were compiled into an index. The cut-off score for a high risk of developing CPSP with an optimal trade-off between sensitivity and specificity was identified. The data of 150 patients who underwent different types of surgery were included in the analyses. Six months after surgery, 43.3% of the patients reported CPSP. Five predictors multivariately contributed to the prediction of CPSP: capacity overload, preoperative pain in the operating field, other chronic preoperative pain, post-surgical acute pain and co-morbid stress symptoms. These results suggest that several easily assessable preoperative and perioperative patient characteristics can predict a patient's risk of developing CPSP. The risk index may help caregivers to tailor individual pain management and to assist high-risk patients with pain coping.

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In this study, we demonstrated that going beyond conventional one-time measurements of acute pain by modelling pain trajectories may substantially enhance research on pain chronification in two ways: First, pain trajectories bear great potential to improve the prediction of CPSP. Second, they represent a meaningful link between psychosocial vulnerability and CPSP because they can be used to uncover mechanisms by which psychosocial vulnerability unfolds. The reported findings suggest that the incidence of CPSP may be reduced by optimizing post-operative pain monitoring.