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Updated: Feb 19, 2026
An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
External Validation and Update of the RICP-A Multivariate Model to Predict Chronic Postoperative Pain
Tim Mathes1, Carolina Pape-Köhler2, Lena Moerders1
1Institute for Research in Operative Medicine, Witten/Herdecke University, Cologne, Germany.
Objective:
The development of chronic postsurgical pain (CPSP) is influenced by several factors. The risk index for chronic pain (RICP) was developed to identify patients at high risk for CPSP. The aim of this study was the external validation and update of the RICP.
Design:
Prospective cohort study.
Setting:
Two German hospitals.
Subjects:
Participants who underwent orthopedic surgery, general surgery, visceral surgery, and neurosurgery.
Methods:
The predicted outcome was CPSP at six months. We validated the original RICP externally and performed a model update. Analysis was performed using logistic regression. We analyzed the discrimination and calibration of the model. Furthermore, the updated model was internally validated.
Results:
We included 205 patients. The mean age of participants was 51 years. CPSP was reported by 53.9% of participants. In our population, the original RICP (preoperative pain in the operating field, other preoperative pain, postoperative acute pain, capacity overload, and comorbid stress symptoms) showed a sensitivity of 0.708 and a specificity of 0.727 (area under the curve [AUC] = 0.766, 95% confidence interval [CI] = 0.688-0.843). The updated RICP (preoperative pain in the operating field, other preoperative pain, postoperative acute pain, sex, marital status) yielded a sensitivity of 0.746 and a specificity of 0.726 (AUC = 0.813, 95% CI = 0.740-0.886). The results were confirmed by cross-validation. Pre- and postoperative pain measures showed the highest predictive ability.
Discussion:
The study indicates external validity of the original RICP. The updated RICP also showed good predictive ability. The results are limited by the small sample size and the amount of missing outcome data.
