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Updated: Aug 8, 2025
An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
Predicting severe pain after major surgery: a secondary analysis of the Peri-operative Quality Improvement Programme
R A Armstrong1,2, A Fayaz3,4, G L P Manning4
1Department of Population Health Sciences, University of Bristol, Bristol, UK.
Insights
Predicting severe postoperative pain is crucial for better patient outcomes. A new model using pre-operative data shows moderate accuracy, but including peri-operative factors improves prediction significantly.
Area of Science:
- Anesthesiology and Perioperative Medicine
- Pain Management
- Health Informatics
Background:
- Acute postoperative pain is a frequent complication, increasing patient morbidity.
- Early identification of at-risk patients can enable targeted interventions.
- Developing a predictive tool for severe postoperative pain is essential for proactive management.
Purpose of the Study:
- To develop and internally validate a predictive tool for severe acute postoperative pain.
- To identify pre-operative and peri-operative variables associated with severe postoperative pain.
- To assess the performance of a predictive model for identifying high-risk surgical patients.
Main Methods:
- Logistic regression model developed using pre-operative variables from 17,079 major surgery patients.
- Internal validation included assessing model calibration and discrimination (c-statistic).
- Secondary analyses incorporated peri-operative variables to evaluate predictive performance improvement.
Main Results:
- 18.4% of patients reported severe postoperative pain, more common in females, cancer patients, and smokers.
- The pre-operative model achieved moderate discrimination (c-statistic 0.66) and good calibration.
- Including intra-operative variables significantly improved model performance, indicating pre-operative data alone is insufficient.
Conclusions:
- A predictive model using pre-operative factors can identify some patients at risk of severe postoperative pain.
- Peri-operative variables are necessary to adequately predict postoperative pain.
- Modifiable factors like smoking and psychological well-being present targets for intervention.
Abstract:
Acute postoperative pain is common, distressing and associated with increased morbidity. Targeted interventions can prevent its development. We aimed to develop and internally validate a predictive tool to pre-emptively identify patients at risk of severe pain following major surgery. We analysed data from the UK Peri-operative Quality Improvement Programme to develop and validate a logistic regression model to predict severe pain on the first postoperative day using pre-operative variables. Secondary analyses included the use of peri-operative variables. Data from 17,079 patients undergoing major surgery were included. Severe pain was reported by 3140 (18.4%) patients; this was more prevalent in females, patients with cancer or insulin-dependent diabetes, current smokers and in those taking baseline opioids. Our final model included 25 pre-operative predictors with an optimism-corrected c-statistic of 0.66 and good calibration (mean absolute error 0.005, p = 0.35). Decision-curve analysis suggested an optimal cut-off value of 20-30% predicted risk to identify high-risk individuals. Potentially modifiable risk factors included smoking status and patient-reported measures of psychological well-being. Non-modifiable factors included demographic and surgical factors. Discrimination was improved by the addition of intra-operative variables (likelihood ratio χ2 496.5, p < 0.001) but not by the addition of baseline opioid data. On internal validation, our pre-operative prediction model was well calibrated but discrimination was moderate. Performance was improved with the inclusion of peri-operative covariates suggesting pre-operative variables alone are not sufficient to adequately predict postoperative pain.
