Perioperative medicine & outcomes
Predicting severe pain after major surgery: a secondary analysis of the Peri‐operative Quality Improvement Programme ( PQIP ) dataset
Authors' conclusion
On internal validation, our pre-operative prediction model was well calibrated but discrimination was moderate.
Study context
- Population
- Patients undergoing major surgery, UK Peri-operative Quality Improvement Programme
- Participants
- 17,079 patients
- Primary endpoint
- Severe pain predicted using pre-operative variables
- Follow-up
- First postoperative day
Methodological detailsprediction model development and internal validation, logistic regression, decision-curve analysis
- Prediction model development and internal validation
- Logistic regression
- Decision-curve analysis
Primary result
As reported in the abstract
Pre-operative prediction model performance
Pre-operative predictors in final model
25
Optimism-corrected c-statistic
0.66
Good calibration, mean absolute error (p = 0.35)
0.005
Optimal cut-off predicted risk (decision-curve analysis)
20-30%
Further findings
Incidence of severe pain · first postoperative day
- Severe pain reported
- 3140 (18.4%)
- More prevalent in females, patients with cancer or insulin-dependent diabetes, current smokers and those taking baseline opioids
Addition of intra-operative variables
- Discrimination improved, likelihood ratio χ2 (p < 0.001)
- 496.5
- Discrimination not improved by baseline opioid data
Risk factors
- Potentially modifiable: smoking status and patient-reported psychological well-being; non-modifiable: demographic and surgical factors
Original publication
Predicting severe pain after major surgery: a secondary analysis of the Peri‐operative Quality Improvement Programme ( PQIP ) dataset. Anaesthesia. Published 2 March 2023. doi:10.
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