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.
| Original language | English |
|---|---|
| Pages (from-to) | 840-852 |
| Number of pages | 13 |
| Journal | Anaesthesia |
| Volume | 78 |
| Issue number | 7 |
| Early online date | 2 Mar 2023 |
| DOIs | |
| Publication status | E-pub ahead of print - 2 Mar 2023 |
Bibliographical note
Funding Information:The Peri‐operative Quality Improvement Programme is funded by the Royal College of Anaesthetists, UK; the University College London/UCL Hospitals Surgical Outcomes Research Centre, UK; and the Health Foundation, UK. RA was a National Institute for Health Research‐funded Academic Clinical Fellow at the time of this work. SM is supported in part by the UCLH NIHR Biomedical Research Centre. All views expressed here are those of the authors and not of the NIHR or Department of Health and Social Care. The authors wish to thank the PQIP team (details in Online Supporting Information Appendix S2 ), the clinicians contributing to the study and the patients who have participated. No competing interests declared.
Funding Information:
The Peri-operative Quality Improvement Programme is funded by the Royal College of Anaesthetists, UK; the University College London/UCL Hospitals Surgical Outcomes Research Centre, UK; and the Health Foundation, UK. RA was a National Institute for Health Research-funded Academic Clinical Fellow at the time of this work. SM is supported in part by the UCLH NIHR Biomedical Research Centre. All views expressed here are those of the authors and not of the NIHR or Department of Health and Social Care. The authors wish to thank the PQIP team (details in Online Supporting Information Appendix S2), the clinicians contributing to the study and the patients who have participated. No competing interests declared.
Publisher Copyright:
© 2023 The Authors. Anaesthesia published by John Wiley & Sons Ltd on behalf of Association of Anaesthetists.
