Predicting severe pain after major surgery: a secondary analysis of the Peri-operative Quality Improvement Programme (PQIP) dataset

14Citations
Citations of this article
24Readers
Mendeley users who have this article in their library.

This article is free to access.

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.

Cite

CITATION STYLE

APA

Armstrong, R. A., Fayaz, A., Manning, G. L. P., Moonesinghe, S. R., Oliver, C. M., Wagstaff, D., … Brett, S. (2023). Predicting severe pain after major surgery: a secondary analysis of the Peri-operative Quality Improvement Programme (PQIP) dataset. Anaesthesia, 78(7), 840–852. https://doi.org/10.1111/anae.15984

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free