Predicting severe pain after major surgery: a secondary analysis of the Peri-operative Quality Improvement Programme (PQIP) dataset.
Richard A ArmstrongA FayazG L P ManningS Ramani Moonesinghenull nullC M Olivernull nullPublished in: Anaesthesia (2023)
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 χ 2 496.5, 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.
Keyphrases
- postoperative pain
- chronic pain
- pain management
- quality improvement
- minimally invasive
- patients undergoing
- patient reported
- coronary artery bypass
- neuropathic pain
- risk factors
- type diabetes
- early onset
- electronic health record
- drug induced
- big data
- end stage renal disease
- surgical site infection
- cardiovascular disease
- clinical trial
- liver failure
- ejection fraction
- chronic kidney disease
- newly diagnosed
- intensive care unit
- study protocol
- glycemic control
- artificial intelligence
- respiratory failure
- adipose tissue
- acute coronary syndrome
- machine learning
- peritoneal dialysis
- hepatitis b virus
- atrial fibrillation
- cross sectional
- data analysis
- patient reported outcomes
- prognostic factors
- high intensity