Predicting Acute Pain After Surgery: A Multivariate Analysis

Abstract

Objectives: To identify perioperative practice patterns that predictably impact postoperative pain.

Background: Despite significant advances in perioperative medicine, a significant portion of patients still experience severe pain after major surgery. Postoperative pain is associated with serious adverse outcomes that are costly to patients and society.

Methods: The presented analysis took advantage of a unique observational data set providing unprecedented detailed pharmacological information. The data were collected by PAIN OUT, a multinational registry project established by the European Commission to improve postoperative pain outcomes. A multivariate approach was used to derive and validate a model predictive of pain on postoperative day 1 (POD1) in 1008 patients undergoing back surgery.

Results: The predictive and validated model was highly significant (P = 8.9E-15) and identified modifiable practice patterns. Importantly, the number of nonopioid analgesic drug classes administered during surgery predicted decreased pain on POD1. At least 2 different nonopioid analgesic drug classes (cyclooxygenase inhibitors, acetaminophen, nefopam, or metamizol) were required to provide meaningful pain relief (>30%). However, only a quarter of patients received at least 2 nonanalgesic drug classes during surgery. In addition, the use of very short-acting opioids predicted increased pain on POD1, suggesting room for improvement in the perioperative management of these patients. Although the model was highly significant, it only accounted for a relatively small fraction of the observed variance.

Conclusion: The presented analysis offers detailed insight into current practice patterns and reveals modifications that can be implemented in today's clinical practice. Our results also suggest that parameters other than those currently studied are relevant for postoperative pain including biological and psychological variables.

Conflict of interest statement

The authors report no conflicts of interest.

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

Figures

FIGURE 1.

Elastic net model. All clinical and pharmacological parameters collected by the PAIN OUT initiative are visualized via force directed layout, which groups highly collinear parameters close to each other. Some predictive parameters were highly collinear (green circle), while others provided more independent information. Parameters included in the predictive model are color-coded, red indicating a positive and blue indicating a negative correlation with pain on POD1. The color intensity is proportional to the model coefficient, and the circle size is proportional to the P value of the correlation. Edges represent the most significant correlations between parameters.

FIGURE 2.

Model coefficient versus measured pain on POD1. A, The EN model predicted pain on POD1 in the training cohort (P<1.0E-17; r= 0.44), and (B) in the validation cohort (P< 8.9E-15; r= 0.34). The box plots depict the relationship between reported pain and model coefficients. Black bars, boxes, and whiskers represent the median, interquartile range, and 95% confidence interval, respectively.

FIGURE 3.

Parameters predicting pain on POD1 are robust. Bootstrapping results are depicted by bars for all assessed clinical and pharmacological parameters. The width of a bar indicates how often (%) in the iterative process a parameter was included in the predictive model. Bars are color-coded, red indicating a positive and blue indicating a negative correlation with pain on POD1. The predictive model included parameters that were selected with a frequency ≥98%.

FIGURE 4.

Individual clinical and pharmacological parameters. A, Opioid use in the post-anesthesia care unit correlated (P < 1.0E-17) with pain on POD1. B, No such correlation was found between intraoperative opioid use and pain on POD1 (P = 4.5E-01). C, The number of different classes of nonopioid analgesics given intraoperatively correlated with decreased pain on POD1 (P = 3.4E-09). D, The use of intraoperative remifentanil correlated with increased pain on POD1 (P = 4.8E-05). Black bars, boxes, and whiskers represent the median, interquartile range, and 95% confidence interval, respectively.