Predicting acute postsurgical pain in the postanesthesia care unit: risk tool development and internal validation.

INTRODUCTION

Moderate-to-severe acute postsurgical pain (APSP) is experienced by approximately 30% of surgical patients.

OBJECTIVES

To improve early APSP management, we developed 2 pragmatic and generalizable point-of-care risk tools to preoperatively predict moderate-to-severe and severe APSP in the postanaesthesia care unit (PACU).

METHODS

This was a multicenter prospective cohort study of adult patients undergoing elective surgical procedures between May 2021 and May 2023. Candidate predictors were preidentified. Logistic regression was used to develop the models. Internal validation was conducted with bootstrap resampling. Model performance was assessed by discrimination and calibration. Decision curve analysis was conducted to evaluate clinical utility of the models.

RESULTS

The final cohort included 1380 patients; 45.1% experienced moderate-to-severe APSP and 12.4% experienced severe APSP in the PACU. Predictors retained in the final models independently associated with increased risk of both moderate-to-severe and severe pain included younger age, female sex, preoperative pain in the surgical area, preoperative opioid use, and longer expected surgery duration. Orthopedic surgery and regional anesthesia were associated with decreased risk of both outcomes. In addition, minimally invasive surgery was associated with increased risk of moderate-to-severe APSP, and other preoperative pain was associated with increased risk of severe APSP. Optimism-corrected area under the receiver operating characteristic curves were 0.75 and 0.72 for moderate-to-severe and severe APSP models, respectively. Both models demonstrated good calibration and beneficial clinical utility.

CONCLUSION

Our models developed using point-of-care data on a heterogeneous surgery sample demonstrated acceptable performance and clinical utility for early APSP prediction. External validation is needed before implementation.