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

Area of Science:

• Anesthesiology and Pain Management
• Surgical Patient Outcomes
• Predictive Analytics in Healthcare

Background:

• Moderate-to-severe acute postsurgical pain (APSP) affects approximately 30% of surgical patients.
• Effective early management of APSP is crucial for patient recovery.
• Preoperative prediction of APSP can facilitate timely interventions.

Purpose of the Study:

• To develop and validate point-of-care risk prediction tools for moderate-to-severe and severe APSP.
• To identify key predictors associated with increased risk of APSP.
• To assess the clinical utility of these predictive models.

Main Methods:

• A multicenter prospective cohort study of 1380 adult patients undergoing elective surgery.
• Logistic regression models were developed using preidentified candidate predictors.
• Internal validation included bootstrap resampling and decision curve analysis.

Main Results:

• Models predicted moderate-to-severe APSP (45.1% incidence) and severe APSP (12.4% incidence) with optimism-corrected AUCs of 0.75 and 0.72, respectively.
• Predictors for increased risk included younger age, female sex, preoperative pain, preoperative opioid use, and longer surgery duration.
• Orthopedic surgery and regional anesthesia were associated with decreased risk.

Conclusions:

• Point-of-care risk tools demonstrated acceptable performance and clinical utility for early APSP prediction.
• These models, developed on diverse surgical cases, can aid in proactive pain management.
• External validation is recommended prior to widespread clinical implementation.