Identifying Patients Vulnerable to Inadequate Pain Resolution After Cardiac Surgery

Area of Science:

• Pain Management
• Cardiac Surgery
• Data Science

Background:

• Acute postoperative pain (APOP) management is critical for patient recovery after cardiac surgery.
• Traditional pain assessment often relies on point-in-time measures, potentially missing crucial pain trajectory information.
• Understanding pain resolution patterns can optimize patient care and resource allocation.

Purpose of the Study:

• To identify distinct subgroups of acute postoperative pain (APOP) resolution trajectories in cardiac surgical patients.
• To elucidate patient characteristics and pre-existing conditions that predict problematic pain courses.
• To inform potential improvements in postoperative pain monitoring and management strategies.

Main Methods:

• Retrospective analysis of data from 2810 cardiac surgical patients.
• Application of the k-means algorithm for longitudinal data to cluster pain trajectories over 5 postoperative days.
• Ordinal and multinomial logistic regression used to identify predictors of cluster membership.

Main Results:

• Three distinct APOP resolution subgroups were identified: good (37.7%), moderate (44.2%), and poor (18.2%) resolution.
• Predictors for worse pain trajectories included type I diabetes, preoperative opioid use, and illicit drug use.
• Factors increasing risk for the worst pain resolution cluster were female gender, depression, and chronic pain.

Conclusions:

• This study defined three APOP resolution subgroups based on pain score trends post-cardiac surgery.
• Identified patient factors predispose individuals to moderate or poor pain resolution, necessitating targeted interventions.
• Patients with poorer pain trajectories experienced increased opioid consumption and prolonged treatment duration, highlighting the need for further research into optimized management.