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Trajectories of Pain Scores and Analgesic Administration Following Cardiac Surgery
Adam J Milam1, David O Warner2, Skye Buckner-Petty3
1Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Phoenix, AZ; Department of Quantitative Health Sciences, Mayo Clinic, Phoenix, AZ.
Objectives:
Limited data exist on postoperative pain trajectories following cardiac surgery. This study sought to identify distinct postoperative trajectories of pain and opioid use in patients undergoing cardiothoracic surgery with cardiopulmonary bypass.
Design:
Post hoc analysis of a retrospective cohort study.
Setting:
Four US hospital campuses.
Participants:
Adult patients who underwent cardiac surgery from 2018 to 2023.
Interventions:
None.
Measurements And Main Results:
The primary aim was the identification of joint trajectories of postoperative pain scores (0-10 verbal rating scale) and opioid administration (standardized to morphine milligram equivalents [MMEs] from postoperative days 0-7), using latent class trajectory modeling. Multinomial logistic regression assessed associations between predictors and trajectory class membership. Eight distinct postoperative pain and MME trajectory classes were identified among 11,172 patients, ranging from low pain/low MMEs to escalating or persistent pain/high MMEs. The most favorable group (class 1 [13%]) showed consistently low pain and low opioid use; these patients were more likely to be older, have a lower body mass index, present for surgery in 2018 (v 2023), and receive intraoperative methadone and less likely to be diagnosed with chronic pain preoperatively. Odds of class membership varied by demographics, procedure type, and intraoperative analgesia. Minor differences in postoperative complications were observed across classes.
Conclusions:
Latent class trajectory modeling identified heterogeneous recovery patterns of pain and opioid use after cardiothoracic surgery. Although we have identified some factors associated with class membership, including demographic, procedural, and medication variables, the causal reasons that determine pain experiences remain largely unexplained. This modeling method holds promise for future hypothesis-generating observational studies to optimize postoperative pain outcomes.
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