Preoperatively assessed offset analgesia predicts acute postoperative pain following orthognathic surgery.

Objectives: High intensity and longer duration of acute postoperative pain are generally associated with a higher risk of developing chronic postoperative pain. Therefore, it is important to identify the preoperative predictors for acute postoperative pain. Preoperative evaluation of offset analgesia (OA) and the Pain Catastrophising Scale (PCS) may be potential predictors for acute postoperative pain. This study aimed to investigate the relationship between preoperative OA, PCS, and acute postoperative pain following orthognathic surgery.
Methods: Thirty patients (19 females) scheduled to undergo orthognathic surgery were included in this study. OA and PCS were evaluated preoperatively, and the patients reported their postoperative pain intensity using the visual analogue scale [0-100 mm] until it reached zero (number of days with pain). OA was induced on the dominant forearm via three consecutive painful heat pulses delivered for 5 s (T1=46 °C), 5 s (T2=47 °C), and 20 s (T3=46 °C). Subsequently, the associations between OA, PCS, and the number of days with pain were analysed.
Results: The median duration of postoperative pain was 10.3 days. Multiple linear regression analysis showed a significant (p=0.0019) predictive value of OA (p=0.008) for the number of days with pain. The PCS-magnification component was positively correlated with the number of days with pain (R=0.369, p=0.045), with no predictive values of PCS-total and PCS-subscale scores observed.
Conclusions: Preoperative evaluation of OA may be a new individualised, predictive tool for the number of days with acute postoperative pain following orthognathic surgery; hence, a possible biomarker for the patient's vulnerability to developing chronic postoperative pain.
Ethical Committee Number: The study was approved by the Ethics Committee of Meikai University (A1624, A2113).
Trial Registry Number: This study was registered in the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) Clinical Trial (Unique ID: UMIN000026719, UMIN000046957).
(© 2023 the author(s), published by De Gruyter, Berlin/Boston.)