Few studies have investigated factors associated with acute postsurgical pain (APSP) trajectories, and whether the APSP trajectory can predict chronic postsurgical pain (CPSP) remains unclear. We aimed to identify the predictors of APSP trajectories in patients undergoing gastrointestinal surgery. Moreover, we hypothesised that APSP trajectories were independently associated with CPSP. We conducted a prospective cohort study of 282 patients undergoing gastrointestinal surgery to describe APSP trajectories. Psychological questionnaires were administered 1 day before surgery. Meanwhile, demographic characteristics and perioperative data were collected. Average pain intensity during the first 7 days after surgery was assessed by a numeric rating scale (NRS). Persistent pain intensity was evaluated at 3 and 6 months postoperatively by phone call interview. CPSP was defined as pain at the incision site or surrounding areas of surgery with a pain NRS score ≥ 1 at rest. The intercept and slope were calculated by linear regression using the least squares method. The predictors for the APSP trajectory and CPSP were determined using multiple linear regression and multivariate logistic regression, respectively. Body mass index, morphine milligram equivalent (MME) consumption, preoperative chronic pain and anxiety were predictors of the APSP trajectory intercept. Moreover, MME consumption and preoperative anxiety could independently predict the APSP trajectory slope. The incidence of CPSP at 3 and 6 months was 30.58% and 16.42% respectively. APSP trajectory and age were predictors of CPSP 3 months postoperatively, while female sex and preoperative anxiety were predictive factors of CPSP 6 months postoperatively. Preoperative anxiety and postoperative analgesic consumption can predict APSP trajectory. In addition, pain trajectory was associated with CPSP. Clinicians need to stay alert for these predictors and pay close attention to pain resolution.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021210 | PMC |
http://dx.doi.org/10.1038/s41598-022-10504-5 | DOI Listing |
Sci Rep
April 2022
Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, NO. 1 East Jianshe Road, Zhengzhou, 450000, China.
Few studies have investigated factors associated with acute postsurgical pain (APSP) trajectories, and whether the APSP trajectory can predict chronic postsurgical pain (CPSP) remains unclear. We aimed to identify the predictors of APSP trajectories in patients undergoing gastrointestinal surgery. Moreover, we hypothesised that APSP trajectories were independently associated with CPSP.
View Article and Find Full Text PDFSci Rep
August 2021
Department of Anesthesiology, Pain and Palliative Medicine, Radboud University Medical Center, intern 714, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.
Identifying patients at risk is the start of adequate perioperative pain management. We aimed to identify preoperative predictors for acute postsurgical pain (APSP) and for pain at 3 months after surgery to develop prediction models. In a prospective observational study, we collected preoperative predictors and the movement-evoked numerical rating scale (NRS-MEP) of postoperative pain at day 1, 2, 3, 7, week 1, 6 and 3 months after surgery from patients with a range of surgical procedures.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
October 2008
Center for Vision, Speech and Signal Processing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, UK.
In this paper, we propose a multilayered data association scheme with graph-theoretic formulation for tracking multiple objects that undergo switching dynamics in clutter. The proposed scheme takes as input object candidates detected in each frame. At the object candidate level, "tracklets'' are "grown'' from sets of candidates that have high probabilities of containing only true positives.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!