A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 176

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016

File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

Quantitative sensory testing, psychological factors, and quality of life as predictors of current and future pain in patients with knee osteoarthritis. | LitMetric

AI Article Synopsis

  • Osteoarthritis (OA) pain varies significantly between individuals, and factors such as psychological well-being, sensory testing, and health-related quality of life play crucial roles in determining pain levels and treatment outcomes.
  • This study explored how baseline pain intensity in OA patients relates to their pain after taking a combination of nonsteroidal anti-inflammatory drugs and paracetamol for three weeks, using the KOOS pain score to measure changes.
  • Findings revealed that pain catastrophizing scores (PCS) were a significant predictor of both baseline and follow-up pain levels, suggesting that incorporating psychological elements like PCS and pressure pain thresholds (TSP) in treatment plans could be beneficial for managing OA pain.

Article Abstract

Substantial interindividual variability characterizes osteoarthritis (OA) pain. Previous findings identify quantitative sensory testing (QST), psychological factors, and health-related quality of life as contributors to OA pain and predictors of treatment outcomes. This exploratory study aimed to explain baseline OA pain intensity and predict OA pain after administration of a nonsteroidal anti-inflammatory drug in combination with paracetamol for 3 weeks. The Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score was used to estimate OA pain presentation. One hundred one patients were assessed at baseline and follow-up using QST (pressure pain thresholds and temporal summation of pain [TSP]), symptoms of depression and anxiety, pain catastrophizing scales (PCSs), and health-related quality of life. Linear regression with backward selection identified that PCS significantly explained 34.2% of the variability in baseline KOOS pain, with nonsignificant contributions from TSP. Pain catastrophizing score and TSP predicted 29.3% of follow-up KOOS pain, with nonsignificant contributions from symptoms of anxiety. When assessed separately, PCS was the strongest predictor (32.2% of baseline and 24.1% of follow-up pain), but QST, symptoms of anxiety and depression, PCS, and quality of life also explained some variability in baseline and follow-up knee OA pain. Further analyses revealed that only TSP and PCS were not mediated by any other included variables, highlighting their role as unique contributors to OA pain presentation. This study emphasizes the importance of embracing a multimodal approach to OA pain and highlights PCS and TSP as major contributors to the baseline OA pain experience and the OA pain experience after OA treatment.

Download full-text PDF

Source
http://dx.doi.org/10.1097/j.pain.0000000000003194DOI Listing

Publication Analysis

Top Keywords

pain
19
quality life
16
koos pain
12
quantitative sensory
8
sensory testing
8
psychological factors
8
health-related quality
8
contributors pain
8
baseline pain
8
pain presentation
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!