Prognostics for pain in osteoarthritis: Do clinical measures predict pain after total joint replacement?

Autor: João Pinto-Ramos, Joana Barroso, Vasco Galhardo, Diane Reckziegel, Kenta Wakaizumi, Thomas J. Schnitzer, A. Vania Apkarian
Přispěvatelé: Instituto de Investigação e Inovação em Saúde
Rok vydání: 2020
Předmět:
Male
Questionnaires
Biopsychosocial model
Knee Joint
Physiology
Knee Joint / physiopathology
Arthroplasty
Replacement
Hip
Knees
medicine.medical_treatment
Sensory Physiology
Osteoarthritis
Knee / surgery
Osteoarthritis
Arthroplasty
Replacement
Hip / adverse effects
Pathology and Laboratory Medicine
Severity of Illness Index
Osteoarthritis
Hip
Mathematical and Statistical Techniques
0302 clinical medicine
Quality of life
Medicine and Health Sciences
Pain
Postoperative / epidemiology
Arthroplasty
Replacement
Knee
Musculoskeletal System
Pain Measurement
Pain
Postoperative
Principal Component Analysis
Multidisciplinary
Statistics
Pain
Postoperative / therapy
Middle Aged
Osteoarthritis
Knee
Sensory Systems
Somatosensory System
Research Design
Physical Sciences
Neuropathic pain
Medicine
Legs
Female
Anatomy
Research Article
Arthroplasty
Replacement / adverse effects
medicine.medical_specialty
Science
Arthroplasty
Replacement
Knee / methods
Arthroplasty
Replacement
Knee / adverse effects
Pain
Surgical and Invasive Medical Procedures
Research and Analysis Methods
Pelvis
Pain
Postoperative / physiopathology
03 medical and health sciences
Signs and Symptoms
Rheumatology
Diagnostic Medicine
Osteoarthritis
Hip / physiopathology
Severity of illness
medicine
Arthroplasty
Replacement
Hip / methods
Humans
Pain Management
Knee Joint / surgery
Arthroplasty
Replacement
Statistical Methods
Aged
Neuropathic Pain
030203 arthritis & rheumatology
Hip surgery
Hip
Survey Research
Osteoarthritis
Hip / surgery
business.industry
Arthritis
Biology and Life Sciences
Pain Sensation
Osteoarthritis
Knee / physiopathology
medicine.disease
Arthroplasty
Mood
Pain Measurement / methods
Body Limbs
Multivariate Analysis
Physical therapy
business
Mathematics
030217 neurology & neurosurgery
Neuroscience
Zdroj: PLoS ONE
PLoS ONE, Vol 15, Iss 1, p e0222370 (2020)
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0222370
Popis: A significant proportion of osteoarthritis (OA) patients continue to experience moderate to severe pain after total joint replacement (TJR). Preoperative factors related to pain persistence are mainly studied using individual predictor variables and distinct pain outcomes, thus leading to a lack of consensus regarding the influence of preoperative parameters on post-TJR pain. In this prospective observational study, we evaluated knee and hip OA patients before, 3 and 6 months post-TJR searching for clinical predictors of pain persistence. We assessed multiple measures of quality, mood, affect, health and quality of life, together with radiographic evaluation and performance-based tasks, modeling four distinct pain outcomes. Multivariate regression models and network analysis were applied to pain related biopsychosocial measures and their changes with surgery. A total of 106 patients completed the study. Pre-surgical pain levels were not related to post-surgical residual pain. Although distinct pain scales were associated with different aspects of post-surgical pain, multi-factorial models did not reliably predict post-surgical pain in knee OA (across four distinct pain scales) and did not generalize to hip OA. However, network analysis showed significant changes in biopsychosocial-defined OA personality post-surgery, in both groups. Our results show that although tested clinical and biopsychosocial variables reorganize after TJR in OA, their presurgical values are not predictive of post-surgery pain. Derivation of prognostic markers for pain persistence after TJR will require more comprehensive understanding of underlying mechanisms. J.B. was funded through CCDRN [Norte- 08-5369-FSE-000026], OARSI Collaborative Scholarship 2018 and Luso-American Development Foundation R&D@PhD scholarship grant. This research did not receive other specific funding from agencies in the public or commercial sectors.
Databáze: OpenAIRE
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