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The following is a summary of “Pain mechanistic networks: the development using supervised multivariate data analysis and implications for chronic pain,” published in the September 2024 issue of Pain by Giordano et al.
Chronic postoperative pain occurs in approximately 20% of individuals with total knee arthroplasty, and pain mechanisms are linked with its development and maintenance.
Researchers conducted a retrospective study to evaluate pain sensitivity, inflammation, microRNAs, and psychological factors combined in a network for chronic postoperative pain.
They analyzed 75 patients with and without chronic postoperative pain after total knee arthroplasty. Clinical pain intensity, Oxford Knee Score, pain catastrophizing, quantitative sensory testing, microRNAs, and inflammatory markers were estimated. The Data Integration Analysis for Biomarker Discovery using the Latent components (DIABLO) method was used to describe chronic postoperative pain intensity, constructing 2 models based on 3 or 2 groups defined by clinical pain intensities.
The results showed that DIABLO effectively explained chronic postoperative pain and identified factors within pain mechanistic networks. Models categorizing patients into 3 or 2 groups accounted for 81% and 69% of clinical postoperative pain intensity variability, respectively. Reducing the number of parameters stabilized the models but decreased their explanatory value to 69% and 51%.
They concluded as the first study to use the DIABLO model for chronic postoperative pain and demonstrate how different pain mechanisms form a pain mechanistic network. The complex model explained 81% of the variability of clinical pain intensity, whereas the less complex model explained 51% of the variability of clinical pain intensity.
Source: journals.lww.com/pain/abstract/9900/pain_mechanistic_networks__the_development_using.709.aspx
