Pain mechanistic networks: the development using supervised multivariate data analysis and implications for chronic pain.

Chronic postoperative pain is present in approximately 20% of patients undergoing total knee arthroplasty. Studies indicate that pain mechanisms are associated with development and maintenance of chronic postoperative pain. The current study assessed pain sensitivity, inflammation, microRNAs, and psychological factors and combined these in a network to describe chronic postoperative pain. This study involved 75 patients with and without chronic postoperative pain after total knee arthroplasty. Clinical pain intensity, Oxford Knee Score, and pain catastrophizing were assessed as clinical parameters. Quantitative sensory testing was assessed to evaluate pain sensitivity and microRNAs, and inflammatory markers were likewise analyzed. Supervised multivariate data analysis with "Data Integration Analysis for Biomarker Discovery" using Latent cOmponents (DIABLO) was used to describe the chronic postoperative pain intensity. Two DIABLO models were constructed by dividing the patients into 3 groups or 2 defined by clinical pain intensities. Data Integration Analysis for Biomarker discovery using Latent cOmponents model explained chronic postoperative pain and identified factors involved in pain mechanistic networks among assessments included in the analysis. Developing models of 3 or 2 patient groups using the assessments and the networks could explain 81% and 69% of the variability in clinical postoperative pain intensity. The reduction of the number of parameters stabilized the models and reduced the explanatory value to 69% and 51%. This is the first study to use the DIABLO model for chronic postoperative pain and to 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.