Multi-Site Observational Study to Assess Biomarkers for Susceptibility or Resilience to Chronic Pain: The Acute to Chronic Pain Signatures (A2CPS) Study Protocol

Statistical modeling of acute and chronic pain patient-reported outcomes obtained from ecological momentary assessment

ABSTRACT

Ecological momentary assessment (EMA) allows for the collection of participant-reported outcomes (PROs), including pain, in the normal environment at high resolution and with reduced recall bias. Ecological momentary assessment is an important component in studies of pain, providing detailed information about the frequency, intensity, and degree of interference of individuals' pain. However, there is no universally agreed on standard for summarizing pain measures from repeated PRO assessment using EMA into a single, clinically meaningful measure of pain. Here, we quantify the accuracy of summaries (eg, mean and median) of pain outcomes obtained from EMA and the effect of thresholding these summaries to obtain binary clinical end points of chronic pain status (yes/no). Data applications and simulations indicate that binarizing empirical estimators (eg, sample mean, random intercept linear mixed model) can perform well. However, linear mixed-effect modeling estimators that account for the nonlinear relationship between average and variability of pain scores perform better for quantifying the true average pain and reduce estimation error by up to 50%, with larger improvements for individuals with more variable pain scores. We also show that binarizing pain scores (eg, <3 and ≥3) can lead to a substantial loss of statistical power (40%-50%). Thus, when examining pain outcomes using EMA, the use of linear mixed models using the entire scale (0-10) is superior to splitting the outcomes into 2 groups (<3 and ≥3) providing greater statistical power and sensitivity.

Introduction to Bayesian Analyses for Clinical Research

Bayesian analyses are becoming more popular as a means of analyzing data, yet the Bayesian approach is novel to many members of the broad clinical audience. While Bayesian analyses are foundational to anesthesia pharmacokinetic/pharmacodynamic modeling, they also can be used for analyzing data from clinical trials or observational studies. The traditional null hypothesis significance testing (frequentist) approach uses only the data collected from the current study to make inferences. On the other hand, the Bayesian approach quantifies the external information or expert knowledge and combines the external information with the study data, then makes inference from this combined information. We introduce to the clinical and translational science researcher what it means to do Bayesian statistics, why a researcher would choose to perform their analyses using the Bayesian approach, when it would be advantageous to use a Bayesian instead of a frequentist approach, and how Bayesian analyses and interpretations differ from the more traditional frequentist methods. Throughout this paper, we use various pain- and anesthesia-related examples to highlight the ideas and statistical concepts that should be relatable to other areas of research as well.

Electroacupuncture prevents the development or establishment of chronic pain via IL-33/ST2 signaling in hyperalgesic priming model rats

BACKGROUND

Chronic pain is acomplexhealth issue. Compared to acute pain, which has a protective value, chronic pain is defined as persistent pain after tissue injury. Few clinical advances have been made to prevent the transition from acute to chronic pain. Electroacupuncture (EA), the most common form of acupuncture, is widely used in clinical practice to relieve pain.

METHODS

The hyperalgesic priming model, established via a carrageenan injection followed by a prostaglandin E2 injection, was used to investigate the development or establishment of chronic pain. We observed the hyperalgesic effect of EA on rats and investigated the expression p38 mitogen-activated protein kinase, interleukin-33 (IL-33), and its receptor ST2 in astrocytes in the L4-L6 spinal cord dorsal horns (SDHs) after EA. The IL-33/ST2 signaling pathway in SDH is associated with the development of chronic pain.

RESULTS

EA can reverse the pain threshold in hyperalgesic priming model rats and regulates the expression of phosphorylated p38, IL-33, and ST2 in astrocytes in the L4-L6 SDHs. We discovered that EA raises the pain threshold. This suggests that EA can prevent the development or establishment of chronic pain by inhibiting IL-33/ST2 signaling in the lower central nervous system.

CONCLUSIONS

EA can alleviate the development or establishment of chronic pain by modulating IL-33/ST2 signaling in SDHs. Our findings will help clinicians understand the mechanisms of EA analgesia.

Predicting chronic postsurgical pain: current evidence and a novel program to develop predictive biomarker signatures

ABSTRACT

Chronic pain affects more than 50 million Americans. Treatments remain inadequate, in large part, because the pathophysiological mechanisms underlying the development of chronic pain remain poorly understood. Pain biomarkers could potentially identify and measure biological pathways and phenotypical expressions that are altered by pain, provide insight into biological treatment targets, and help identify at-risk patients who might benefit from early intervention. Biomarkers are used to diagnose, track, and treat other diseases, but no validated clinical biomarkers exist yet for chronic pain. To address this problem, the National Institutes of Health Common Fund launched the Acute to Chronic Pain Signatures (A2CPS) program to evaluate candidate biomarkers, develop them into biosignatures, and discover novel biomarkers for chronification of pain after surgery. This article discusses candidate biomarkers identified by A2CPS for evaluation, including genomic, proteomic, metabolomic, lipidomic, neuroimaging, psychophysical, psychological, and behavioral measures. Acute to Chronic Pain Signatures will provide the most comprehensive investigation of biomarkers for the transition to chronic postsurgical pain undertaken to date. Data and analytic resources generatedby A2CPS will be shared with the scientific community in hopes that other investigators will extract valuable insights beyond A2CPS's initial findings. This article will review the identified biomarkers and rationale for including them, the current state of the science on biomarkers of the transition from acute to chronic pain, gaps in the literature, and how A2CPS will address these gaps.