Latent variables may be useful in pain’s assessment

Raymond F Palmer,Donald R Royall,Ricardo Salazar

doi:10.1186/1477-7525-12-13

Abstract

BackgroundUnobserved “latent” variables have the potential to minimize “measurement error” inherent to any single clinical assessment or categorical diagnosis.ObjectivesTo demonstrate the potential utility of latent variable constructs in pain’s assessment.DesignWe created two latent variables representing depressive symptom-related pain (Pd) and its residual, “somatic” pain (Ps), from survey questions.SettingThe Hispanic Established Population for Epidemiological Studies in the Elderly (H-EPESE) project, a longitudinal population-based cohort study.ParticipantsCommunity dwelling elderly Mexican-Americans in five Southwestern U.S. states. The data were collected in the 7th HEPESE wave in 2010 (N = 1,078).MeasurementsSelf-reported pain, Center for Epidemiological Studies Depression Scale (CES-D) scores, bedside cognitive performance measures, and informant-rated measures of basic and instrumental Activities of Daily Living.ResultsThe model showed excellent fit [χ2 = 20.37, DF = 12; p = 0.06; Comparative fit index (CFI) = 0.998; Root mean statistical error assessment (RMSEA) = 0.025]. Ps was most strongly indicated by self-reported pain-related physician visits (r = 0.48, p ≤0.001). Pd was most strongly indicated by self-reported pain-related sleep disturbances (r = 0.65, p <0.001). Both Pd and Ps were significantly independently associated with chronic pain (> one month), regional pain and pain summed across selected regions. Pd alone was significantly independently associated with self-rated health, life satisfaction, self-reported falls, Life-space, nursing home placement, the use of opiates, and a variety of sleep related disturbances. Ps was associated with the use of NSAIDS. Neither construct was associated with declaration of a resuscitation preference, mode of resuscitation preference declaration, or with opting for a “Do Not Resuscitate” (DNR) order.ConclusionThis analysis illustrates the potential of latent variables to parse observed data into “unbiased” constructs with unique predictive profiles. The latent constructs, by definition, are devoid of measurement error that affects any subset of their indicators. Future studies could use such phenotypes as outcome measures in clinical pain management trials or associate them with potential biomarkers using powerful parametric statistical methods.

Highlights

The experience of pain is a complex mental phenomenon only partly explained by physical injury or dysfunction
The latent constructs, by definition, are devoid of measurement error that affects any subset of their indicators
We recently developed a novel latent variable approach to address the similar challenges to cognition’s assessment

Summary

Introduction

The experience of pain is a complex mental phenomenon only partly explained by physical injury or dysfunction. Dementia and depression are likely to influence pain’s report in multiple ways. Both conditions have been recently associated with functional central nervous system (CNS) connectivity, especially in the Default Mode Network (DMN) [1]. DMN connectivity is diminished in Alzheimer’s disease (AD), even at pre-clinical stages [3], and abnormally increased in major depression [4,5]. This may explain the poor correlations between cognitive performance and pain reports in dementia, and increased pain complaints in depressive states [6]. Unobserved “latent” variables have the potential to minimize “measurement error” inherent to any single clinical assessment or categorical diagnosis

Objectives

Results

Conclusion