Using Quantitative Sensory Testing to Predict Attrition in an Interdisciplinary Pain Management Program: A Pilot Study

Zafereo, Jason; Uhlenbrock, Brittany; Watson, Seth; Wang-Price, Sharon; Noe, Carl E.; Jarrett, Robin B.; Meltzer, Karen J.; Huang, Mu

doi:10.2217/pmt-2021-0053

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…Hardman et al (2019) found higher pain catastrophizing to predict attrition. Zafereo et al (2022) found pain intensity and cold hyperalgesia predicted dropout.…”

Section: Ipp Attrition Is High and Variablementioning

confidence: 88%

Predictors of attrition in an interdisciplinary pain management program.

Chong¹

2023

Rehabilitation Psychology

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Purpose/Objective: This study sought to investigate the extent to which demographic and clinical characteristics predict which patients drop out of an interdisciplinary pain management program (IPP). Research Method/Design: Participants (N = 178 outpatients, 18–75 years of age) received treatment for various chronic pain conditions in an IPP (including biopsychosocial assessment, cognitive–behavioral, and physical therapies). Separate logistic regression analyses identified the demographic and clinical variables most predictive of attrition across five domains: (a) demographics, (b) number of medical and non/psychiatric diagnoses, (c) opioid use (yes versus no)/risk of misuse, (d) pain-related cognition and behavior, and (e) physical, social, and mental well-being. Significant predictors from the five domains were integrated in a final multivariable logistic regression model. Results: Among patients exposed to a 4-week IPP, 34% dropped out. In the final model, significant predictors of higher odds of attrition included younger age or being unemployed. Also, patients on opioids at preintervention had higher odds of completing the IPP than patients not on opioids at preintervention. Follow-up analyses revealed 24 of 37 completers (65%) on opioids at preintervention reduced or eliminated use over the course of the IPP. Conclusions/Implications: Because findings are limited by sample and design characteristics, they require replication yet offer novel hypotheses for identifying patients at risk of attrition. Specifically, patients with preintervention opioid use (contrasted with opioid dependence) may particularly benefit from an IPP. Patients at highest risk for early dropout can be targeted for specific engagement interventions to promote completion and effectiveness of IPP.

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“…Hardman et al (2019) found higher pain catastrophizing to predict attrition. Zafereo et al (2022) found pain intensity and cold hyperalgesia predicted dropout.…”

Section: Ipp Attrition Is High and Variablementioning

confidence: 88%

Predictors of attrition in an interdisciplinary pain management program.

Chong¹

2023

Rehabilitation Psychology

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“…Similarly, Dursteler and colleagues showed that low descending pain inhibition, i.e., low conditioned pain modulation (CPM), as assessed by QST was associated withincreased risk for persistent pain following knee replacement surgery [40]. Zaferio and colleagues used QST findings to predict which participants would complete a pain management program and get relief [41]. And Stark weather and colleagues were able to differentiate patients with sustained low back pain from those whose pain resolved based on their results of thermal QST [42].…”

Section: Qst In Precision Pain Managementmentioning

confidence: 99%

The Role of Quantitative Sensory Testing in Clinical Medicine and Research

Anastasi¹,

Capili²,

McMahon³

2022

J Clin Biomed Res

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A healthy peripheral nervous system allows for perception of temperature, touch and vibration at normal levels of stimulation without hypersensitivity or pain and is a key determinant of quality of life. Quantitative sensory testing (QST) describes an array of noninvasive methods for quantifying sensory function and monitoring sensory loss among patients with or at-risk for peripheral neuropathy (PN). Like audiometry, QST quantifies patients’ subjective sensory experience with instrumentation that is precise and amenable to serial use. Thermal QST is the only diagnostic technology able to detect and quantify preclinical sensory deficit via direct patient feedback and merits wider use in clinical practice and research. A comprehensive QST assessment of 13 sensory parameters may be performed in an hour or less; abbreviated assessments take substantially less time. Although increasingly employed to evaluate sensation, define pain phenotypes, and predict outcomes in research studies and to monitor neurologic deficits in clinical practice, QST remains undervalued and underutilized possibly due to limited familiarity, experience, and understanding within the medical community and a lack of published standards regarding its use. This article is intended as a concise QST overview (with a focus on the use of thermal QST for the detection of small fiber abnormalities often associated with early PN) aimed at improving awareness of the unique role of QST in evaluating sensory function

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Quantifying Movement Behavior of Chronic Low Back Pain Patients in Virtual Reality

Gröhn

Liikkanen

Huttunen³

et al. 2023

ACM Trans. Comput. Healthcare

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Chronic low back pain (CLBP) is a globally common musculoskeletal problem. Measuring the sensation of pain and the effect of a treatment has always been a challenge for healthcare. Here, we study how the movement data, collected while using a virtual reality (VR) program, could be used as an objective measurement in patients with CLBP. A specific data collection method based on VR was developed and used with CLBP patients and healthy volunteers. We demonstrate that the movement data in VR can be used to classify individuals in these two groups with a high accuracy by using logistic regression. The most discriminative features are the duration of the movements and the total variation of movement velocity. Furthermore, we show that hidden Markov models can divide movement data into meaningful segments, which creates possibilities for defining even more detailed features, with potential to improve accuracy, when larger datasets become available in the future.

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Using Quantitative Sensory Testing to Predict Attrition in an Interdisciplinary Pain Management Program: A Pilot Study

Cited by 3 publications

References 40 publications

Predictors of attrition in an interdisciplinary pain management program.

Predictors of attrition in an interdisciplinary pain management program.

The Role of Quantitative Sensory Testing in Clinical Medicine and Research

Quantifying Movement Behavior of Chronic Low Back Pain Patients in Virtual Reality

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