Derivation of a Risk Assessment Tool for Prediction of Long-Term Pain Intensity Reduction After Physical Therapy.
Abstract
<h4>Rationale</h4>Risk assessment tools can improve clinical decision-making for individuals
with musculoskeletal pain, but do not currently exist for predicting reduction of
pain intensity as an outcome from physical therapy.<h4>Aims and objective</h4>The
objective of this study was to develop a tool that predicts failure to achieve a 50%
pain intensity reduction by 1) determining the appropriate statistical model to inform
the tool and 2) select the model that considers the tradeoff between clinical feasibility
and statistical accuracy.<h4>Methods</h4>This was a retrospective, secondary data
analysis of the Optimal Screening for Prediction of Referral and Outcome (OSPRO) cohort.
Two hundred and seventy-nine individuals seeking physical therapy for neck, shoulder,
back, or knee pain who completed 12-month follow-up were included. Two modeling approaches
were taken: a longitudinal model included demographics, presence of previous episodes
of pain, and regions of pain in addition to baseline and change in OSPRO Yellow Flag
scores to 12 months; two comparison models included the same predictors but assessed
only baseline and early change (4 weeks) scores. The primary outcome was failure to
achieve a 50% reduction in pain intensity score at 12 months. We compared the area
under the curve (AUC) to assess the performance of each candidate model and to determine
which to inform the Personalized Pain Prediction (P3) risk assessment tool.<h4>Results</h4>The
baseline only and early change models demonstrated lower accuracy (AUC=0.68 and 0.71,
respectively) than the longitudinal model (0.79) but were within an acceptable predictive
range. Therefore, both baseline and early change models were used to inform the P3
risk assessment tool.<h4>Conclusion</h4>The P3 tool provides physical therapists with
a data-driven approach to identify patients who may be at risk for not achieving improvements
in pain intensity following physical therapy.
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https://hdl.handle.net/10161/23410Published Version (Please cite this version)
10.2147/jpr.s305973Publication Info
Horn, Maggie E; George, Steven Z; Li, Cai; Luo, Sheng; & Lentz, Trevor A (2021). Derivation of a Risk Assessment Tool for Prediction of Long-Term Pain Intensity Reduction
After Physical Therapy. Journal of pain research, 14. pp. 1515-1524. 10.2147/jpr.s305973. Retrieved from https://hdl.handle.net/10161/23410.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Steven Zachary George
Laszlo Ormandy Distinguished Professor of Orthopaedic Surgery
Dr. George’s primary interest is research involving biopsychosocial models for the
prevention and treatment of chronic musculoskeletal pain disorders. His long term
goals are to 1) improve accuracy for predicting who is going to develop chronic pain;
and 2) identify non-pharmacological treatment options that limit the development of
chronic pain conditions. Dr. George is an active member of the American Physical
Therapy Association, United States Association of the Study of
Maggie Elizabeth Horn
Assistant Professor in Orthopaedic Surgery
Trevor A. Lentz
Assistant Professor in Orthopaedic Surgery
Sheng Luo
Professor of Biostatistics & Bioinformatics
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