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Derivation of a Risk Assessment Tool for Prediction of Long-Term Pain Intensity Reduction After Physical Therapy

Authors
  • Horn, Maggie E1
  • George, Steven Z2
  • Li, Cai3
  • Luo, Sheng4
  • Lentz, Trevor A2
  • 1 Duke University, Department of Orthopaedic Surgery, Durham, NC, 27701
  • 2 Duke University, Department of Orthopaedic Surgery and Duke Clinical Research Institute, Durham, NC, 27701
  • 3 Yale University, Department of Biostatistics, New Haven, CT
  • 4 Duke University, Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC
Type
Published Article
Journal
Journal of Pain Research
Publisher
Dove Medical Press
Publication Date
May 28, 2021
Volume
14
Pages
1515–1524
Identifiers
DOI: 10.2147/JPR.S305973
PMID: 34093037
PMCID: PMC8169054
Source
PubMed Central
Keywords
Disciplines
  • Original Research
License
Unknown

Abstract

Rationale 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. Aims and Objective 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. Methods 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. Results 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. Conclusion 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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