Predicting Recurrent Care Seeking of Physical Therapy for Musculoskeletal Pain Conditions.

Journal Article (Journal Article)

Objective

Musculoskeletal pain conditions are a leading cause of pain and disability internationally and a common reason to seek health care. Accurate prediction of recurrence of health care seeking due to musculoskeletal conditions could allow for better tailoring of treatment. The aim of this project was to characterize patterns of recurrent physical therapy seeking for musculoskeletal pain conditions and to develop a preliminary prediction model to identify those at increased risk of recurrent care seeking.

Design

Retrospective cohort.

Setting

Ambulatory care.

Subjects

Patients (n = 578,461) seeking outpatient physical therapy (United States).

Methods

Potential predictor variables were extracted from the electronic medical record, and patients were placed into three different recurrent care categories. Logistic regression models were used to identify individual predictors of recurrent care seeking, and the least absolute shrinkage and selection operator (LASSO) was used to develop multivariate prediction models.

Results

The accuracy of models for different definitions of recurrent care ranged from 0.59 to 0.64 (c-statistic), and individual predictors were identified from multivariate models. Predictors of increased risk of recurrent care included receiving workers' compensation and Medicare insurance, having comorbid arthritis, being postoperative at the time of the first episode, age range of 44-64 years, and reporting night sweats or night pain. Predictors of decreased risk of recurrent care included lumbar pain, chronic injury, neck pain, pregnancy, age range of 25-44 years, and smoking.

Conclusion

This analysis identified a preliminary predictive model for recurrence of care seeking of physical therapy, but model accuracy needs to improve to better guide clinical decision-making.

Full Text

Duke Authors

Cited Authors

  • George, SZ; Giczewska, A; Alhanti, B; Lutz, AD; Shanley, E; Thigpen, CA; Bhavsar, NA

Published Date

  • August 2021

Published In

Volume / Issue

  • 22 / 8

Start / End Page

  • 1837 - 1849

PubMed ID

  • 33905514

Pubmed Central ID

  • PMC8502462

Electronic International Standard Serial Number (EISSN)

  • 1526-4637

International Standard Serial Number (ISSN)

  • 1526-2375

Digital Object Identifier (DOI)

  • 10.1093/pm/pnab154

Language

  • eng