Musculoskeletal (MSK) pain conditions are a leading cause of pain and disability internationally and a common reason to seek health care. Accurate prediction of recurrence for health care seeking due to MSK conditions could allow for better tailoring of treatment. The aim of this project was to characterize patterns of recurrent physical therapy seeking for MSK pain conditions and to develop a preliminary prediction model to identify those at increased risk for recurrent care seeking.
Retrospective cohort.
Ambulatory care.
Patients (n = 578,461) seeking outpatient physical therapy (United States).
Potential predictor variables were extracted from the electronic medical record and patients were placed into three different recurrent care categories. Logistic regression models identified individual predictors of recurrent care seeking and Least Absolute Shrinkage and Selection Operator (LASSO) developed multivariate prediction models.
Accuracy of models for different definitions of recurrent care ranged from 0.59 – 0.64 (c-statistic) and individual predictors were identified from multivariate models. Predictors of increased risk for recurrent care included: worker’s compensation and Medicare insurance, comorbid arthritis, post-operative at time of first episode, age range from 44 -64 years, and reporting night sweats/night pain. Predictors of decreased risk for recurrent care included: lumbar pain, chronic injury, neck pain, pregnancy, age range from 25-44 years, and smoking.
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.
© The Author(s) 2021. Published by Oxford University Press on behalf of the American Academy of Pain Medicine.
Predicting Recurrent Care Seeking of Physical Therapy for Musculoskeletal Pain Conditions.
