The effectiveness of post-professional physical therapist training in the treatment of chronic low back pain using a propensity score approach with machine learning

Low back pain (LBP) is a leading cause of disability in the United States creating substantial hardships through negative social, financial, and health effects. Chronic low back pain (CLBP) accounted for above half of patients treated in physical therapy (PT) clinics for LBP. However, research shows small benefit from PT in CLBP treatment. Preliminary evidence suggests clinician-level training variables may affect outcomes, but requires further investigation to determine whether patients with CLBP benefit from treatment by providers with post-professional training. This study examined the relationship between clinician training levels and patient-reported outcomes in CLBP treatment.

Physical therapies were surveyed using a large patient outcome assessment system to determine and categorise them by level of post-professional education. To account for the possibility that clinicians with higher levels of training are referred more-complex patients, a machine learning approach was used to identify predictive variables for clinician group, then to construct propensity scores to account for differences between groups. Differences in functional status score change among pooled data were analysed using linear models adjusted for propensity scores.

There were no clinically meaningful differences in patient outcomes when comparing clinician post-professional training level. The propensity score method proved to be a valuable way to account for differences at baseline between groups.

Post-professional training does not appear to contribute to improved patient outcomes in the treatment of CLBP. This study demonstrates that propensity score analysis can be used to ensure that differences observed are true and not due to differences at baseline.