Background & Aims
The STarT Back Screening Tool (SBST) and OSPRO Yellow Flag (OSPRO-YF) assessment tool guide decision-making for musculoskeletal pain management. The SBST identifies risk for persistent disability due to low back pain (LBP). The OSPRO-YF identifies yellow flags in negative mood, fear avoidance, and negative pain coping domains. OSPRO-YF has been used to characterize psychological distress phenotypes (low, medium, and high distress) in people with LBP. A prior clinical trial found that 20% of patients who transitioned to chronic LBP were SBST low risk. The OSPRO-YF may offer information that complements the SBST to better guide prognosis and treatment, but their combined use has not been explored. Our aims are 1) to determine if both SBST and OSPRO-YF explain unique variance in physical function and pain interference due to LBP, and 2) to describe the prevalence of OSPRO-YF domain yellow flags in those with SBST low risk that present with a medium or high distress OSPRO-YF phenotype.
Methods
We used baseline data from two completed randomized LBP trials in the United States Military Health System (n=510; 253 males (49.6%), mean (SD) age 34.7 (8.1) years). For aim 1, we developed hierarchical multivariable regression models to predict PROMIS physical function (PF) and pain interference (PI) with demographic variables (age, sex, race, ethnicity, and education) in step 1, SBST total score in step 2, and OSPRO-YF count in step 3. We then reversed the order of steps 2 and 3 to determine additional variance explained by SBST after accounting for OSPRO-YF. For aim 2, we cross-tabulated high, medium, and low risk classifications of SBST with OSPRO-YF distress phenotypes. Participants classified as SBST low risk that had a medium or high psychological distress OSPRO-YF phenotype were pooled . We then assessed prevalence rates of yellow flags within each of the 3 domains (negative mood, fear avoidance, negative pain coping) in this pooled cohort.
Results
After controlling for demographics, SBST score explained 30% of variance in PROMIS PF (beta = -.561, p<.001). Adding OSPRO-YF (beta = -.155, p<.001) increased the explained variance by 2% (model r-square = .349, p<.001). Reversing order of entry, additional explained variance by OSPRO-YF and SBST was 15% and 17% (both p<.001), respectively. For PROMIS PI, SBST score explained 38% of the variance (beta = .627, p<.001) after controlling for demographic variables. Adding OSPRO-YF (beta = .254, p<.001) increased explained variance by 4% (model r-square = .474, p<.001). Reversing order of entry, additional explained variance by OSPRO-YF and SBST was 24% and 19% (both p<.001), respectively. In aim 2, n=249 (49%) were classified as SBST low risk. Of these, 53% (n=132) had a medium (n=98) or high (n=34) distress phenotype. Within OSPRO-YF domains, 89% had all 3 negative pain coping flags, 18% had all 3 negative mood flags, and 61% had 3 or more fear avoidance flags (out of 5 possible).
Conclusions
Considered separately, SBST explains greater amounts of variance in baseline PI and PF compared to OSPRO-YF. However, using both tools improves variance explained (by at least 2-4%) over using either alone. Together with demographic data, these tools explain a higher percentage of variance in pain interference (47%) compared to physical function (35%) at baseline. Approximately half of those with SBST low risk had phenotypes suggesting higher levels of psychological distress than measured by the SBST. This discrepancy is at least partially explained by high prevalence rates of negative pain coping domain characteristics like low pain self-efficacy and acceptance in the low risk group. These characteristics may not be as well-assessed by the SBST, compared to other psychological characteristics like depression (i.e., negative mood), avoidance beliefs, and catastrophizing. Future studies should determine the collective value of these two tools for predicting longitudinal outcomes.
References
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