Background & Aims

Musculoskeletal pain is a common reason for Emergency Department (ED) visits, but assessment of its impact on outcomes beyond pain severity is often limited (Baharuddin et al., 2010; Fontánez et al., 2021; Garbez et al., 2005). The Optimal Screening for Prediction of Referral and Outcome (OSPRO) Review of Symptoms (ROS) and Yellow Flag (YF) Assessment Tools were developed in physical therapy populations to assess somatic symptoms and psychological factors affecting musculoskeletal pain-related outcomes (George et al., 2015; George et al., 2018; Lentz et al., 2016; Neeley et al., 2022). The purpose of this study was to assess the ability of the OSPRO instruments to predict same-day patient-reported outcomes of pain interference, physical function, and sleep in ED patients with musculoskeletal pain.

Methods

Of 215 adult ED patients presenting with musculoskeletal neck, back, or extremity pain, 165 completed the OSPRO-ROS, OSPRO-YF, Functional Comorbidity Index (FCI) and Patient-Reported Outcomes Measurement Information System (PROMIS-29) and were included in this analysis. Linear regression models were used to assess the capability of the two OSPRO-ROS scores and the four subdomains of OSPRO-YF in predicting each of three PROMIS domains of interest (pain interference, physical function, and sleep). Predictive performance was assessed using Akaikes Information Criteron (AIC). An exhaustive subset search of the 64 possible combinations of the six scores was conducted to find the model with the lowest AIC value. All regression models considered adjusted for patient age, sex, race, and functional comorbidity index (FCI).

Results

The addition of the OSPRO-ROS+ and YF-catastrophizing, fear avoidance and positive coping scores were found to significantly improve the prediction of pain interference scores. The addition of OSPRO-ROS+ and YF-fear avoidance and positive coping scores also improved the prediction of physical function scores. The addition of OSPRO-ROS and YF-positive coping and negative mood scores improved prediction of sleep scores. These added variables were each statistically significant (F-test p-values all <0.001) beyond the base patient characteristics.

Conclusions

These findings suggest the OSPRO assessments may improve physician understanding of ED patients’ pain experience and the biopsychosocial factors that may predict outcomes.

References

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2. Fontánez R, Guasp WR, Ramírez H, et al. Musculoskeletal conditions in the emergency room: a teaching opportunity for medical students and residents. P R Health Sci J. 2021;40(2):68-74.
3. Garbez R, Puntillo K. Acute musculoskeletal pain in the emergency department: a review of the literature and implications for the advanced practice nurse. AACN Clin Issues. 2005;16(3):310-319. doi:10.1097/00044067-200507000-00005
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5. George SZ, Beneciuk JM, Bialosky JE, et al. Development of a Review-of-Systems Screening Tool for Orthopaedic Physical Therapists: Results From the Optimal Screening for Prediction of Referral and Outcome (OSPRO) Cohort. J Orthop Sports Phys Ther. 2015;45(7):512-526. doi:10.2519/jospt.2015.5900
6. George SZ, Beneciuk JM, Lentz TA, et al. Optimal Screening for Prediction of Referral and Outcome (OSPRO) for Musculoskeletal Pain Conditions: Results From the Validation Cohort. J Orthop Sports Phys Ther. 2018;48(6):460-475. doi:10.2519/jospt.2018.7811
7. Lentz TA, Beneciuk JM, Bialosky JE, et al. Development of a Yellow Flag Assessment Tool for Orthopaedic Physical Therapists: Results From the Optimal Screening for Prediction of Referral and Outcome (OSPRO) Cohort. J Orthop Sports Phys Ther. 2016;46(5):327-343. doi:10.2519/jospt.2016.6487
8. Neeley DK, George SZ, Minick K, Snow G, Brennan G. Four Variables Were Sufficient for Low Back Pain: Determining Which Patient-Reported Tools Pain and Disability Improvements. J Orthop Sports Phys Ther. 2022;52(10):685-693. doi:10.2519/jospt.2022.11018

Presenting Author

Stephanie Eucker

Poster Authors

Stephanie Eucker

Duke University

Lead Author

Jenny Foster

B.S.

Duke University School of Medicine

Lead Author

Steven George

Duke University

Lead Author

Alexander Gordee

M.A.

Duke University, Department of Biostatistics & Bioinformatics

Lead Author

Maragatha Kuchibhatla

Ph.D.

Duke University, Department of Biostatistics & Bioinformatics

Lead Author

Topics

  • Assessment and Diagnosis