4.5 Article

Mapping PROMIS physical function and pain interference to the modified low back pain disability questionnaire

Journal

QUALITY OF LIFE RESEARCH
Volume 31, Issue 12, Pages 3467-3482

Publisher

SPRINGER
DOI: 10.1007/s11136-022-03174-3

Keywords

Low back pain; Physical function; Pain interference; PROMIS; Scale linking

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Linear regression, linear, and equipercentile equating methods were used to map between MDQ and PROMIS-PF and PROMIS-PI scales. Results showed that using both PROMIS-PF and PROMIS-PI together had the closest estimated means, lowest RMSE and MAE, and highest correlations for estimating MDQ total scores. Equipercentile equating using the MDQ items performed best for estimating each of PROMIS-PF and PROMIS-PI T-scores.
Purpose The Modified Low Back Pain Disability Questionnaire (MDQ) is a commonly used tool to assess functioning of patients with low back pain (LBP). Recently, the Patient-Reported Outcomes Measurement Information System (PROMIS) was suggested as an alternative platform to assess LBP patient-reported health. We sought to map between the MDQ and PROMIS Physical Function (PROMIS-PF) and Pain Interference (PROMIS-PI) scales using multiple methods. Methods In a retrospective analysis of LBP patients seen at Cleveland Clinic 11/14/18-12/11/19, T-scores from each PROMIS scale were mapped to MDQ total score individually and together. MDQ item and total scores were mapped to each PROMIS scale. Linear regression as well as linear and equipercentile equating were used. Split sample internal validation using root mean squared error (RMSE), mean absolute error (MAE), and correlations were used to assess accuracy of mapping equations. Results 13585 patients completed the three scales. In the derivation cohort, average age was 59.0 (SD = 15.8); 53.3% female and 82.9% white. Average MDQ total, PROMIS-PF, and PROMIS-PI T-scores were 40.3 (SD = 19.0), 37.2 (SD = 7.6), and 62.9 (SD = 7.2), respectively. For estimating MDQ total scores, methods that used both PROMIS-PF and PROMIS-PI had closest estimated means, lowest RMSE and MAE, and highest correlations. For estimating each of PROMIS-PF and PROMIS-PI T-scores, the best performing method was equipercentile equating using the MDQ items. Conclusions We created and internally validated maps between MDQ and PROMIS-PF and PROMIS-PI using linear regression, linear and equipercentile equating. Our equations can be used by researchers wishing to translate scores between these scales.

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