BACKGROUND: Patient-reported outcome measures (PROMs) are increasingly integrated into reporting requirements tied to reimbursement. There may be advantages to computer adaptive tests that apply to many different anatomical regions and diseases, provided that important information is not lost. QUESTIONS: 1) Does the Patient-Reported Outcomes Measurement Information System Physical Function (PROMIS PF) computer adaptive test correlate with the Hip injury and Osteoarthritis Outcome Score for Joint Replacement (HOOS, JR: a hip-specific PROM); 2) Is there any difference in the amount of variation explained by various factors (e.g. age, BMI, presence of concomitant knee pain) for both measures? METHODS: In this prospective, cross-sectional study of 213 patients, we assessed the Pearson correlation of PROMIS PF and HOOS, JR. To investigate the variation explained by various patient-level factors, we constructed two multivariable linear regression models. RESULTS: We found a large correlation between PROMIS PF and HOOS, JR (r 0.58, P < 0.001). Disabled or unemployed status was independently associated with both lower PROMIS PF and HOOS, JR scores (regression coefficient [β] -3.4; 95% confidence interval [CI] -5.8 to -1.0; P = 0.006 and β -11; 95% CI -17 to -5.0; P < 0.001, respectively). Private rather than public insurance was associated with both higher PROMIS PF and HOOS, JR scores (β 4.5; 95% CI 2.2 to 6.8; P < 0.001 and β 6.4; 95% CI 0.49 to 12; P = 0.034, respectively). No floor or ceiling effects were observed for PROMIS PF. HOOS, JR scores showed 4.2% floor and 0.5% ceiling effect. CONCLUSIONS: This study adds to the evidence that general measures of physical limitations may provide similar information as joint- or region-specific measures. LEVEL OF EVIDENCE: Level III.
BACKGROUND: Patient-reported outcome measures (PROMs) are increasingly integrated into reporting requirements tied to reimbursement. There may be advantages to computer adaptive tests that apply to many different anatomical regions and diseases, provided that important information is not lost. QUESTIONS: 1) Does the Patient-Reported Outcomes Measurement Information System Physical Function (PROMIS PF) computer adaptive test correlate with the Hip injury and Osteoarthritis Outcome Score for Joint Replacement (HOOS, JR: a hip-specific PROM); 2) Is there any difference in the amount of variation explained by various factors (e.g. age, BMI, presence of concomitant knee pain) for both measures? METHODS: In this prospective, cross-sectional study of 213 patients, we assessed the Pearson correlation of PROMIS PF and HOOS, JR. To investigate the variation explained by various patient-level factors, we constructed two multivariable linear regression models. RESULTS: We found a large correlation between PROMIS PF and HOOS, JR (r 0.58, P < 0.001). Disabled or unemployed status was independently associated with both lower PROMIS PF and HOOS, JR scores (regression coefficient [β] -3.4; 95% confidence interval [CI] -5.8 to -1.0; P = 0.006 and β -11; 95% CI -17 to -5.0; P < 0.001, respectively). Private rather than public insurance was associated with both higher PROMIS PF and HOOS, JR scores (β 4.5; 95% CI 2.2 to 6.8; P < 0.001 and β 6.4; 95% CI 0.49 to 12; P = 0.034, respectively). No floor or ceiling effects were observed for PROMIS PF. HOOS, JR scores showed 4.2% floor and 0.5% ceiling effect. CONCLUSIONS: This study adds to the evidence that general measures of physical limitations may provide similar information as joint- or region-specific measures. LEVEL OF EVIDENCE: Level III.
Authors: Paul A Harris; Robert Taylor; Robert Thielke; Jonathon Payne; Nathaniel Gonzalez; Jose G Conde Journal: J Biomed Inform Date: 2008-09-30 Impact factor: 6.317
Authors: Jorge A Padilla; Hayeem L Rudy; Jonathan A Gabor; Scott Friedlander; Richard Iorio; Raj J Karia; James D Slover Journal: J Arthroplasty Date: 2018-10-13 Impact factor: 4.757
Authors: Stephen Lyman; Yuo-Yu Lee; Patricia D Franklin; Wenjun Li; David J Mayman; Douglas E Padgett Journal: Clin Orthop Relat Res Date: 2016-02-29 Impact factor: 4.176
Authors: Larry D Waldrop; Joseph J King; John Mayfield; Kevin W Farmer; Aimee M Struk; Thomas W Wright; Bradley S Schoch Journal: J Shoulder Elbow Surg Date: 2018-03-06 Impact factor: 3.019
Authors: Man Hung; Charles L Saltzman; Tom Greene; Maren W Voss; Jerry Bounsanga; Yushan Gu; Mike B Anderson; Christopher L Peters; Jeremy Gililland; Christopher E Pelt Journal: J Orthop Res Date: 2017-10-09 Impact factor: 3.494
Authors: Nikolas H Kazmers; Man Hung; Ajinkya A Rane; Jerry Bounsanga; Cindy Weng; Andrew R Tyser Journal: J Hand Surg Am Date: 2017-09-09 Impact factor: 2.230
Authors: James F Fries; James Witter; Matthias Rose; David Cella; Dinesh Khanna; Esi Morgan-DeWitt Journal: J Rheumatol Date: 2013-11-15 Impact factor: 4.666