The future of measuring patient‐reported outcomes in rheumatology: Patient‐Reported Outcomes Measurement Information System (PROMIS)

Khanna, Dinesh; Krishnan, Eswar; DeWitt, Esi Morgan; Khanna, Puja P.; Spiegel, Brennan; Hays, Ron D.

doi:10.1002/acr.20581

Cited by 117 publications

(87 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The computer-adaptive assessment continued in this way until the standard error fell below an acceptable level and the patient's responses were consistent with a certain level of physical function on a latent trait continuum. 11 This often resulted in the participant answering between 4 and 12 questions before the assessment ended. Once the computer-adaptive test was complete, a raw physical function score between 0 and 100 was calculated along with a standard deviation that placed each participant along the same latent trait continuum.…”

Section: Methodsmentioning

confidence: 99%

“…Unlike classical test theory, computer-adaptive tests use psychometric properties of item response theory to rank order unidimensional items based on difficulty. 10,11 Essentially, the difficulty of the items and the ability of the patients can be placed on a common metric to allow for a better understanding of the capabilities and precision of an instrument. When these tests are administered using computer-adaptive platforms, unnecessary or redundant items are eliminated and fewer questions are then needed to capture a patient's current health status, improving test accuracy and alleviating patient and clinician burden.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Patient-Reported Outcomes Measurement Information System Physical Function Item Bank, Version 1.0: Physical Function Assessment for Athletic Patient Populations

Madsen

Evans

Snyder

et al. 2016

Journal of Athletic Training

View full text Add to dashboard Cite

Context: The Patient-Reported Outcomes Measurement Information System (PROMIS) item banks have been validated for general populations, but their application to high-functioning patient populations remains speculative.Objective: To examine the measurement properties of the PROMIS physical function item bank, version 1.0, when applied to individuals representing high levels of physical ability.Design: Cross-sectional study. Setting: National Collegiate Athletic Association Division I and III collegiate athletic training rooms and intramural events.Patients or Other Participants: A heterogeneous sample of 215 adults from Division I or Division III collegiate or recreational sports volunteered for this study. Participants were divided into 4 groups depending on sport activity and injury status: healthy collegiate (HC; 33 men, 37 women; age ¼ 19.7 6 1.1 years), injured and currently active in sport (IP; 21 men, 29 women; age ¼ 19.9 6 1.2 years), injured and currently not active in sport (INP; 12 men, 18 women; age ¼ 19.7 6 1.3 years), and healthy recreational (HR; 47 men, 18 women; age ¼ 20.1 6 1.4 years).Main Outcome Measure(s): Participants completed 2 assessments: (1) an injury-history questionnaire and (2) the PROMIS physical function item bank, version 1.0, in computeradaptive form. Mean PROMIS physical function scores were determined for each group.Results: The PROMIS physical function score for the HC group (61.7 6 6.0) was higher than for the IP (54.9 6 7.5) and INP (44.1 6 8.2) groups (P , .001). The IP group had a higher score than the INP group (P , .001). Mean PROMIS scores were not different between the HC and HR participants (mean difference ¼ 1.9, P ¼ .10).Conclusions: The computer-adaptive PROMIS physical function item bank, version 1.0, accurately distinguished injury status in elite-level athletes on a physical function latent trait continuum. Although it was unable to distinguish HC athletes from HR athletes, exposing a possible ceiling effect, it offers potential for use as an outcome instrument for athletic trainers and other sports medicine clinicians.Key Words: athletic injuries, clinical decision making, PROMIS Key PointsThe Patient-Reported Outcomes Measurement Information System (PROMIS) physical function item bank, version 1.0, accurately differentiated physical function in collegiate athletes who were healthy, injured and participating, or injured and not participating. The PROMIS did not distinguish healthy collegiate athletes from healthy recreational athletes. The most difficult sport-related items address running speed and distance, which may not accurately distinguish elite athletes from the general population. Based on these findings, the PROMIS may be a useful tool for assessing physical ability in the early stages of rehabilitation. However, it seems to lack the ability to identify healthy individuals with unrestricted participation.

show abstract

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Patient-Reported Outcomes Measurement Information System Physical Function Item Bank, Version 1.0: Physical Function Assessment for Athletic Patient Populations

Madsen

Evans

Snyder

et al. 2016

Journal of Athletic Training

View full text Add to dashboard Cite

show abstract

“…In an attempt to address these issues, the National Institutes of Health (NIH) fostered the Patient Reported Outcomes Measurement Information System (PROMIS) 13,19 , a program of research designed to develop standardized item banks to assess patient-reported outcomes relevant across diverse medical fields [20][21][22][23] . Through an innovative computerized adaptive testing (CAT) system based on item response theory, only relevant items are selected on the basis of previous responses [20][21][22][23] . The potential for error is reduced and confidence in the respondent's score increases as supplementary items are administered 24 .…”

mentioning

confidence: 99%

Computerized Adaptive Testing of Psychological Factors

Menendez

Bot

Hageman

et al. 2013

The Journal of Bone & Joint Surgery

View full text Add to dashboard Cite

BACKGROUND: Psychological factors are important mediators of the differences between impairment and disability. The most commonly used measures of disability and psychological factors are lengthy and are usually administered as paper questionnaires. The aim of this study was to assess the correlation between perceived disability and psychological factors with use of the user-friendly, web-based Patient Reported Outcomes Measurement Information System initiative, and to compare its correlation with a frequently used, paper-based, pain self-efficacy questionnaire. METHODS: A cohort of 213 patients completed a web-based version of the abbreviated version of the Disabilities of the Arm, Shoulder and Hand questionnaire (QuickDASH), the pain self-efficacy questionnaire, the Patient Reported Outcomes Measurement Information System-based computerized adaptive testing Pain Interference questionnaire, and the Patient Reported Outcomes Measurement Information System-based computerized adaptive testing Depression questionnaire. Bivariate and multivariable analyses measured the correlation of these psychological measures with QuickDASH. RESULTS: There was large correlation between QuickDASH and the Pain Interference computerized adaptive testing (r = 0.74; p lt; 0.001), between the Pain Interference computerized adaptive testing and the pain self-efficacy questionnaire (r = -0.72; p lt; 0.001), and between QuickDASH and the pain self-efficacy questionnaire (r = -0.76; p lt; 0.001). The Depression computerized adaptive testing showed a medium correlation both with QuickDASH (r = 0.37; p lt; 0.001) and with the Pain Interference computerized adaptive testing (r = 0.40; p lt; 0.001). The best multivariable model for QuickDASH included the Pain Interference computerized adaptive testing, prior treatment received, and smoking, and accounted for 57% of the variability. Fifty-one percent of the variability in the QuickDASH was explained by pain interference alone. CONCLUSIONS: Maladaptive responses to upper-extremity pain are accurately measured by the relatively user-friendly Patient Reported Outcomes Measurement Information System-based computerized adaptive testing questionnaire. LEVEL OF EVIDENCE: Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.

show abstract

“…It has demonstrated construct validity in assessing important areas of a person's life affected by chronic diseases and is easily comparable among disease states. However, it has no clinical endpoints yet defined and it may not capture commonly cited problems among patients with SSc, such as symptom burden on their social lives or emotional distress related to physical appearance [18,101,102].…”

Section: Quality Of Life In Ssc-measurement Instrumentsmentioning

confidence: 99%