OBJECTIVE: Assessment of patient-reported outcome measures (PROMs) for hand and upper-extremity surgery patients using measures such as the Quick Disabilities of the Arm, Shoulder, and Hand (qDASH), as well as general measures including the Patient-Reported Outcomes Measurement Information System Upper Extremity Physical Function domain via a Computer-Adaptive Test (PROMIS UE CAT), has become commonplace. The aim of this study was to link, for crosswalking, the qDASH measure to both versions of the PROMIS UE CAT (v1.2 and v2.0).
METHODS: We included 18,944 hand and upper-extremity patients who completed both versions of the PROMIS UE CAT and the qDASH at the same clinical encounter. Shoulder pathology was excluded. Score linkage was performed using the R package equate, and multiple equating models (linear regression, identity, mean, linear, equipercentile, and circle-arc models) were used to establish crosswalk tables.
RESULTS: Mean qDASH and PROMIS UE CAT v1.2 scores were 38.2 (SD = 23.1) and 36.6 (SD = 9.8), respectively. Mean qDASH and PROMIS UE CAT v2.0 scores were 37.3 (SD = 21.8) and 38.3 (SD = 10.4), respectively. Pearson correlations had very strong linear relationships between the qDASH and the PROMIS UE CAT v1.2 and PROMIS UE CAT v2.0 (r = -0.83 [-0.84, -0.92] and r = -0.80 [-0.81, -0.80], respectively). For the equipercentile equating models, the intraclass correlation coefficient (ICC) had very strong positive relationships to linking measures with ICC = 0.85 (0.84, 0.86) for the qDASH-UE CAT v1.2 crosswalk and ICC = 0.83 (0.82, 0.84) for the qDASH-UE CAT v2.0 crosswalk.
CONCLUSIONS: The linkages establish crosswalk tables using equipercentile equating models to convert the PROMIS UE CAT v1.2 and v2.0 scores to the qDASH and vice versa.
CONCLUSIONS: This study provides crosswalk tables for commonly collected PROMs in hand surgery, increasing the comparability of results between centers using different PROMs to study the same conditions or treatments.

UNASSIGNED: Major depressive disorder (MDD) is a leading cause of disability worldwide. An accurate assessment of depressive symptomology is crucial for clinical management and research. This study assessed the convergent validity, reliability, and total scale score interconversion across the 9-item Patient Health Questionnaire (PHQ-9) self-report, the 16-item Quick Inventory of Depressive Symptomatology-clinician report (QIDS-C) (two widely used clinical ratings) and the 5-item Very Brief Quick Inventory of Depressive Symptoms-clinician report (VQIDS-C), which evaluate the core features of MDD.
UNASSIGNED: This study leveraged electronic health record (EHR)-derived, de-identified data from the NeuroBlu Database (Version 23R1), a longitudinal behavioural health real-world platform. Classical Test Theory (CTT) and Item Response Theory (IRT) analyses were used to evaluate the reliability, validity of, and conversions between the scales. The Test Information Function (TIF) was calculated for each scale, with greater test information reflecting higher precision and reliability in measuring depressive symptomology. IRT was also used to generate conversion tables so that total scores on each scale could be compared to the other.
UNASSIGNED: The study sample (n = 2,156) had an average age of 36.4 years (standard deviation [SD] = 13.0) and 59.7% were female. The mean depression scores for the PHQ-9, QIDS-C, and VQIDS-C were 12.9 (SD = 6.6), 12.0 (SD = 4.9), and 6.18 (SD = 3.2), respectively. The Cronbach\'s alpha coefficients for PHQ-9, QIDS-C, and VQIDS-C were 0.9, 0.8, and 0.7, respectively, suggesting acceptable internal consistency. PHQ-9 (TIF = 30.3) demonstrated the best assessment of depressive symptomology, followed by QIDS-C (TIF = 25.8) and VQIDS-C (TIF = 17.7).
UNASSIGNED: Overall, PHQ-9, QIDS-C, and VQIDS-C appear to be reliable and convertible measures of MDD symptomology within a US-based adult population in a real-world clinical setting.

OBJECTIVE: To develop a simple, practical methodology to equate or link equivalent domains of the 36-item Short-Form Health Survey (SF-36) and the Patient-Reported Outcomes Measurement Information System 29-item questionnaire (PROMIS-29) using the Rasch framework.
METHODS: In April 2016, the PROMIS-29 and SF-36 were completed by 1501 individuals selected to be representative of the French population. For each domain common to the two questionnaires, a Partial Credit Model was fitted to the items related to that dimension in the two questionnaires. These items were then calibrated on the same metric, which enabled the scores from one questionnaire to be associated with the scores from the other.
RESULTS: Six of the seven PROMIS-29 scales and five of the six SF-36 subscales (physical, pain, social, vitality, depression and anxiety domains) were equated or linked. Correspondence tables between scores, with a 95% confidence interval, were established for each domain. A freely available Stata macro program was developed to automatize the equating or linking process.
CONCLUSIONS: These results should facilitate comparisons across studies using the SF-36 and the PROMIS-29 in France. The equating or linking process developed is simple to implement and can be used in other countries and for other instruments.

BACKGROUND: The Sheehan Disability Scale (SDS) and the World Health Organization Disability Assessment Scale (WHODAS 2.0) have been widely used to measure functional impairment and disability. To ensure that the scores from these two scales are practically exchangeable across diseases, therapies, and care programmes, the current study aimed to examine the linkage of the WHODAS 2.0 with the SDS and develop a simple and reliable conversion table for the two scales in people with mental disorders.
METHODS: A total of 798 patients (mean age = 36.1, SD = 12.7) were recruited from outpatient clinics of the Institute of Mental Health, and the Community Wellness Clinic in Singapore. Using a single-group design, an equipercentile equating method with log-linear smoothing was used to establish a conversion table from the SDS to the WHODAS 2.0 and vice versa.
RESULTS: The conversion table showed that the scores were consistent for the entire range of scores when the scores were converted either from the SDS to the WHODAS 2.0 or from the WHODAS 2.0 to the SDS. The agreement between the WHODAS 2.0\'s raw and converted scores and SDS\'s raw and converted scores were interpreted as good with intraclass correlation coefficient of 0.711 and 0.725, respectively.
CONCLUSIONS: This study presents a simple and reliable method for converting the SDS scores to the WHODAS 2.0 scores and vice versa, enabling interchangeable use of data across these two disability measures.

BACKGROUND: Trauma registries and their quality improvement programs only collect data from the acute hospital admission, and no additional information is captured once the patient is discharged. This lack of long-term data limits these programs\' ability to affect change. The goal of this study was to create a longitudinal patient record by linking trauma registry data with third party payer claims data to allow the tracking of these patients after discharge.
METHODS: Trauma quality collaborative data (2018-2019) was utilized. Inclusion criteria were patients age ≥18, ISS ≥5 and a length of stay ≥1 d. In-hospital deaths were excluded. A deterministic match was performed with insurance claims records based on the hospital name, date of birth, sex, and dates of service (±1 d). The effect of payer type, ZIP code, International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis specificity and exact dates of service on the match rate was analyzed.
RESULTS: The overall match rate between these two patient record sources was 27.5%. There was a significantly higher match rate (42.8% versus 6.1%, P < 0.001) for patients with a payer that was contained in the insurance collaborative. In a subanalysis, exact dates of service did not substantially affect this match rate; however, specific International Classification of Diseases, Tenth Revision, Clinical Modification codes (i.e., all 7 characters) reduced this rate by almost half.
CONCLUSIONS: We demonstrated the successful linkage of patient records in a trauma registry with their insurance claims. This will allow us to the collect longitudinal information so that we can follow these patients\' long-term outcomes and subsequently improve their care.

UNASSIGNED: Little is known about the outcomes that are most important to prosthesis users and funders. A Prosthetic Interventions Core Outcome Set (PI-COS) will help researchers and practitioners measure outcomes that are the most important to prosthesis users and funders.
UNASSIGNED: Prosthesis users and funders rated the importance of 121 International Classification of Functioning, Disability, and Health (ICF) second-level categories using a two-round Delphi survey. A Consensus Meeting using the nominal group technique resolved rating differences between groups. The ICF second-level categories were ranked according to importance and a K-Means Cluster Analysis helped establish the PI-COS.
UNASSIGNED: 65 users and 8 funders completed the Delphi surveys, followed by a Consensus Meeting. 26 ICF second-level categories were considered important to prosthesis users and funders and a PI-COS of 14 ICF second-level categories drawn predominantly from five ICF chapters was established: Sensory Functions and Pain (b2), Neuromusculoskeletal and Movement-related Functions (b7), General Tasks and Demands (d2), Mobility (d4), and Products and Technology (e1).
UNASSIGNED: The PI-COS describes the outcomes that are most important to prosthesis users and funders. The PI-COS can help focus on the most important outcome measures in clinical practice and research, including future prosthetic health economic evaluations.

There is a clear need to harmonize outcome measurement. Some authors propose to express scores as T scores to facilitate interpretation of PROM results in clinical practice. While this is a step in the right direction, there are important limitations to the acceptance of the T score metric as a common metric when T scores are based on raw sum scores of ordinal items: Such T scores of different instruments are not exactly comparable because they are not interval scaled; T scores of different measures are only on the same scale if exactly the same reference group is used; and the T sore metric cannot be maintained because it is reference population-dependent and needs to be updated regularly. These limitations can be overcome by using an item response theory (IRT)-based metric. Items from different measures can be placed on the same IRT metric to make scores comparable on an interval scale. The PROMIS initiative used IRT to develop item banks for measuring various health outcomes. Other PROMs have been linked to the PROMIS metric. Although PROMIS uses a T-score metric for practical reasons, the underlying PROMIS metric is actually an IRT metric. An IRT approach also enables further development of an item bank while preserving the underlying metric. Therefore, IRT-based metrics should be considered as common metrics for the future.

OBJECTIVE: To provide equipercentile equating of physical function (PF) scores from frequently used patient-reported outcome measures (PROMs) in cancer patients to facilitate data pooling and comparisons.
METHODS: Adult cancer patients from five European countries completed the European Organization for Research and Treatment of Cancer (EORTC) computer adaptive test (CAT) Core, EORTC Quality of Life Questionnaire Version 3.0 (QLQ-C30), Functional Assessment of Cancer Therapy - General (FACT-G), 36-item Short Form Health Survey (SF-36), and the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function 20a short form. The R package \"equate\" was used to establish conversion tables of PF scores on those measures with a bivariate rank correlation of at least 0.75.
RESULTS: In total, 953 patients with cancer (mean age 58.9 years, 54.7% men) participated. Bivariate rank correlations between PF scores from the EORTC CAT Core, EORTC QLQ-C30, SF-36, and PROMIS were all above 0.85, but below 0.69 for the FACT-G. Conversion tables were established for all measures but the FACT-G. These tables indicate which score from one PROM best matches the score from another PROM and provide standard errors of converted scores.
CONCLUSIONS: Our analysis indicates that linking of PF scores from both EORTC measures (CAT and QLQ-C30) with PROMIS and SF-36 is possible, whereas the physical domain of the FACT-G seems to be different. The established conversion tables may be used for comparing results or pooling data from clinical studies using different PROMs.

Aligned with the increasing need for standardized assessment of physical function in older individuals with cancer and other conditions, several patient-reported outcome measures (PROMs) have been developed and published. The aim of this study is to link the Strength, Assistance with walking, Rising from a chair, Climbing stairs, and Falls questionnaire (SARC-F), and the Patient-Reported Outcomes Measurement Information System® (PROMIS®) Physical Function Short Form 8c (PROMIS PF 8c), and make their scores convertible, in order to expand the use of both instruments in research and inform clinicians and researchers about the interchangeability of critical cut-off scores.
The sample included 300 participants recruited from an online panel. Participants were included if they had received a cancer diagnosis from a clinician and reported receiving anti-cancer treatment. We conducted five linking procedures and selected an optimal one to generate the crosswalk table between the two measures.
The linked T scores of all five methods showed acceptably small mean differences from the observed T scores, and the standard deviation (SD), and root-mean-squared deviation (RMSD) of the differences were generally similar across all methods. After comparing across all statistics, the Stocking-Lord approach was considered as the optimal approach to compute the crosswalk table for converting SARC-F raw scores to PROMIS PF 8c scores. The crosswalk table shows that the SARC-F cut-off value of 4 between healthy versus symptomatic with a corresponding score of 37 fell in the range of moderate physical function limitation from 30 to 39 on the PROMI PF 8c T score metric.
The linkage in this study has potential for improving clinical and research activities for people with cancer and perhaps others with a similar range of physical function. It facilitates the interpretability in scores of both measures on a common metric anchored on general population for further group-level analysis. Researchers can use this crosswalk to harmonize data collected from either instrument without requiring all cohorts to administer the same instrument for a prospective data collection or retrospective data analysis purpose or for a cross-study effectiveness study.

Research on health anxiety has bloomed in recent years, but summaries of the literature are complicated by the use of dissimilar self-report questionnaires. Furthermore, these instruments have rarely been administered in parallel, and especially not in clinical samples. In this study, we aimed to investigate the relationship between five widespread health anxiety measures, and to draft guidelines for the conversion of different sum scores.
Clinical trial participants with principal pathological health anxiety (n = 335) and a sample of healthy volunteers (n = 88) completed the 14-item Whiteley Index (WI-14), the Illness Attitude Scale (IAS), and the 14-, 18-, and 64-item Health Anxiety Inventory (the HAI-64, HAI-18, and HAI-14). Cross-sectional data from all participants were pooled (N = 423) and we conducted a joint factor analysis and approximate equipercentile linking of the WI-14, IAS, HAI-64, HAI-18, and HAI-14.
Inter-scale correlations were high (rs ≥ 0.90 and ≥ 0.88 in adjusted analyses), and the scree plot of the joint factor analysis spoke for a unifactorial solution where 89/105 items (85%) had loadings ≥ 0.40. Most items at the core of this broad trait health anxiety factor pertained to the worry about health, the fear of having or developing a serious disease, and to some extent bodily preoccupation. We present a cross-walk table of observed equipercentile linked sum scores.
This study speaks clearly in favor of the WI-14, IAS, HAI-64, HAI-18, and HAI-14 all tapping into the same trait health anxiety construct, the core of which appears to concern the worry about health, the fear of having or developing a serious disease, and to some extent bodily preoccupation. Based on recently reported cut-offs for the HAI-14, a reasonable cutoff for pathological health anxiety in a psychiatric setting probably lies around 7-8 on the WI-14, 52-53 on the IAS, 82-83 on the HAI-64, and 26-27 on the HAI-18.
ClinicalTrials.gov NCT01966705, NCT02314065.