A rare disease patient-reported outcome measure: revision and validation of the German version of the Systemic Sclerosis Quality of Life Questionnaire (SScQoL) using the Rasch model

Background Rare disease patient-reported outcome measures (PROMs) require linguistic adaptation to overcome the challenge of geographically dispersed patient populations. Importantly, PROMs such as health-related quality of life (HRQoL) should accurately capture responses to patient-identified concerns. The Systemic Sclerosis Quality of Life Questionnaire (SScQoL) is a 29-item tool validated in six languages. Previous evaluation of the German version revealed problems with dichotomous responses. This study aimed to revise the German SScQoL, extend the response structure, and evaluate content and construct validity, reliability and unidimensionality. Methods The instrument validation study involved revising the German SScQoL response structure, cognitive debriefing with patients and validation using Rasch analysis. The revised SScQoL was completed by Swiss-German-speaking patients with SSc within the Swiss MANagement Of Systemic Sclerosis (MANOSS) study. Rasch analysis was employed to test the validity, reliability and unidimensionality of the revised instrument. Results Based on cognitive debriefing with patients (n = 6) dichotomous items were extended to a polytomous 4-point response structure. A total of 78 patients completed the revised SScQoL. Initial analysis of the 29 items suggested the scale lacked fit to the model (χ2 = 51.224, df = 29, p = 0.007). Grouping items into five domains resulted in an adequate fit to the Rasch model (χ2 = 5.343, df = 5, p = 0.376) and unidimensionality (proportion of significant independent t tests: 0.045, 95% CI 0.016–0.114). Overall, the scale was well targeted, had high internal consistency (Person Separation Index, PSI = 0.931) and worked consistently in patients with different demographic and clinical characteristics. Conclusions The revised German SScQoL has a 4-point response structure and is a valid, reliable measure. Rasch analysis is useful for validating continuous response structure of quality of life measures. Further evaluation of measurement equivalence with other German-speaking cultures is required for multinational comparisons and data pooling.

Rare disease patient matchmaking: development and outcomes of an internet case-finding strategy in the Undiagnosed Diseases Network

Background Although clinician, researcher, and patient resources for matchmaking exist, finding similar patients remains an obstacle for rare disease diagnosis. The goals of this study were to develop and test the effectiveness of an Internet case-finding strategy and identify factors associated with increased matching within a rare disease population. Methods Public web pages were created for consented participants. Matches made, time to each inquiry and match, and outcomes were recorded and analyzed using descriptive statistics. A Poisson regression model was run to identify characteristics associated with matches. Results 385 participants were referred to the project and 158 had pages posted. 579 inquiries were received; 89.0% were from the general public and 24.7% resulted in a match. 81.6% of pages received at least one inquiry and 15.0% had at least one patient match. Primary symptom category of neurology, diagnosis, gene page, and photo were associated with increased matches (p ≤ 0.05). Conclusions This Internet case-finding strategy was of interest to patients, families, and clinicians, and similar patients were identified using this approach. Extending matchmaking efforts to the general public resulted in matches and suggests including this population in matchmaking activities can improve identification of similar patients.

The Multiple Stakeholder Approach to Real-world Evidence Generation: the Results of the Jandhyala Method in Observing Expert Consensus on Quality Indicators of Rare Disease Patient Registries (RDRs) by Stakeholder Group

Background:Evidence is valuable to inform decision making. Understanding stakeholder needs from the evidence of key stakeholders is empirically important to those involved in its generation. Where multiple stakeholders exist, an understanding of whether their questions are homogenous or heterogenous necessitates a dedicated approach that will be of value to those invested in the outcomes of their decisions.The pharmaceutical industry engages with non-pharmaceutical industry stakeholders: Payors, Prescribers and, under carefully controlled circumstances, patients when commercialising its medicines. This original research focussed on the differences between these groups and their pharma aligned stakeholders: Regulatory Affairs, Market Access, Commercial and Medical Affairs using measures of their common quality indicators (QIs) from rare disease patient registries.QIs were solicited using the Jandhyala method for observing item awareness and consensus from list generating questioning. They were compared for homogeneity between individual stakeholder groups and the combined pharma and non-pharma stakeholder group population.Results:All stakeholder groups were unique and suggested items peculiar to their own group.One hundred and eleven discrete QIs were identified: Commercial (8/111; 7.21%), Market Access (6/111; 5.41%), Medical Affairs (4/111; 3.60%), Regulatory Affairs (6/111; 5.41%), Patients (14/111; 12.61%), Payors (6/111; 5.41%), and Prescribers (9/111; 8.11%). Each stakeholder's proportion of unique QIs to the total was not statistically significant to the group as a whole.Non-pharma stakeholders generated 29/111 (26.13%) unique QIs, while pharma stakeholders generated 24/111 (21.62%). The total unique QIs across both stakeholder groups were 53/111 (47.75%). Two QIs were unanimously suggested and agreed upon by all stakeholder groups (7/7; 100%): 'Engages with patients and gains their buy-in' and 'Includes a core data set as part of outcomes'.There was homogeneity in consensus on common QIs between Commercial – Market Access (P=0.006), Market Access – Regulators (P=0.006), Commercial – Prescribers (P=0.001), Market Access – Prescribers (P=0.033), and Commercial – Medical Affairs (P=0.020).Conclusions:There is sufficient evidence to support a multiple stakeholder approach for generating real-world evidence. There was a mismatch between pharma and non-pharma stakeholders of 47.75% indicating redundancies of QIs on each side of this divide. Patients and Payor (non-pharma) groups have been highlighted for greater alignment with pharma stakeholder groups.

VarSight: prioritizing clinically reported variants with binary classification algorithms

Background When applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient’s phenotypes. Typically, this is done through annotation, filtering, and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance. Methods We tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network. Results We treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20. Conclusions We demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets.

Challenges in Building and Maintaining Rare Disease Patient Registries: Results of a Questionnaire Survey in Japan at 2012

BackgroundResearch infrastructure such as patient registries and biobanks is expected to play important roles by aggregating information and biospecimens to promote research and development for rare diseases. However, both building and maintaining them can be costly. This paper presents results of a survey of patient registries for rare diseases in Japan conducted at the end of 2012, with emphasis on clarifying costs and efforts related to building and maintaining them.ResultsOf 31 patient registries in Japan found by searching a database of research grant reports and by searching the internet, 11 returned valid responses to this survey. Results show that labor and IT system costs are major expenses for developing and maintaining patient registries. Half of the respondent patient registries had no prospect of securing a budget to maintain them. Responders required the following support for patient registries: financial support, motivation of registrants (medical doctors or patients), and improved communication with and visibility to potential data users. These results resemble those reported from another survey conducted almost simultaneously in Europe (EPIRARE survey).ConclusionsSurvey results imply that costs and efforts to build and maintain patient registries for many rare diseases make them unrealistic. Some alternative strategy is necessary to reduce burdens, such as offering a platform that supplies IT infrastructure and basic secretariat functions that can be used commonly among many patient registries.