scholarly journals The International LAM Registry: A Component of an Innovative Web-Based Clinician, Researcher, and Patient-Driven Rare Disease Research Platform

2010 ◽  
Vol 8 (1) ◽  
pp. 81-87 ◽  
Author(s):  
Michael Nurok ◽  
Ian Eslick ◽  
Carlos R. R. Carvalho ◽  
Ulrich Costabel ◽  
Jeanine D'Armiento ◽  
...  
Keyword(s):  
Rare Disease ◽  
Web Based ◽  
Disease Research ◽  
Research Platform

scholarly journals Validating online approaches for rare disease research using latent class mixture modeling

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Andrew A. Dwyer ◽  
Ziwei Zeng ◽  
Christopher S. Lee
Keyword(s):  
Rare Disease ◽  
Latent Class ◽  
Depression Scale ◽  
Mixture Modeling ◽  
Class I ◽  
Depression Symptoms ◽  
Patient Organization ◽  
Web Based ◽  
Disease Research ◽  
Patient Organizations

Abstract Background Rare disease patients are geographically dispersed, posing challenges to research. Some researchers have partnered with patient organizations and used web-based approaches to overcome geographic recruitment barriers. Critics of such methods claim that samples are homogenous and do not represent the broader patient population—as patients recruited from patient organizations are thought to have high levels of needs. We applied latent class mixture modeling (LCMM) to define patient clusters based on underlying characteristics. We used previously collected data from a cohort of patients with congenital hypogonadotropic hypogonadism who were recruited online in collaboration with a patient organization. Patient demographics, clinical information, Revised Illness Perception Questionnaire (IPQ-R) scores and Zung self-rating depression Scale (SDS) were used as variables for LCMM analysis. Specifically, we aimed to test the classic critique that patients recruited online in collaboration with a patient organization are a homogenous group with high needs. We hypothesized that distinct classes (clinical profiles) of patients could be identified—thereby demonstrating the validity of online recruitment and supporting transferability of findings. Results In total, 154 patients with CHH were included. The LCMM analysis identified three distinct subgroups (Class I: n = 84 [54.5%], Class II: n = 41 [26.6%], Class III: n = 29 [18.8%]) that differed significantly in terms of age, education, disease consequences, emotional consequences, illness coherence and depression symptoms (all p < 0.001) as well as age at diagnosis (p = 0.045). Classes depict a continuum of psychosocial impact ranging from severe to relatively modest. Additional analyses revealed later diagnosis (Class I: 19.2 ± 6.7 years [95% CI 17.8–20.7]) is significantly associated with worse psychological adaptation and coping as assessed by disease consequences, emotional responses, making sense of one’s illness and SDS depressive symptoms (all p < 0.001). Conclusions We identify three distinct classes of patients who were recruited online in collaboration with a patient organization. Findings refute prior critiques of patient partnership and web-based recruitment for rare disease research. This is the first empirical data suggesting negative psychosocial sequelae of later diagnosis (“diagnostic odyssey”) often observed in CHH.

scholarly journals A Medical Report Feature for OSSE Rare Disease Registries

2021 ◽  
Author(s):  
Katja Schueler ◽  
Axel Zieschank ◽  
Jens Göbel ◽  
Jessica Vasseur ◽  
Jannik Schaaf ◽  
...  
Keyword(s):  
Rare Disease ◽  
Process Data ◽  
Patient Registries ◽  
Web Based ◽  
Disease Research ◽  
Current State ◽  
Disease Registries ◽  
Medical Reports ◽  
Rare Disease Registry ◽  
New Feature

Web-based patient registries support clinicians by providing a way to effectively store and process data. Here, we present a new feature for the open-source registry software OSSE: medical reports generated with R Markdown. As part of a rare disease research project, we describe the process from requirements assessment to the current state of technical implementation. The feature offers clinicians the possibility to download customised as well as generic reports from an OSSE rare disease registry.

scholarly journals Validating Online Approaches for Rare Disease Research Using Latent Class Mixture Modeling.

2021 ◽  
Author(s):  
Andrew A. Dwyer ◽  
Ziwei Zeng ◽  
Christopher S. Lee
Keyword(s):  
Rare Disease ◽  
Latent Class ◽  
Depression Scale ◽  
Mixture Modeling ◽  
Class I ◽  
Depression Symptoms ◽  
Patient Organization ◽  
Web Based ◽  
Disease Research ◽  
Patient Organizations

Abstract Background: Rare disease patients are geographically dispersed, posing challenges to research. Some researchers have partnered with patient organizations and used web-based approaches to overcome geographic recruitment barriers. Critics of such methods claim that samples are homogenous and do not represent the broader patient population - as patients recruited from patient organizations are thought to have high levels of needs. We applied latent class mixture modeling (LCMM) to define patient clusters based on underlying characteristics. We used previously collected data from a cohort of patients with congenital hypogonadotropic hypogonadism (CHH) who were recruited online in collaboration with a patient organization. Patient demographics, clinical information, Revised Illness Perception Questionnaire (IPQ-R) scores and Zung self-rating depression Scale (SDS) were used as variables for LCMM analysis. Specifically, we aimed to test the classic critique that patients recruited online in collaboration with a patient organization are a homogenous group with high needs. We hypothesized that distinct classes (clinical profiles) of patients could be identified - thereby demonstrating the validity of online recruitment and supporting transferability of findings. Results: In total, 154 patients with CHH were included. The LCMM analysis identified three distinct subgroups (Class I: n=84 [54.5%], Class II: n=41 [26.6%], Class III: n=29 [18.8%]) that differed significantly in terms of age, education, disease consequences, emotional consequences, illness coherence and depression symptoms (all p<0.001) as well as age at diagnosis (p=0.045). Classes depict a continuum of psychosocial impact ranging from severe to relatively modest. Additional analyses revealed later diagnosis (Class I: 19.2±6.7 yrs. [95%CI: 17.8-20.7]) is significantly associated with worse psychological adaptation and coping as assessed by disease consequences, emotional responses, making sense of one’s illness and SDS depressive symptoms (all p<0.001). Conclusions: We identify three distinct classes of patients who were recruited online in collaboration with a patient organization. Findings refute prior critiques of patient partnership and web-based recruitment for rare disease research. This is the first empirical data suggesting negative psychosocial sequelae of later diagnosis (“diagnostic odyssey”) often observed in CHH.

scholarly journals A novel approach to conducting clinical trials in the community setting: Utilizing patient-driven platforms and social media to drive web-based patient recruitment

2019 ◽  
Author(s):  
Janelle Applequist ◽  
Cristina Burroughs ◽  
Artemio Ramirez ◽  
Peter A. Merkel ◽  
Marc E. Rothenberg ◽  
...  
Keyword(s):  
Social Media ◽  
Clinical Research ◽  
Rare Disease ◽  
Patient Recruitment ◽  
Research Approach ◽  
Marketing Theory ◽  
Disease Category ◽  
Web Based ◽  
Recruitment Methods ◽  
Research Studies

Abstract Background: Participant recruitment for clinical research studies remains a significant challenge for researchers. Novel approaches to recruitment are necessary to ensure that populations are easier to reach. In the context of rare diseases, social media provides a unique opportunity for connecting with patient groups that have representatively lower diagnosis rates when compared with more common maladies. We describe the implementation of designing a patient-centered approach to message design for the purposes of recruiting patients for clinical research studies for rare disease populations. Methods: Using an iterative research approach, we analyzed our previous experience of using web-based direct-to-patient recruitment methods to compare these online strategies with traditional center of excellence recruitment strategies. After choosing six research studies for inclusion in the previous study, in-depth, online interviews ( n = 37) were conducted with patients represented in each disease category to develop and test recruitment message strategies for social media and a Web-based platform for patients to access study information and pre-screen. Finally, relationships were established with Patient Advocacy Groups representing each rare disease category to ensure further dissemination of recruitment materials via their own social media networks. Results: Guided by social marketing theory, we created and tested various recruitment message designs. Three key message concepts preferred by patients emerged: (1) infographic; (2) positive emotional messages; and (3) educational information for sharing. A base study website that was created and edited based on qualitative user-testing. This website includes the option for potential participants to pre-screen and determine their eligibility for the study. Conclusions: Study participants report wanting to be involved in the design and implementation of recruitment approaches for clinical research studies. The application of the aforementioned methods could aide in the evolution of clinical research practices for the recruitment of both rare and common diseases, where patient-centric approaches can help to create targeted messages designs that participants pre-test and support.

scholarly journals A flexible computational pipeline for research analyses of unsolved clinical exome cases

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Timo Lassmann ◽  
Richard W. Francis ◽  
Alexia Weeks ◽  
Dave Tang ◽  
Sarra E. Jamieson ◽  
...  
Keyword(s):  
Open Access ◽  
Rare Disease ◽  
Exome Sequencing ◽  
Gene Variant ◽  
Computational Pipeline ◽  
Patient Report ◽  
Web Based ◽  
Pathogenic Variants ◽  
Flexible Framework ◽  
Disease Variant

AbstractExome sequencing has enabled molecular diagnoses for rare disease patients but often with initial diagnostic rates of ~25−30%. Here we develop a robust computational pipeline to rank variants for reassessment of unsolved rare disease patients. A comprehensive web-based patient report is generated in which all deleterious variants can be filtered by gene, variant characteristics, OMIM disease and Phenolyzer scores, and all are annotated with an ACMG classification and links to ClinVar. The pipeline ranked 21/34 previously diagnosed variants as top, with 26 in total ranked ≤7th, 3 ranked ≥13th; 5 failed the pipeline filters. Pathogenic/likely pathogenic variants by ACMG criteria were identified for 22/145 unsolved cases, and a previously undefined candidate disease variant for 27/145. This open access pipeline supports the partnership between clinical and research laboratories to improve the diagnosis of unsolved exomes. It provides a flexible framework for iterative developments to further improve diagnosis.

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