RARASnet: Mapping, infrastructure and data analysis for the Brazilian Network of Rare Diseases - a Study Protocol (Preprint)

2020 ◽  
Author(s):  
Domingos Alves ◽  
Diego Bettiol Yamada ◽  
Filipe Andrade Bernardi ◽  
Isabelle Carvalho ◽  
Márcio Eloi ◽  
...  
Keyword(s):  
Data Analysis ◽  
Rare Disease ◽  
Rare Diseases ◽  
Health Professionals ◽  
Medical Condition ◽  
Digital Health ◽  
Health Indicators ◽  
Disease Diagnosis ◽  
World Health ◽  
Network Member

BACKGROUND A rare disease is a medical condition with low prevalence in the general population, but that collectively can affect up to 10% of the population. Thus, rare diseases have a significant impact on the healthcare system, and health professionals must be familiar with the diagnosis, management, and treatment. OBJECTIVE To provide health indicators regarding the rare diseases in Brazil, and to create a network of reference centers with health professionals from different regions of the country, the RARASnet proposes to map, analyze and communicate all the data regarding the infrastructure of the centers, and the patient's evolution or needs. The focus of the proposed study is to provide all the technical infrastructure and analysis following the World Health Organization and the Brazilian Ministry of Health guidelines. METHODS To build this digitized system, we will provide a security framework to assure the privacy and protection of each patient when collecting data. Also, DevOps methodologies will be applied to align software development, infrastructure operation, and quality assurance. After data collection of all information designed by specialists, the computational analysis, modeling, and results will be communicated in scientific research papers and in a digital health observatory. RESULTS The project has several activities and it is in an initial stage. Initially, a survey was applied to all health care centers to understand the technical aspects of each network member such as the existence of computers,technical support staff, and digitized systems. In this survey, we detected that 64% of participating health units have electronic medical records, while 36% have paper records. Therefore, we will have different strategies to access the data from each center. CONCLUSIONS The nature of rare disease diagnosis is complex and diverse, and many problems will be faced in the evolution of the project. However, decisions based on data analysis are the best option for the improvement of the rare disease network in Brazil. The creation of the RARASnet, along with all the digitized infrastructure, can improve the accessibility of information and standardization of the rare diseases in the country.

Facebook support groups for pediatric rare diseases: opportunities, limitations and privacy concerns (Preprint)

2021 ◽  
Author(s):  
Sarah Catrin Titgemeyer ◽  
Christian Patrick Schaaf
Keyword(s):  
Rare Disease ◽  
Support Groups ◽  
Support Group ◽  
Rare Diseases ◽  
Health Professionals ◽  
Online Survey ◽  
Privacy Rights ◽  
Cross Sectional ◽  
Privacy Concerns ◽  
Group Members

BACKGROUND Due to the nature of rare diseases with affected individuals being widely geographically dispersed, finding an in-person/offline support group itself can be a challenge. Affected individuals therefore turn to social networking platforms such as Facebook for online support groups. OBJECTIVE We aim to put into perspective the opportunities Facebook offers as a tool for pediatric rare disease support groups by investigating its use, advantages and limitations including privacy concerns. We analyze group accessibility and usage, advantages specific to rare diseases, perceived privacy and views on using Facebook for communication between health professionals and parents, pharmaceutical companies and for study recruitment. METHODS We contacted twelve Facebook support groups for twelve respective rare diseases with pediatric onset and invited group members to participate in a cross-sectional online survey. RESULTS Of 231 respondents, 87.0% (n=201) respondents were female, 12.6% (n=29) were male and 1 respondent reported another sex (0.4%, n=1). Respondents’ mean age was 41.56 years (SEM=0.621, SD=9.375). 91.3% (n=211) respondents were parents (183 mothers, 27 fathers, 1 other sex). 59.7% (n=138) reported a self-initiated search for the Facebook group, 24.2% (n=56) received recommendations from their health professionals, 12.6% (n=29) recommendations from someone else affected by the disease. On average, support group members visited Facebook at least once a day, visited and passively participated (read/liked posts) several times a week and participated actively (commented/posted) once a month. 79,6% agreed that they would like to have health professionals as members of the respective Facebook group. Group members expressed more concern about privacy issues on Facebook in general than in their respective Facebook support groups, with concerns mostly related to Facebook itself and non-group-members. CONCLUSIONS Our study confirmed that Facebook enhances support group accessibility for parents of children with rare diseases. Group participants perceive a reduction and elimination of distance, a common challenge in rare disease, and Facebook support groups create an environment of perceived privacy. The group’s privacy setting can be a critical factor for active support group participation. Sharing personal information and pictures on Facebook is very common among group participants, which shows the importance of discussing and protecting children’s privacy rights in this context. CLINICALTRIAL DRKS00016067

scholarly journals Utilization of Electronic Medical Records and Biomedical Literature to Support Rare Disease Diagnosis (Preprint)

2018 ◽  
Author(s):  
Feichen Shen ◽  
Sijia Liu ◽  
Yanshan Wang ◽  
Andrew Wen ◽  
Liwei Wang ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Rare Disease ◽  
Electronic Medical Records ◽  
Likelihood Ratio ◽  
Rare Diseases ◽  
Medical Records ◽  
Disease Diagnosis ◽  
Heterogeneous Datasets ◽  
Log Likelihood ◽  
Log Likelihood Ratio

BACKGROUND In the United States, rare diseases are defined as those affecting fewer than 200,000 patients at any given time. Patients with rare diseases are frequently either misdiagnosed or left undiagnosed, possibly due in part to a lack of knowledge or experience with the rare disease on the part of care providers. With an exponentially growing volume of electronically accessible medical data, a large volume of information on thousands of rare diseases and their potentially associated diagnostic information is buried in electronic medical records (EMRs) and medical literature. OBJECTIVE We hypothesize that patients’ phenotypic information available within these heterogeneous resources (e.g., electronic medical records and biomedical literature) can be leveraged to accelerate disease diagnosis. In this study, we aimed to leverage information contained in heterogeneous datasets to assist rare disease diagnosis. METHODS In a previous study, we proposed utilizing a collaborative filtering recommendation system enriched with natural language processing and semantic techniques to assist rare disease diagnosis based on phenotypic characterizations derived solely from EMR data. In this study, in order to further investigate the performance of collaborative filtering on heterogeneous datasets, we studied EMR data generated at Mayo Clinic as well as published article abstracts retrieved from the Semantic MEDLINE Database. Specifically, in this study, we applied Tanimoto coefficient similarity, overlap coefficient similarity, Fager & McGowan coefficient similarity, and log likelihood ratio similarity with K nearest neighbor and threshold based patient neighbor algorithms on various combinations of datasets. RESULTS We evaluated different approaches to this problem using characterizations derived from various combinations of EMR data and literature, as well as with solely EMR data. We extracted 12.8 million EMRs from the Mayo Clinic unstructured patient cohort generated between 2010 through 2015 and retrieved all article abstracts from the semi-structured Semantic MEDLINE Database that were published through the end of 2016. We applied a collaborative filtering model and compared the performance generated by different metrics. Log likelihood ratio similarity combined with K nearest neighbor on heterogeneous datasets showed the optimal performance in patient recommendation with PRAUC 0.475 (string match), 0.511 (SNOMED match), and 0.752 (GARD match). Log likelihood ratio similarity also performed the best with mean average precision 0.465 (string match), 0.5 (SNOMED match), and 0.749 (GARD match). Performance of rare disease prediction was also demonstrated by using the optimal algorithm. Macro-average F-measure for string, SNOMED-CT, and GARD match were 0.32, 0.42, and 0.63, respectively. CONCLUSIONS This study demonstrated potential utilization of heterogeneous datasets in a collaborative filtering model to support rare disease diagnosis. In addition to phenotypic-based analysis, in the future, we plan to resolve the heterogeneity issue and reduce miscommunication between EMR and literature by mining genotypic information to establish a comprehensive disease-phenotype-gene network for rare disease diagnosis.

scholarly journals RDmap: A Map for Exploring Rare Diseases

2021 ◽  
Author(s):  
Jian Yang ◽  
Cong Dong ◽  
Huilong Duan ◽  
Qiang Shu ◽  
Haomin Li
Keyword(s):  
Rare Disease ◽  
Rare Diseases ◽  
Genetic Diseases ◽  
Distance Matrix ◽  
Disease Diagnosis ◽  
Human Phenotype ◽  
Pathogenic Genes ◽  
Disease Information ◽  
Rare Genetic Diseases ◽  
Interactive Map

Abstract Background: The complexity of the phenotypic characteristics and molecular bases of many rare human genetic diseases makes the diagnosis of such diseases a challenge for clinicians. A map for visualizing, locating and navigating rare diseases based on similarity will help clinicians and researchers understand and easily explore these diseases. Methods: A distance matrix of rare diseases included in Orphanet was measured by calculating the quantitative distance among phenotypes and pathogenic genes based on Human Phenotype Ontology (HPO) and Gene Ontology (GO), and each disease was mapped into Euclidean space. A rare disease map, enhanced by clustering classes and disease information, was developed based on ECharts. Results: A rare disease map called RDmap was published at http://rdmap.nbscn.org. Total 3,287 rare diseases are included in the phenotype-based map, and 3,789 rare genetic diseases are included in the gene-based map; 1,718 overlapping diseases are connected between two maps. RDmap works similarly to the widely used Google Map service and supports zooming and panning. The phenotype similarity base disease location function performed better than traditional keyword searches in an in silico evaluation, and 20 published cases of rare diseases also demonstrated that RDmap can assist clinicians in seeking the rare disease diagnosis. Conclusion: RDmap is the first user-interactive map-style rare disease knowledgebase. It will help clinicians and researchers explore the increasingly complicated realm of rare genetic diseases.

scholarly journals RDmap: a map for exploring rare diseases

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Jian Yang ◽  
Cong Dong ◽  
Huilong Duan ◽  
Qiang Shu ◽  
Haomin Li
Keyword(s):  
Rare Disease ◽  
Rare Diseases ◽  
Genetic Diseases ◽  
Distance Matrix ◽  
Disease Diagnosis ◽  
Human Phenotype ◽  
Pathogenic Genes ◽  
Disease Information ◽  
Rare Genetic Diseases ◽  
Interactive Map

Abstract Background The complexity of the phenotypic characteristics and molecular bases of many rare human genetic diseases makes the diagnosis of such diseases a challenge for clinicians. A map for visualizing, locating and navigating rare diseases based on similarity will help clinicians and researchers understand and easily explore these diseases. Methods A distance matrix of rare diseases included in Orphanet was measured by calculating the quantitative distance among phenotypes and pathogenic genes based on Human Phenotype Ontology (HPO) and Gene Ontology (GO), and each disease was mapped into Euclidean space. A rare disease map, enhanced by clustering classes and disease information, was developed based on ECharts. Results A rare disease map called RDmap was published at http://rdmap.nbscn.org. Total 3287 rare diseases are included in the phenotype-based map, and 3789 rare genetic diseases are included in the gene-based map; 1718 overlapping diseases are connected between two maps. RDmap works similarly to the widely used Google Map service and supports zooming and panning. The phenotype similarity base disease location function performed better than traditional keyword searches in an in silico evaluation, and 20 published cases of rare diseases also demonstrated that RDmap can assist clinicians in seeking the rare disease diagnosis. Conclusion RDmap is the first user-interactive map-style rare disease knowledgebase. It will help clinicians and researchers explore the increasingly complicated realm of rare genetic diseases.

scholarly journals Digit-all: Rare Diseases

2020 ◽  
Keyword(s):  
Public Health ◽  
Health System ◽  
Rare Disease ◽  
Rare Diseases ◽  
Care Coordination ◽  
Digital Health ◽  
Global Public Health ◽  
Public Health Priority ◽  
Health Initiatives ◽  
Public Health Challenges

Rare diseases are increasingly recognised as a global public health priority and contribute to significant and disproportionately high health system impacts. Accordingly, they present clinical and public health challenges, as well as opportunities for digital health solutions across the lifespan, including improved diagnosis, treatment, navigation and care coordination, and integration and coordination for broader societal and patient wellbeing. People living with rare diseases, individually and cumulatively, are digital disruptors. In this manuscript the authors describe some of the unique dynamics of the rare disease domain as they currently, or have the potential to in the future, apply to digital health; highlight some recent international rare diseases digital health initiatives; and touch upon implications for those with more common disorders.

scholarly journals How to design a registry for undiagnosed patients in the framework of rare disease diagnosis – suggestions on software, data set and coding system

2021 ◽  
Author(s):  
Alexandra Berger ◽  
Anne-Kathrin Rustemeier ◽  
Jens Göbel ◽  
Dennis Kadioglu ◽  
Vanessa Britz ◽  
...  
Keyword(s):  
Open Source ◽  
Rare Disease ◽  
Rare Diseases ◽  
Action Plan ◽  
Correct Diagnosis ◽  
Disease Patient ◽  
Disease Diagnosis ◽  
Clinical Findings ◽  
Snomed Ct ◽  
Data Set

Abstract Background: About 30 million people in the EU and USA, respectively, suffer from a rare disease. Driven by European legislative requirements, national strategies for the improvement of care in rare diseases are being developed. To improve timely and correct diagnosis for patients with rare diseases, the development of a registry for undiagnosed patients was recommended by the German National Action Plan. In this paper we focus on the question on how such a registry for undiagnosed patients can be built and which information it should contain.Results: To develop a registry for undiagnosed patients, a software for data acquisition and storage, an appropriate data set and an applicable terminology/classification system for the data collected are needed. We have used the open-source software OSSE (Open-Source Registry System for Rare Diseases) to build the registry for undiagnosed patients. Our data set is based on the minimal data set for rare disease patient registries recommended by the European Rare Disease Registries Platform. We extended this Common Data Set to also include symptoms, clinical findings and other diagnoses. In order to ensure findability, comparability and statistical analysis, symptoms, clinical findings and diagnoses have to be encoded. We evaluated three medical ontologies (SNOMED CT, HPO and LOINC) for their usefulness. With exact matches of 98% of tested medical terms, a mean number of five deposited synonyms, SNOMED CT seemed to fit our needs best. HPO and LOINC provided 73% and 31% of exacts matches of clinical terms respectively. Allowing more generic codes for a defined symptom, with SNOMED CT 99%, with HPO 89% and with LOINC 39% of terms could be encoded.Conclusions: With the use of the OSSE software and a data set, which, in addition to the Common Data Set, focuses on symptoms and clinical findings, a functioning and meaningful registry for undiagnosed patients can be implemented. The next step is the implementation of the registry in centres for rare diseases. With the help of medical informatics and big data analysis, case similarity analyses could be realized and aid as a decision-support tool enabling diagnosis of some patients.

scholarly journals Digital Education of Health Professionals on the Management of Domestic Violence: Systematic Review and Meta-Analysis by the Digital Health Education Collaboration (Preprint)

2019 ◽  
Author(s):  
Ushashree Divakar ◽  
Nuraini Nazeha ◽  
Pawel Posadzki ◽  
Krister Jarbrink ◽  
Ram Bajpai ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Health Professionals ◽  
Digital Health ◽  
Meta Analysis ◽  
World Health ◽  
Health Concern ◽  
Digital Education ◽  
Randomized Controlled ◽  
Education Interventions ◽  
Health Organization

BACKGROUND The World Health Organization states that 35% of women experience domestic violence at least once during their lifetimes. However, approximately 80% of health professionals have never received any training on management of this major public health concern. OBJECTIVE The objective of this study was to evaluate the effectiveness of health professions digital education on domestic violence compared to that of traditional ways or no intervention. METHODS Seven electronic databases were searched for randomized controlled trials from January 1990 to August 2017. The Cochrane Handbook guideline was followed, and studies reporting the use of digital education interventions to educate health professionals on domestic violence management were included. RESULTS Six studies with 631 participants met our inclusion criteria. Meta-analysis of 5 studies showed that as compared to control conditions, digital education may improve knowledge (510 participants and 5 studies; standardized mean difference [SMD] 0.67, 95% CI 0.38-0.95; I2=59%; low certainty evidence), attitudes (339 participants and 3 studies; SMD 0.67, 95% CI 0.25-1.09; I2=68%; low certainty evidence), and self-efficacy (174 participants and 3 studies; SMD 0.47, 95% CI 0.16-0.77; I2=0%; moderate certainty evidence). CONCLUSIONS Evidence of the effectiveness of digital education on health professionals’ understanding of domestic violence is promising. However, the certainty of the evidence is predominantly low and merits further research. Given the opportunity of scaled transformative digital education, both further research and implementation within an evaluative context should be prioritized.

Gestational Gigantomastia: A Rarity

2016 ◽  
Vol 1 (1) ◽  
pp. 13-14
Author(s):  
Lata Agrawal ◽  
HK Premi ◽  
Disha Shakya ◽  
Tripti Gupta
Keyword(s):  
Connective Tissue ◽  
Rare Disease ◽  
Rare Diseases ◽  
Medical Condition ◽  
Antenatal Clinic

ABSTRACT Hypertrophy of the breast is a rare medical condition of breast connective tissue. It is listed as a ‘rare disease’ by the ‘Office of Rare Diseases’ of National Institute of Health (NIH). A woman aged 20 years presented to our antenatal clinic in her first pregnancy at 23 weeks of gestation with excessive enlargement of breasts since conception. Although its etiology has yet to be clarified, it has been associated with the response of breast receptors to gestational hormones. Gestational gigantomastia is a complication whose etiology and pathogenesis have yet to be fully clarified. However it has been speculated that placental hormones may trigger the condition. How to cite this article Shakya D, Gupta T, Agrawal L, Premi HK. Gestational Gigantomastia: A Rarity. Int J Adv Integ Med Sci 2016;1(1):13-14.

scholarly journals The frequency of rare and monogenic diseases in pediatric organ transplant recipients in Italy

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Tiziana Vaisitti ◽  
Daniela Peritore ◽  
Paola Magistroni ◽  
Andrea Ricci ◽  
Letizia Lombardini ◽  
...  
Keyword(s):  
Rare Disease ◽  
Organ Failure ◽  
Rare Diseases ◽  
Clinical Symptoms ◽  
Specific Treatment ◽  
Disease Diagnosis ◽  
Definitive Treatment ◽  
Transplant Recipients ◽  
Monogenic Diseases ◽  
Lung Transplants

Abstract Background Rare diseases are chronic and life-threatening disorders affecting < 1 person every 2,000. For most of them, clinical symptoms and signs can be observed at birth or childhood. Approximately 80% of all rare diseases have a genetic background and most of them are monogenic conditions. In addition, while the majority of these diseases is still incurable, early diagnosis and specific treatment can improve patients’ quality of life. Transplantation is among the therapeutic options and represents the definitive treatment for end-stage organ failure, both in children and adults. The aim of this paper was to analyze, in a large cohort of Italian patients, the main rare genetic diseases that led to organ transplantation, specifically pointing the attention on the pediatric cohort. Results To the purpose of our analysis, we considered heart, lung, liver and kidney transplants included in the Transplant Registry (TR) of the Italian National Transplantation Center in the 2002–2019 timeframe. Overall, 49,404 recipients were enrolled in the cohort, 5.1% of whom in the pediatric age. For 40,909 (82.8%) transplant recipients, a disease diagnosis was available, of which 38,615 in the adult cohort, while 8,495 patients (17.2%) were undiagnosed. There were 128 disease categories, and of these, 117 were listed in the main rare disease databases. In the pediatric cohort, 2,294 (5.6%) patients had a disease diagnosis: of the 2,126 (92.7%) patients affected by a rare disease, 1,402 (61.1%) presented with a monogenic condition. As expected, the frequencies of pathologies leading to organ failure were different between the pediatric and the adult cohort. Moreover, the pediatric group was characterized, compared to the adult one, by an overall better survival of the graft at ten years after transplant, with the only exception of lung transplants. When comparing survival considering rare vs non-rare diseases or rare and monogenic vs rare non-monogenic conditions, no differences were highlighted for kidney and lung transplants, while rare diseases had a better survival in liver as opposed to heart transplants. Conclusions This work represents the first national survey analyzing the main genetic causes and frequencies of rare and/or monogenic diseases leading to organ failure and requiring transplantation both in adults and children.

scholarly journals Improvements needed to support people living and working with a rare disease in Northern Ireland: current rare disease support perceived as inadequate

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Julie McMullan ◽  
Ashleen L. Crowe ◽  
Caitlin Bailie ◽  
Kerry Moore ◽  
Lauren S. McMullan ◽  
...  
Keyword(s):  
Northern Ireland ◽  
Rare Disease ◽  
Rare Diseases ◽  
Health Professionals ◽  
Online Survey ◽  
Essential Element ◽  
Future Research ◽  
Structured Interviews ◽  
Collaborative Groups ◽  
Related Information

Abstract Background Many people living and working with rare diseases describe consistent difficulties accessing appropriate information and support. In this study an evaluation of the awareness of rare diseases, alongside related information and educational resources available for patients, their families and healthcare professionals, was conducted in 2018–2019 using an online survey and semi-structured interviews with rare disease collaborative groups (charities, voluntary and community groups) active across Northern Ireland (NI). Methods This study had 2 stages. Stage 1 was an online survey and stage 2 involved semi-structured interviews both with rare disease collaborative groups in Northern Ireland. The surveys and interviews were used to locate existing resources as well as identify gaps where the development of further resources would be appropriate. Results Ninety-nine rare disease collaborative groups engaged with the survey with 31 providing detailed answers. Resources such as information, communication, ‘registries’, online services, training and improvements to support services were queried. Excellent communication is an important factor in delivering good rare disease support. Training for health professionals was also highlighted as an essential element of improving support for those with a rare disease to ensure they approach people with these unique and challenging diseases in an appropriate way. Carers were mentioned several times throughout the study; it is often felt they are overlooked in rare disease research and more support should be in place for them. Current care/support for those with a rare disease was highlighted as inadequate. Nine semi-structured interviews were conducted with rare disease collaborative groups. Reoccurring themes included a need for more effective information and communication, training for health professionals, online presence, support for carers, and involvement in research. Conclusions All rare disease collaborative groups agreed that current services for people living and working with a rare disease are not adequate. An important finding to consider in future research within the rare disease field is the inclusion of carers perceptions and experiences in studies. This research provides insight into the support available for rare diseases across Northern Ireland, highlights unmet needs, and suggests approaches to improve rare disease support.

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