scholarly journals Is It Possible to Implement a Rare Disease Case-finding Tool in Primary Care? A UK-based Pilot Study

2021 ◽  
Author(s):  
Orlando Buendia ◽  
Sneha Shankar ◽  
Hadley Mahon ◽  
Connor Toal ◽  
Lara Menzies ◽  
...  
Keyword(s):  
Primary Care ◽  
Rare Disease ◽  
Rare Diseases ◽  
Population Level ◽  
Disease Diagnosis ◽  
Case Finding ◽  
Snomed Ct ◽  
Efficient Manner ◽  
Screening Criteria ◽  
Disease Case

Abstract Introduction:This study implemented MendelScan, a primary care rare disease case-finding tool, into a UK NHS population. Rare disease diagnosis is challenging due to disease complexity and low physician awareness. The 2021 UK Rare Diseases Framework highlights a global need for faster diagnosis to improve clinical outcomes as a key priority.Methods & Results:A UK primary care locality with 68,705 patients was examined. MendelScan encodes diagnostic/screening criteria for multiple rare diseases, mapping clinical terms to appropriate SNOMED CT codes (UK primary care standardised clinical terminology) to create digital algorithms. These algorithms were applied to a pseudo-anonymised structured data extract of the electronic health records (EHR) in this locality to "flag" at-risk patients who may require further evaluation. All flagged patients then underwent internal clinical review; for those that passed this review, a report was returned to their GP. 55 of 76 disease criteria flagged at least one patient. 227 (0.33% of the total population) patients were flagged; 18 EHR were already diagnosed with the disease. 75/227 (33%) passed our internal review. Thirty-six reports were returned to the GP. Feedback was available for 28/36 of the reports sent. GP categorised nine reports as "Reasonable possible diagnosis" (advance for investigation), six reports as "diagnosis has already been excluded", ten reports as "patient has a clear alternative aetiology", and three reports as "Other" (patient left study locality, unable to reidentify accurately). All the 9 cases considered as "reasonable possible diagnosis" had a further actionable evaluation.Conclusions:This pilot demonstrates that implementing such a tool is feasible at a population level in an ethical, technical and efficient manner. The case-finding tool identified credible cases which were subsequently referred for further investigation. Future work includes performance-based validation studies of diagnostic algorithms and the scalability of the tool.

scholarly journals Is it Possible to Implement a Rare Disease Case-Finding Tool in Primary Care? A UK-Based Pilot Study

2021 ◽  
Author(s):  
Orlando Buendia ◽  
Sneha Shankar ◽  
Hadley Mahon ◽  
Connor Toal ◽  
Lara Menzies ◽  
...  
Keyword(s):  
Primary Care ◽  
Rare Disease ◽  
Behçet’S Disease ◽  
Rare Diseases ◽  
Behcet’S Disease ◽  
Behçet's Disease ◽  
Case Finding ◽  
Snomed Ct ◽  
Behcet's Disease ◽  
Disease Case

Abstract Introduction:This study implemented MendelScan, a primary care rare disease case-finding tool, into a UK National Health Service (NHS) population. Rare disease diagnosis is challenging due to disease complexity and low physician awareness. The 2021 UK Rare Diseases Framework highlights a global need for faster diagnosis to improve clinical outcomes as a key priority.Methods & Results:A UK primary care locality with 68,705 patients was examined. MendelScan encodes diagnostic/screening criteria for multiple rare diseases, mapping clinical terms to appropriate SNOMED CT codes (UK primary care standardised clinical terminology) to create digital algorithms. These algorithms were applied to a pseudo-anonymised structured data extract of the electronic health records (EHR) in this locality to "flag" at-risk patients who may require further evaluation. All flagged patients then underwent internal clinical review (a doctor reviewing each EHR flagged by the algorithm, removing all cases with a clear diagnosis that explains the clinical features that led to the patient being flagged); for those that passed this review, a report was returned to their GP. 55 of 76 disease criteria flagged at least one patient. 227 (0.33%) of the total 68,705 of EHR were flagged; 18 EHR were already diagnosed with the disease (The highlighted EHR has a diagnostic code for the same RD it was screened for. e.g Behcet’s disease algorithm identifying an EHR with a SNOMED CT code Behcet's disease). 75/227 (33%) EHR passed our internal review. Thirty-six reports were returned to the GP. Feedback was available for 28/36 of the reports sent. GP categorised nine reports as "Reasonable possible diagnosis" (advance for investigation), six reports as "diagnosis has already been excluded", ten reports as "patient has a clear alternative aetiology", and three reports as "Other" (patient left study locality, unable to re identify accurately). All the 9 cases considered as "reasonable possible diagnosis" had a further actionable evaluation.Conclusions:This pilot demonstrates that implementing such a tool is feasible at a population level. The case-finding tool identified credible cases which were subsequently referred for further investigation. Future work includes performance-based validation studies of diagnostic algorithms and the scalability of the tool.

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

2021 ◽  
Vol 16 (1) ◽  
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 Open-Source Registry System for Rare Diseases (OSSE) 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 undiagnosed patients.

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

2020 ◽  
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 is needed. We 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 for 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 Potentials and current shortcomings in the cooperation between German centers for rare diseases and primary care physicians: results from the project TRANSLATE-NAMSE

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
D. Druschke ◽  
F. Krause ◽  
G. Müller ◽  
J. Scharfe ◽  
G. F. Hoffmann ◽  
...  
Keyword(s):  
Primary Care ◽  
Rare Disease ◽  
Rare Diseases ◽  
Primary Care Physicians ◽  
Postal Survey ◽  
National Plan ◽  
Major Step ◽  
Interface Management ◽  
Telephone Interviews ◽  
Center For Rare Diseases

Abstract Background The TRANSLATE-NAMSE project with the strengthening of the centers for rare diseases with their affiliation to the European Reference Networks was a major step towards the implementation of the German National Plan of Action for People with Rare Diseases establishing better care structures. As primary care physicians, general practitioners and pediatricians play a central role in the diagnosis of patients with rare disease, as it is usually them referring to specialists and rare disease centers. Therefore, the interface management between primary care physicians and the centers for rare diseases is of particular importance. Methods In a mixed-method-approach an anonymous postal survey of 1,500 randomly selected primary care physicians in Germany was conducted with focus on (1) knowledge about a center for rare diseases and how it works, (2) in case of cooperation, satisfaction with the services provided by centers, and (3) expectations and needs they have with regard to the centers. In addition, in-depth telephone interviews were conducted with physicians who had already referred patients to a center. Results In total, 248 physicians responded to the survey, and 15 primary care physicians were interviewed. We observed a wide lack of knowledge about the existence of (45.6% confirmed to know at least one center) about how to access rare disease centers (50.4% of those who know a center confirmed knowledge) and what the center specializes in. In case of cooperation the evaluation was mostly positive. Conclusion To improve medical care, the interplay between primary care physicians and rare disease centers needs to be strengthened. (1) To improve the communication, the objectives and functioning of the rare disease centers should become more visible. (2) Other projects dealing with the analysis and improvement of interface management between centers and primary care physicians, as described in the National Plan of Action for People with Rare Diseases, need to be implemented immediately. (3) If the project is evaluated positively, the structures of TRANSLATE-NAMSE should be introduced nationwide into the German health care system to ensure comprehensive, quality-assured care for people with rare diseases with special consideration of the key role of primary care physicians—also taking into account the financial expenditures of this new care model.

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

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Kimberly LeBlanc ◽  
◽  
Emily G. Kelley ◽  
Anna Nagy ◽  
Jorick Bater ◽  
...  
Keyword(s):  
Rare Disease ◽  
General Public ◽  
Disease Patient ◽  
Disease Diagnosis ◽  
Case Finding ◽  
Web Pages ◽  
Patient Resources ◽  
Undiagnosed Diseases ◽  
Undiagnosed Diseases Network ◽  
Rare Disease Patient

Abstract 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.

EXPLORING VALUES OF HEALTH TECHNOLOGY ASSESSMENT AGENCIES USING REFLECTIVE MULTICRITERIA AND RARE DISEASE CASE

2017 ◽  
Vol 33 (4) ◽  
pp. 504-520 ◽  
Author(s):  
Mireille M. Goetghebeur ◽  
Monika Wagner ◽  
Dima Samaha ◽  
William O'Neil ◽  
Danielle Badgley ◽  
...  
Keyword(s):  
Health Technology Assessment ◽  
Rare Disease ◽  
Rare Diseases ◽  
Technology Assessment ◽  
Ethical Reasoning ◽  
Stakeholder Involvement ◽  
Health Technology ◽  
Trade Offs ◽  
Disease Case ◽  
The Impact

Objectives: Tackling ethical dilemmas faced by reimbursement decision makers requires deeper understanding of values on which health technology assessment (HTA) agencies are founded and how trade-offs are made. This was explored in this study including the case of rare disease.Methods: Representatives from eight HTA explored values on which institutions are founded using a narrative approach and reflective multicriteria (developed from EVIDEM, criteria derived from ethical imperatives of health care). Trade-offs between criteria and the impact of incorporating defined priorities (including for rare diseases) were explored through a quantitative values elicitation exercise.Results: Participants reported a diversity of substantive and procedural values with a common emphasis on scientific excellence, stakeholder involvement, independence, and transparency. Examining the ethical imperatives behind EVIDEM criteria was found to be useful to further explore substantive values. Most criteria were deemed to reflect institutions’ values, while 70 percent of the criteria were reported by at least half of participants to be considered formally by their institutions. The quantitative values elicitation highlighted the difficulty to balance imperatives of “alleviating or preventing patient suffering,” “serving the whole population equitably,” “upholding healthcare system sustainability,” and “making decisions informed by evidence and context” but may help share the ethical reasoning behind decisions. Incorporating “Priorities” (including for rare diseases) helped reveal trade-offs from other criteria and their underlying ethical imperatives.Conclusions: Reflective multicriteria are useful to explore substantive values of HTAs, reflect how these values and their ethical underpinnings can be operationalized into criteria, and explore the ethical reasoning at the heart of the healthcare debate.

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.

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