డయాగ్నస్టిక్ పాథాలజీ: ఓపెన్ యాక్సెస్

నైరూప్య

Methodology for Generating Standardized Datasets with Characteristic Diagnostic Parameters of Rare Diseases in Form of HPO-Terms

Ann-Christin Liebers-Kyungay, Klaus Mohnike, Corine van Lingen, Anita Bressan, Cinzia Maria Bellettato, Maurizio Scarpa, Katja Palm, Athanasia Ziagaki

Background: Finding a diagnosis for rare diseases is a challenge for patients and those treating them. Establishing a uniform methodology for specifying the symptoms of a patient seems useful. This, as well as a database with clinical parameters reported in patients already diagnosed with the corresponding disease or that has led to the diagnosis, would facilitate the global data exchange between specialists and subsequently diagnosis. This work aims to introduce a methodology for generating data sets with characteristic diagnostic parameters of rare diseases using exemplarily the three rare metabolic diseases late-onset Pompe disease, Gaucher disease Type I and Smith-Lemli-Opitz syndrome. For these data sets, a standardized word form is to be chosen that enables European or even worldwide exchange.

Methods and results: A systematic literature review of characteristic symptoms and diagnostic criteria was performed for each of the three disorders. These parameters were converted into vocabulary standardized by The Human Phenotype Ontology (HPO), so-called HPO terms. Subsequently, a retrospective analysis of the patient files of 23 late-onset Pompe disease patients, 21 Gaucher disease Type I patients and 25 Smith-Lemli-Opitz syndrome patients was carried out together with the University Children's Hospital Magdeburg and the Center of excellence for Rare Metabolic Diseases at the Charité Berlin. Features present in ≥ 40% of the cohort and collected simultaneously in a certain minimum number of patients were filtered out. The analysis resulted in data sets with 22 diagnostic parameters for late-onset Pompe disease, 16 features for Gaucher disease Type I and 17 parameters for Smith- Lemli-Opitz syndrome. After the statistical evaluation, the results were discussed comparatively with similar studies.

Conclusion: Using the introduced methodology data sets with characteristic diagnostic criteria for three rare diseases could be established. The developed datasets provide a good basis for expansion with further patient examples and for extending the methodology to other diseases to improve the diagnostic pathway and thus the health care of patients with rare diseases.