Early Detection of Fabry Disease

The main aim of this study is early detection of FD using real-world data for the development of advanced natural language processing methods and to develop a predictive algorithm and to measure the performance of the algorithm in identifying participants with FD.

This study is about using data from hospital Electronic Health Record database from the last 10 years to describe the ranking of participants with FD using multilevel likelihood ratios and to validate the algorithm using positive controls. No investigational medicinal product or device will be tested in this study. Hospital electronic health record data will be analyzed for a period of up to 6 months.

Completed

Looking for future studies?

Notify Me

Key information

Conditions

Fabry Disease Brain Diseases Brain Diseases, Metabolic Brain Diseases, Metabolic, Inborn Cardiovascular Diseases Central Nervous System Diseases Cerebral Small Vessel Diseases Cerebrovascular Disorders Congenital, Hereditary, and Neonatal Diseases and Abnormalities Genetic Diseases, Inborn Genetic Diseases, X-Linked Lipid Metabolism Disorders Lipid Metabolism, Inborn Errors Lipidoses Lysosomal Storage Diseases Lysosomal Storage Diseases, Nervous System Metabolic Diseases Metabolism, Inborn Errors Nervous System Diseases Nutritional and Metabolic Diseases Sphingolipidoses Vascular Diseases

Sex eligibility

All sexes

Study type

Observational

Primary location

Universitätsklinikum Erlangen Kinder- und Jugendklinik, Erlangen, Germany

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

In-patient or out-patient datasets of the participating hospital in the last 10 years
Participants at any age Positive controls: a subset of all participant hospital records that includes the participants with confirmed FD.

Treatment and study plan

No intervention

Other

This is non-interventional study.

Primary outcomes

Percentage of Participants With Positive Predictive Value (PPV) at Different Cut-off Values (top 10, 20, 50, 100 and 200)

Time frame: Up to End of the study (approximately 6 months)

PPV is a clinically relevant statistical measure that indicates how likely participants that screen positive are to be affected by the condition assessed. Thus, the PPV can be considered as the percentage of participants which are identified as FD candidates by the ranking algorithm who are indeed FD participants. As FD predictive algorithm, we will use (multilevel) likelihood ratios (LRs) as this method permits a good use of clinical test results to establish diagnoses for the individual participant. LR is calculated, defined as the probability of a patient who has FD to present with this feature divided by the probability of a participant who not has FD to present with the feature: Likelihood ratio= features the participant/Fabry divided by features the participant/not Fabry. Positive predictive value of the algorithm at several cutoffs (top 10, top 20, top 50, top 100, top 200) will be reported.

Secondary outcomes

Percentage of Participants Based on Ranking With Known FD Using Multilevel Likelihood Ratios For Algorithm Validation Purposes

Time frame: Up to End of the study (approximately 6 months)

To validate the algorithm, records of participants with a high predictive value are reexamined by medical experts who assess the likelihood of FD based on the participant records. Percentage of participants based on ranking with known FD using multilevel likelihood ratios for algorithm validation purpose will be reported. Likelihood ratio= features the participant/Fabry divided by features the participant/not Fabry.

Sponsors and collaborators

Lead sponsor

Takeda

Industry

Registry information

Official study title

Early Detection of Fabry Disease (FD): Using Real-World Data for the Development of Advanced Natural Language Processing Methods: Retrospective Database Analysis

Important dates

Study start: 2023
Primary completion: 2025
Study completion: 2025
First posted: Nov 4, 2021
Registry last updated: Mar 16, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed