Early Detection of Fabry Disease

NCT05106764 | Completed

• 2 other identifiers: TAK-675-4011-1FLAGGING FD-1
• Study Type: observational
• Target: 50
• Locations: 1 country
• Sites: 3

Brief Summary

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.

Conditions

Fabry Disease

Outcome Measures

Primary Outcomes (1)

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

  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.

  Up to End of the study (approximately 6 months)

Secondary Outcomes (1)

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

  Up to End of the study (approximately 6 months)

Study Arms (1)

Retrospective Database Analysis

Data from patient's hospital records of the last 10 years will be collected/extracted retrospectively using epidemiological methods to test the forecasting power of the algorithm.

Other: No intervention

Interventions

No intervention OTHER

This is non-interventional study.

Retrospective Database Analysis

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Data from all in-patient or out- patient datasets of the participating hospital in the last 10 years will be used to test the forecasting power of the algorithm.

You may qualify if:

• 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.

Sponsors & Collaborators

• Takeda: lead

Study Sites (3)

Universitätsklinikum Erlangen Kinder- und Jugendklinik

Erlangen, 91054, Germany

Location

Universitätsklinikum Erlangen Neurologische Klinik

Erlangen, 91054, Germany

Location

Universitätsklinikum Giessen

Giessen, 35389, Germany

Location

Related Links

• To obtain more information on the study, click here/on this link

MeSH Terms

Conditions

Fabry Disease

Condition Hierarchy (Ancestors)

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

Study Officials

• Study Director

  Takeda

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 25, 2021
• First Posted: November 4, 2021
• Study Start: November 30, 2023
• Primary Completion: December 16, 2025
• Study Completion: December 16, 2025
• Last Updated: March 16, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, CSR
• Access Criteria: IPD from eligible studies will be shared with qualified researchers according to the criteria and process described on https://vivli.org/ourmember/takeda/ For approved requests, the researchers will be provided access to anonymized data (to respect patient privacy in line with applicable laws and regulations) and with information necessary to address the research objectives under the terms of a data sharing agreement.

Locations

DE (3)