Early Detection of Fabry Disease

Study Overview
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Brief Summary
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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.

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

Conditions
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Fabry Disease

Intervention / Treatment
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* Other: No intervention


Participation Criteria
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Eligibility Criteria
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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.
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Retrospective Database Analysis<br>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<br>* This is non-interventional study.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Percentage of Participants Based on Ranking With Known FD Using Multilevel Likelihood Ratios For Algorithm Validation Purposes | 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. | Up to End of the study (approximately 6 months) |