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A new approach to identifying patients with elevated risk for Fabry disease using a machine learning algorithm.

John L Jefferies1, Alison K Spencer2, Heather A Lau3

  • 1Division of Cardiovascular Diseases, University of Tennessee Health Science Center, Memphis, TN, USA.

Orphanet Journal of Rare Diseases
|December 21, 2021
PubMed
Summary

An artificial intelligence (AI) tool effectively identifies patients with Fabry disease (FD) by analyzing health records. This AI approach shows promise in addressing the significant underdiagnosis of this rare genetic condition.

Keywords:
AIFabry diseasePatient identificationPhenotypic biomarker

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Area of Science:

  • Genetics
  • Medical Informatics
  • Rare Diseases

Background:

  • Fabry disease (FD) is a rare genetic disorder causing progressive organ damage due to glycosphingolipid accumulation.
  • Heterogeneous and nonspecific symptoms contribute to significant underdiagnosis of FD.
  • Existing diagnostic improvement strategies like provider education and newborn screening have had limited success.

Purpose of the Study:

  • To evaluate the performance of an artificial intelligence (AI) tool in identifying patients with Fabry disease.
  • To assess the AI tool's ability to detect patterns in longitudinal health records for improved FD diagnosis.

Main Methods:

  • The AI tool was calibrated using de-identified health records from nearly 5000 FD patients.
  • Phenotypic patterns associated with FD were extracted and used to estimate individual risk in a testing dataset.
  • AI predictions and identified phenotypic patterns were reviewed and confirmed by medical experts.

Main Results:

  • The AI tool achieved a strong AUROC of 0.82 in identifying FD patients in out-of-sample testing.
  • High performance was maintained in male-only (AUROC 0.83) and female-only (AUROC 0.82) cohorts.
  • The tool identified the highest-risk 1% of the population as having 23.9 times higher FD prevalence, highlighting its ability to pinpoint at-risk individuals.

Conclusions:

  • The AI tool demonstrated high accuracy in identifying Fabry disease using real-world health data.
  • The tool's performance across different cohorts and its identification of key phenotypic features, validated by experts, support its clinical utility.
  • This AI platform has the potential to significantly reduce the underdiagnosis of Fabry disease.