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A machine-learning algorithm using claims data to identify patients with homozygous familial hypercholesterolemia.

Jing Gu1, Matthew Epland2, Xinshuo Ma2

  • 1Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, New York, NY, 10591, USA.

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A machine-learning model effectively identifies patients with homozygous familial hypercholesterolemia (HoFH) using healthcare claims data. This tool aids in diagnosing this ultra-rare disease, improving patient identification and treatment.

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Area of Science:

  • Medical Informatics
  • Rare Diseases
  • Machine Learning in Healthcare

Background:

  • Homozygous familial hypercholesterolemia (HoFH) is an ultra-rare, underdiagnosed, and undertreated genetic disorder.
  • Early diagnosis and intervention are critical for managing HoFH and preventing cardiovascular complications.

Purpose of the Study:

  • To develop and validate a machine-learning model for identifying potential HoFH patients using real-world healthcare claims data.
  • To improve the screening and diagnostic process for HoFH.

Main Methods:

  • Utilized Komodo Healthcare Map claims data and patient support program enrollment (MyRARE).
  • Developed a machine-learning model using a training set (80%) and tested on a separate set (20%).
  • Selected 87 features from 10,616 candidates and employed a fast interpretable greedy-tree sums algorithm.

Main Results:

  • The model achieved high performance metrics: precision of 0.98, recall of 0.88, AUC of 0.98, and accuracy of 0.97.
  • Identified four key features crucial for HoFH prediction.
  • Demonstrated strong performance in identifying HoFH patients within the testing set.

Conclusions:

  • The developed machine-learning model is a valuable tool for HoFH screening and diagnosis.
  • Leveraging healthcare claims data can significantly aid in identifying patients with rare diseases like HoFH.
  • This approach can facilitate earlier detection and improve management strategies for HoFH patients.