Electronic health record signatures identify undiagnosed patients with common variable immunodeficiency disease-Reference-Cited by-同舟云学术

Electronic health record signatures identify undiagnosed patients with common variable immunodeficiency disease

Published:2024-05 Issue:745 Volume:16 Page:
ISSN:1946-6234
Container-title:Science Translational Medicine
language:en
Short-container-title:Sci. Transl. Med.

Author:

Johnson Ruth¹²^ORCID,Stephens Alexis V.³,Mester Rachel¹^ORCID,Knyazev Sergey²^ORCID,Kohn Lisa A.³^ORCID,Freund Malika K.⁴,Bondhus Leroy⁴^ORCID,Hill Brian L.¹^ORCID,Schwarz Tommer⁵,Zaitlen Noah⁶^ORCID,Arboleda Valerie A.²⁴⁷^ORCID,A. Bastarache Lisa⁸,Pasaniuc Bogdan²⁴⁵⁷,Butte Manish J.³⁴⁹^ORCID

Affiliation:

1. Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA.

2. Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.

3. Department of Pediatrics, Division of Immunology, Allergy and Rheumatology, University of California Los Angeles, Los Angeles, CA 90095, USA.

4. Department of Human Genetics, University of California Los Angeles, Los Angeles, CA 90095, USA.

5. Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA.

6. Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA.

7. Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.

8. Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA 37203.

9. Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, CA 90095, USA.

Abstract

Human inborn errors of immunity include rare disorders entailing functional and quantitative antibody deficiencies due to impaired B cells called the common variable immunodeficiency (CVID) phenotype. Patients with CVID face delayed diagnoses and treatments for 5 to 15 years after symptom onset because the disorders are rare (prevalence of ~1/25,000), and there is extensive heterogeneity in CVID phenotypes, ranging from infections to autoimmunity to inflammatory conditions, overlapping with other more common disorders. The prolonged diagnostic odyssey drives excessive system-wide costs before diagnosis. Because there is no single causal mechanism, there are no genetic tests to definitively diagnose CVID. Here, we present PheNet, a machine learning algorithm that identifies patients with CVID from their electronic health records (EHRs). PheNet learns phenotypic patterns from verified CVID cases and uses this knowledge to rank patients by likelihood of having CVID. PheNet could have diagnosed more than half of our patients with CVID 1 or more years earlier than they had been diagnosed. When applied to a large EHR dataset, followed by blinded chart review of the top 100 patients ranked by PheNet, we found that 74% were highly probable to have CVID. We externally validated PheNet using >6 million records from disparate medical systems in California and Tennessee. As artificial intelligence and machine learning make their way into health care, we show that algorithms such as PheNet can offer clinical benefits by expediting the diagnosis of rare diseases.

Publisher

American Association for the Advancement of Science (AAAS)

Link

https://www.science.org/doi/pdf/10.1126/scitranslmed.ade4510

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