Evidence map›Paper›PMID 38691621›Full record

ArticleScience translational medicine2024

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

Ruth Johnson, Alexis V Stephens, Rachel Mester, Sergey Knyazev, Lisa A Kohn, Malika K Freund, Leroy Bondhus, Brian L Hill, Tommer Schwarz, Noah Zaitlen and 4 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
6.7field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 28 citations in OpenAlex.

  1. Article
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  6. Developing a phenotype risk score formedRxiv : the preprint server for health sciences · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 2 institutions in 1 country.

Ruth JohnsonDepartment of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0002-1929-0998
Alexis V StephensDepartment of Pediatrics, Division of Immunology, Allergy and Rheumatology, University of California Los Angeles, Los Angeles, CA 90095, USA.
Rachel MesterDepartment of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0002-6498-7727
Sergey KnyazevDepartment of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0003-0385-1831
Lisa A KohnDepartment of Pediatrics, Division of Immunology, Allergy and Rheumatology, University of California Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0002-1652-1658
Malika K FreundDepartment of Human Genetics, University of California Los Angeles, Los Angeles, CA 90095, USA.
Leroy BondhusDepartment of Human Genetics, University of California Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0003-3398-5347
Brian L HillDepartment of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0002-6881-5770
Tommer SchwarzBioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA.
Noah ZaitlenDepartment of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0002-3553-3670
Valerie A ArboledaDepartment of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0002-9687-9122
Lisa A BastaracheDepartment of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA 37203.
Bogdan PasaniucDepartment of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.
Manish J ButteDepartment of Pediatrics, Division of Immunology, Allergy and Rheumatology, University of California Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0002-4490-5595
University of California, Los Angeles · USVanderbilt University · US

Funding

Collaborative multi-site project to speed the identification and management of rare genetic immune diseasesR01AI153827 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BUTTE, MANISH J, PASANIUC, BOGDAN · 2021 to 2025
$3.9M
Unraveling correlations between Mendelian and common disease using functional genomicsDP5OD024579 · OD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARBOLEDA, VALERIE A · 2017 to 2021
$1.9M
NIAID NIH HHS R01 AI153827NIH HHS DP5 OD024579
6 · The paper itself

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.

Indexed as

Common Variable ImmunodeficiencyElectronic Health RecordsAdultAlgorithmsFemaleHumansMachine LearningMalePhenotypeUndiagnosed Diseases

Identifiers

PMID38691621
PMCPMC11402387
OpenAlexW4396541884

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.