Evidence map›Paper›PMID 38698482›Full record

ArticleOrphanet journal of rare diseases2024

An algorithm to identify patients aged 0-3 with rare genetic disorders.

Bryn D Webb, Lisa Y Lau, Despina Tsevdos, Ryan A Shewcraft, David Corrigan, Lisong Shi, Seungwoo Lee, Jonathan Tyler, Shilong Li, Zichen Wang and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Orphanet journal of rare diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

15 authors.

Bryn D Webb *Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. bdwebb@wisc.edu.ORCID 0000-0001-6174-4677
Lisa Y Lau *GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA.
Despina TsevdosDepartment of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Ryan A ShewcraftGeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA.
David CorriganGeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA.
Lisong ShiGeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA.
Seungwoo LeeGeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA.
Jonathan TylerGeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA.
Shilong LiGeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA.
Zichen WangGeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA.
Gustavo StolovitzkyGeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA.
Lisa EdelmannGeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA.
Rong ChenGeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA.
Eric E Schadt *Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Li Li *GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA. dlleely@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical record data is challenging given incompleteness of records, inaccurate medical diagnosis coding, as well as heterogeneity in clinical symptoms and procedures for specific disorders. We sought to develop a digital phenotyping algorithm (PheIndex) using electronic medical records to identify children aged 0-3 diagnosed with genetic disorders or who present with illness with an increased risk for genetic disorders.

resultsThrough expert opinion, we established 13 criteria for the algorithm and derived a score and a classification. The performance of each criterion and the classification were validated by chart review. PheIndex identified 1,088 children out of 93,154 live births who may be at an increased risk for genetic disorders. Chart review demonstrated that the algorithm achieved 90% sensitivity, 97% specificity, and 94% accuracy.

conclusionsThe PheIndex algorithm can help identify when a rare genetic disorder may be present, alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist.

Indexed as

AlgorithmsRare DiseasesChild, PreschoolElectronic Health RecordsFemaleGenetic Diseases, InbornHumansInfantInfant, NewbornMalePhenotypeAlgorithmClinical decision-makingDigital phenotypingPediatric genetic disorders

Identifiers

PMID38698482
PMCPMC11064409

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Registered trials

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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.