Evidence map›Paper›PMID 41651962›Full record

ArticleCommunications medicine2026

Machine learning to infer neurocognitive testing scores among adolescents and young adults with congenital heart disease.

Mohammad Arafat Hussain, Sheng He, Heather R Adams, Evdokia Anagnoustou, David C Bellinger, Martina Brueckner, Wendy K Chung, John Cleveland, Bruce D Gelb, Elizabeth Goldmuntz and 14 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications medicine, 2026. 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. Review
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

24 authors.

Mohammad Arafat HussainDepartment of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0003-0545-5779
Sheng HeDepartment of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Heather R AdamsDepartments of Neurology and Pediatrics, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.
Evdokia AnagnoustouDepartment of Pediatrics, University of Toronto, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada.
David C BellingerDepartment of Neurology and Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Martina BruecknerDepartments of Genetics and Pediatrics, Yale University School of Medicine, New Haven, CT, USA.ORCID http://orcid.org/0000-0003-0347-5389
Wendy K ChungDepartment of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0003-3438-5685
John ClevelandDepartments of Surgery and Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Bruce D GelbMindich Child Health and Development Institute and Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID http://orcid.org/0000-0001-8527-5027
Elizabeth GoldmuntzDivision of Cardiology, Children's Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0003-2936-4396
Donald J HaglerCenter for Multimodal Imaging and Genetics, and Department of Radiology, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Hao HuangDepartment of Radiology, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-9103-4382
Patrick McQuillenDepartment of Pediatrics, Benioff Children's Hospital, University of California, San Francisco, San Francisco, CA, USA.
Thomas A MillerDepartment of Pediatrics, Primary Children's Hospital, University of Utah, Salt Lake City, UT, USA.
Ami Norris-BrilliantDepartment of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID http://orcid.org/0009-0002-3535-6579
George A PorterDepartment of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA.
Nina ThomasDepartment of Psychiatry, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Madalina E TivarusDepartment of Neuroscience, University of Rochester Medical Center, Rochester, NY, USA.
Duan XuSchool of Medicine, University of California, San Francisco, San Francisco, CA, USA.
Yufeng ShenDepartment of Systems Biology & Department of Biomedical Informatics, Columbia University, New York, NY, USA.
Jane W NewburgerDepartment of Cardiology, Boston Children's Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0002-7794-9017
P Ellen GrantDepartment of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Sarah U MortonDepartment of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. sarah.morton@childrens.harvard.edu.
Yangming OuDepartment of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. yangming.ou@childrens.harvard.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCongenital heart disease (CHD) affects about 1% of births and is linked to differences in thinking and learning. Understanding how birth, genetic, clinical, and environmental factors together explain cognitive variability can inform monitoring and care. This study builds a multivariate model predicting cognition across multiple domains in adolescents and young adults with CHD.

methodsWe studied 89 adolescents and young adults (AYAs; mean age 16 years) with CHD who completed structural and diffusion MRI and fifteen neurocognitive tests across seven domains. Using an enhanced forward-inclusion and backward-elimination strategy with cross-validation, we built multivariate models incorporating biological, socioeconomic, clinical, genetic, and brain imaging features. Performance was evaluated using Pearson correlation (

resultsHere we show that models infer scores with moderate accuracy (

conclusionsA multivariate approach combining brain imaging with genetic, clinical, and environmental factors provides clinically meaningful inference of individual cognitive performance in AYAs with CHD. These findings suggest complementary roles of brain, genetic, and contextual factors in shaping cognitive variability and motivate validation in larger cohorts.

Identifiers

PMID41651962
PMCPMC12987932

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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.