Evidence map›Paper›PMID 42129161›Full record

ArticleNature communications2026

Clinically-applicable prediction of hospital stay and patient similarity retrieval in paediatric cardiology using machine learning.

Louise Rigny, Iona Biggart, Kimberley Zakka, Jingteng Li, Alexander Capstick, Sebin Sabu, Pavithra Rajendran, Shankar Sridharan, Sumera Farooq, Andrew Taylor and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 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. 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

16 authors.

Louise RignyDepartment of Brain Sciences, Imperial College, London, UK. louise.rigny22@imperial.ac.uk.
Iona BiggartDepartment of Brain Sciences, Imperial College, London, UK.ORCID http://orcid.org/0009-0006-5785-4295
Kimberley ZakkaInnovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK.
Jingteng LiDepartment of Brain Sciences, Imperial College, London, UK.
Alexander CapstickDepartment of Brain Sciences, Imperial College, London, UK.ORCID http://orcid.org/0000-0002-3884-8045
Sebin SabuInnovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK.
Pavithra RajendranInnovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK.
Shankar SridharanInnovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK.
Sumera FarooqInnovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK.
Andrew TaylorInnovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK.
John BoothInnovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK.ORCID http://orcid.org/0000-0003-4357-1324
Shiren PatelInnovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK.
Padmanabhan RamnarayanInnovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK.
Neil SebireInnovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK.
Cho NgInnovation and Virtual Environments, NIHR Great Ormond Street Hospital Biomedical Research Centre and GOSH Data Research, London, UK.
Payam BarnaghiDepartment of Brain Sciences, Imperial College, London, UK. p.barnaghi@imperial.ac.uk.ORCID http://orcid.org/0000-0001-8591-9638

Funding

Great Ormond Street Hospital Charity (GOSH) 21PP30RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/W031892/1RCUK | Medical Research Council (MRC) UK DRI-7002
6 · The paper itself

Abstract

Paediatric cardiology presents challenges due to the rarity and complexity of conditions like congenital heart disease. Using retrospective electronic healthcare records from 1,522 Great Ormond Street Hospital cases, we benchmark machine learning models to predict length of stay and retrieve similar patient cases. BioClinical-BERT is used for embedding-based retrieval, while a Random Forest model achieves the best length of stay prediction accuracy (0.88  ± 0.02), outperforming clinicians. The Random Forest model shows mean precision and sensitivity of 0.77  ± 0.03 and 0.76  ± 0.04 during NHS silent deployment (1,052 admissions). K-means identifies three clinically distinct subgroups. Cosine similarity retrieval reveals diagnosis-driven top matches, while complications dominate broader sets. In a 25-case intensive care unit pilot, clinician-rated utility improves from 4.23  ± 2.42 to 4.41  ± 2.16 (scale 0-10). Our models surpass clinician performance in length of stay prediction and show promise for case retrieval, supporting data-driven decision-making in paediatric cardiology and beyond.

Indexed as

CardiologyHeart Defects, CongenitalLength of StayMachine LearningPediatricsChildChild, PreschoolElectronic Health RecordsFemaleHumansInfantPrediction AlgorithmsPredictive Learning ModelsRandom ForestRetrospective Studies

Identifiers

PMID42129161
PMCPMC13376627

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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.