Evidence map›Paper›PMID 40327684›Full record

ArticlePloS one2025

Increasing diagnoses per patient admission at a specialist children's hospital: A retrospective study.

Stuart A Bowyer, John Booth, Daniel Key, Eleni Pissaridou, William A Bryant, Harry Hemingway, Neil J Sebire

Abstract read
In one paragraph

Article in PloS one, 2025. 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

7 authors.

Stuart A BowyerData Research, Innovation and Virtual Environments (DRIVE) Unit, Great Ormond Street Hospital, London United Kingdom.ORCID https://orcid.org/0000-0003-2726-1705
John BoothData Research, Innovation and Virtual Environments (DRIVE) Unit, Great Ormond Street Hospital, London United Kingdom.ORCID https://orcid.org/0000-0003-4357-1324
Daniel KeyData Research, Innovation and Virtual Environments (DRIVE) Unit, Great Ormond Street Hospital, London United Kingdom.ORCID https://orcid.org/0000-0002-9559-1784
Eleni PissaridouData Research, Innovation and Virtual Environments (DRIVE) Unit, Great Ormond Street Hospital, London United Kingdom.
William A BryantData Research, Innovation and Virtual Environments (DRIVE) Unit, Great Ormond Street Hospital, London United Kingdom.
Harry HemingwayInstitute of Health Informatics, Faculty of Population Health Sciences, University College London, London, United Kingdom.
Neil J SebireData Research, Innovation and Virtual Environments (DRIVE) Unit, Great Ormond Street Hospital, London United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveIn adult practice there is recognition that average patient complexity is increasing, with a greater proportion of patients having multiple diagnoses or comorbidities. This study aims to examine whether there has been a change in number of recorded coexisting diagnoses per patient over a 24-year period for children attending as in-patients to a specialist children's hospital in England.

methodsFollowing all in-patient admissions, patient episodes are allocated specific diagnosis codes (ICD-10) by a specialist clinical coding team according to standard NHS criteria and guidance. We examine the number of coexisting diagnoses allocated per patient admission over a 24-year period.

resultsFrom a total of 278,579 overnight in-patient admissions during the study period (2000-2023) there were 1,023,276 ICD-10 patient diagnoses. The mean number of diagnoses per admission increased from 2.72 to 10.43 over the period (Kendall's tau statistic of 0.93; p-value < 0.001), an increase of 284% (95% confidence interval 275% - 293%).

conclusionsOver recent decades, the recorded complexity of patients attending a specialist children's hospital appear to have increased significantly, with an almost 3-fold increase in the number of coexisting diagnoses present per admission. The cause of this finding cannot be determined from the data; however, it appears to be gradual and consistent, and across all speciality areas suggesting biological or referral factors rather than artefactual coding issues. Recognition of such a trend is important when interpreting retrospective data for AI, research, and planning purposes.

Indexed as

Hospitals, PediatricPatient AdmissionAdolescentChildChild, PreschoolEnglandFemaleHumansInfantInternational Classification of DiseasesMaleRetrospective Studies

Identifiers

PMID40327684
PMCPMC12054864

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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.