Evidence map›Paper›PMID 41212874›Full record

ArticlePloS one2025

Developing and validating a clinical prediction model to predict epilepsy-related emergency department attendance, hospital admission, or death: A cohort study protocol.

Gashirai K Mbizvo, Glen P Martin, Laura J Bonnett, Pieta Schofield, Hilary Garret, Alan Griffiths, W Owen Pickrell, Iain Buchan, Gregory Y H Lip, Anthony G Marson

Abstract readValidation Study
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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Gashirai K MbizvoLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.ORCID https://orcid.org/0000-0002-9588-2944
Glen P MartinDivision of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom.
Laura J BonnettUniversity of Liverpool Department of Biostatistics, Liverpool, United Kingdom.ORCID https://orcid.org/0000-0002-6981-9212
Pieta SchofieldDepartment of Public Health, Policy and Systems, Institute of Population Health, University of Liverpool, Liverpool, United Kingdom.
Hilary GarretPublic Involvement Team, Applied Research Collaboration North West Coast (ARC NWC), Liverpool, United Kingdom.
Alan GriffithsPublic Involvement Team, Applied Research Collaboration North West Coast (ARC NWC), Liverpool, United Kingdom.
W Owen PickrellNeurology Research Group, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, Wales, United Kingdom.
Iain BuchanDepartment of Public Health, Policy and Systems, Institute of Population Health, University of Liverpool, Liverpool, United Kingdom.ORCID https://orcid.org/0000-0003-3392-1650
Gregory Y H LipLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.
Anthony G MarsonLiverpool Interdisciplinary Neuroscience Centre, University of Liverpool, Liverpool, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThis retrospective open cohort study develops and externally validates a clinical prediction model (CPM) to predict the joint risk of two important outcomes occurring within the next year in people with epilepsy (PWE). These are: A) seizure-related emergency department or hospital admission; and B) epilepsy-related death. This will provide clinicians with a tool to predict either or both of these common outcomes. This has not previously been done despite both being potentially avoidable, interrelated, and devastating for patients and their families. We hypothesise that the CPM will identify individuals at high or low risk of either or both outcomes. We will guide clinicians on proposed actions to take based on the overall risk score. METHODS AND ANALYSIS: Routinely collected, anonymised, electronic health data from the following research platforms will be used: i) Clinical Practice Research Datalink (CPRD); ii) Secure Anonymised Information Linkage databank (SAIL); iii) Combined Intelligence for Population Health Action (CIPHA); and iv) TriNetX. We will study PWE aged ≥16 years having outcomes A and/or B between 2010-2024 within these datasets. Sample sizes of over 100,000 PWE are expected across these datasets. Candidate predictors will include demographic, lifestyle, clinical, and management variables. Logistic regression and multistate modelling will be used to develop a suitable CPM. The choice of modelling approach will be informed by consultation with clinicians and members of the public. We will assess the model's predictive performance using CPRD as a development dataset, and conduct external validation using SAIL, CIPHA, and TriNetX.

conclusionsThis large study will develop and validate a CPM for PWE, creating an internationally generalisable tool for subsequent clinical implementation. It will predict the joint risk of acute admission and death in PWE. Mortality prediction is highlighted by NICE as a key recommendation for epilepsy research. The study has been co-developed by epilepsy researchers and members of the public affected by epilepsy.

Indexed as

Emergency Service, HospitalEpilepsyHospitalizationAdolescentAdultCohort StudiesFemaleHumansMaleMiddle AgedRetrospective StudiesRisk Assessment

Identifiers

PMID41212874
PMCPMC12599926

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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.