Evidence map›Paper›PMID 32861271›Full record

Trial reportThe Lancet. Child & adolescent health2020

A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial.

Andreea M Pavel, Janet M Rennie, Linda S de Vries, Mats Blennow, Adrienne Foran, Divyen K Shah, Ronit M Pressler, Olga Kapellou, Eugene M Dempsey, Sean R Mathieson and 10 more

Registry-linked trialOpen access · hybridAbstract readMulticenter StudyRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in The Lancet. Child & adolescent health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02431780 (A Multi-centre, Randomised, Controlled, Clinical Investigation of a Standalone Decision Support Algorithm for Neonatal Seizure Recognition), which is not on this map. Cited by 56 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed, 3 pooled it
25.8field-weighted citation impact, top 1% of its field
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.

NCT02431780 nacompletednot on this map

A Multi-centre, Randomised, Controlled, Clinical Investigation of a Standalone Decision Support Algorithm for Neonatal Seizure Recognition

TypeinterventionalSponsorUniversity College CorkRan2015 to 2017Enrolled264ConditionsSeizures, Brain Diseases, Central Nervous System Diseases, EpilepsyArmsANSeR Software System
3 · Its place in the literature

Who cites it

56 citing papers in PubMed, 3 syntheses or guidelines pooled it, 140 citations in OpenAlex.

  1. Anti-seizure medications for neonates with seizures.The Cochrane database of systematic reviews · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors at 11 institutions in 4 countries.

Andreea M PavelINFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
Janet M RennieInstitute for Women's Health, University College London, London, UK.
Linda S de VriesUtrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Mats BlennowDepartment of Neonatal Medicine, Karolinska University Hospital, Stockholm, Sweden; Division of Paediatrics, Department CLINTEC, Karolinska Institutet, Stockholm, Sweden.
Adrienne ForanRotunda Hospital, Dublin, Ireland.
Divyen K ShahRoyal London Hospital, London, UK; London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
Ronit M PresslerDepartment of Clinical Neurophysiology, Great Ormond Street Hospital for Children NHS Trust, London, UK.
Olga KapellouHomerton University Hospital NHS Foundation Trust, London, UK.
Eugene M DempseyINFANT Research Centre, University College Cork, Cork, Ireland.
Sean R MathiesonINFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
Elena PavlidisINFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
Alexander C van HuffelenClinical Neurophysiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Vicki LivingstoneINFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
Mona C ToetUtrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Lauren C WeekeUtrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Mikael FinderDepartment of Neonatal Medicine, Karolinska University Hospital, Stockholm, Sweden; Division of Paediatrics, Department CLINTEC, Karolinska Institutet, Stockholm, Sweden.
Subhabrata MitraInstitute for Women's Health, University College London, London, UK.
Deirdre M MurrayINFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
William P MarnaneINFANT Research Centre, University College Cork, Cork, Ireland.
Geraldine B BoylanINFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland. Electronic address: g.boylan@ucc.ie.
University College Cork · IEInfant · IEUtrecht University · NLUniversity College London · GBHomerton University Hospital NHS Foundation Trust · GBKarolinska Institutet · SEKarolinska University Hospital · SELondon Women's Clinic · GBQueen Mary University of London · GBRotunda Hospital · IEUniversity Medical Center Utrecht · NL

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundDespite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging in clinical practice. Algorithms for decision support in the recognition of neonatal seizures could improve detection. We aimed to assess the diagnostic accuracy of an automated seizure detection algorithm called Algorithm for Neonatal Seizure Recognition (ANSeR).

methodsThis multicentre, randomised, two-arm, parallel, controlled trial was done in eight neonatal centres across Ireland, the Netherlands, Sweden, and the UK. Neonates with a corrected gestational age between 36 and 44 weeks with, or at significant risk of, seizures requiring EEG monitoring, received cEEG plus ANSeR linked to the EEG monitor displaying a seizure probability trend in real time (algorithm group) or cEEG monitoring alone (non-algorithm group). The primary outcome was diagnostic accuracy (sensitivity, specificity, and false detection rate) of health-care professionals to identify neonates with electrographic seizures and seizure hours with and without the support of the ANSeR algorithm. Neonates with data on the outcome of interest were included in the analysis. This study is registered with ClinicalTrials.gov, NCT02431780.

findingsBetween Feb 13, 2015, and Feb 7, 2017, 132 neonates were randomly assigned to the algorithm group and 132 to the non-algorithm group. Six neonates were excluded (four from the algorithm group and two from the non-algorithm group). Electrographic seizures were present in 32 (25·0%) of 128 neonates in the algorithm group and 38 (29·2%) of 130 neonates in the non-algorithm group. For recognition of neonates with electrographic seizures, sensitivity was 81·3% (95% CI 66·7-93·3) in the algorithm group and 89·5% (78·4-97·5) in the non-algorithm group; specificity was 84·4% (95% CI 76·9-91·0) in the algorithm group and 89·1% (82·5-94·7) in the non-algorithm group; and the false detection rate was 36·6% (95% CI 22·7-52·1) in the algorithm group and 22·7% (11·6-35·9) in the non-algorithm group. We identified 659 h in which seizures occurred (seizure hours): 268 h in the algorithm versus 391 h in the non-algorithm group. The percentage of seizure hours correctly identified was higher in the algorithm group than in the non-algorithm group (177 [66·0%; 95% CI 53·8-77·3] of 268 h vs 177 [45·3%; 34·5-58·3] of 391 h; difference 20·8% [3·6-37·1]). No significant differences were seen in the percentage of neonates with seizures given at least one inappropriate antiseizure medication (37·5% [95% CI 25·0 to 56·3] vs 31·6% [21·1 to 47·4]; difference 5·9% [-14·0 to 26·3]).

interpretationANSeR, a machine-learning algorithm, is safe and able to accurately detect neonatal seizures. Although the algorithm did not enhance identification of individual neonates with seizures beyond conventional EEG, recognition of seizure hours was improved with use of ANSeR. The benefit might be greater in less experienced centres, but further study is required.

fundingWellcome Trust, Science Foundation Ireland, and Nihon Kohden.

Indexed as

AlgorithmsElectroencephalographyHumansInfantIntensive Care, NeonatalIrelandMachine LearningMonitoring, PhysiologicNetherlandsSeizuresSwedenUnited Kingdom

Identifiers

PMID32861271
PMCPMC7492960
OpenAlexW3080952300

What OpenQuestion holds

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Registered trials

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.