Evidence map›Paper›PMID 31349360›Full record

ArticlePediatric research2019

Modelling outcomes after paediatric brain injury with admission laboratory values: a machine-learning approach.

Saeed Kayhanian, Adam M H Young, Chaitanya Mangla, Ibrahim Jalloh, Helen M Fernandes, Matthew R Garnett, Peter J Hutchinson, Shruti Agrawal

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In one paragraph

Article in Pediatric research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 2 pooled it
–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

11 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Saeed Kayhanian *Department of Clinical Neurosciences, Division of Neurosurgery, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK. sk776@cam.ac.uk.
Adam M H Young *Department of Clinical Neurosciences, Division of Neurosurgery, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.
Chaitanya ManglaFitzwilliam College, University of Cambridge, Cambridge, UK.
Ibrahim JallohDepartment of Clinical Neurosciences, Division of Neurosurgery, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.
Helen M FernandesDepartment of Clinical Neurosciences, Division of Neurosurgery, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.
Matthew R GarnettDepartment of Clinical Neurosciences, Division of Neurosurgery, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.
Peter J HutchinsonDepartment of Clinical Neurosciences, Division of Neurosurgery, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.
Shruti AgrawalDepartment of Paediatric Intensive Care, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.

Funding

Department of HealthMedical Research Council G0600986Medical Research Council G1002277
6 · The paper itself

Abstract

backgroundSevere traumatic brain injury (TBI) is a leading cause of mortality in children, but the accurate prediction of outcomes at the point of admission remains very challenging. Admission laboratory results are a promising potential source of prognostic data, but have not been widely explored in paediatric cohorts. Herein, we use machine-learning methods to analyse 14 different serum parameters together and develop a prognostic model to predict 6-month outcomes in children with severe TBI.

methodsA retrospective review of patients admitted to Cambridge University Hospital's Paediatric Intensive Care Unit between 2009 and 2013 with a TBI. The data for 14 admission serum parameters were recorded. Logistic regression and a support vector machine (SVM) were trained with these data against dichotimised outcomes from the recorded 6-month Glasgow Outcome Scale.

resultsNinety-four patients were identified. Admission levels of lactate, H+, and glucose were identified as being the most informative of 6-month outcomes. Four different models were produced. The SVM using just the three most informative parameters was the best able to predict favourable outcomes at 6 months (sensitivity = 80%, specificity = 99%).

conclusionsOur results demonstrate the potential for highly accurate outcome prediction after severe paediatric TBI using admission laboratory data.

Indexed as

Machine LearningPatient AdmissionBrain InjuriesChildFemaleHumansMaleTreatment Outcome

Identifiers

PMID31349360

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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.