Evidence map›Paper›PMID 34245632›Full record

ArticleAcademic emergency medicine : official journal of the Society for Academic Emergency Medicine2021

Development and external validation of the KIIDS-TBI tool for managing children with mild traumatic brain injury and intracranial injuries.

Jacob K Greenberg, Ranbir Ahluwalia, Madelyn Hill, Gabbie Johnson, Andrew T Hale, Ahmed Belal, Shawyon Baygani, Margaret A Olsen, Randi E Foraker, Christopher R Carpenter and 11 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Academic emergency medicine : official journal of the Society for Academic Emergency Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

21 authors.

Jacob K GreenbergDepartment of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.ORCID 0000-0003-2675-5658
Ranbir AhluwaliaDepartment of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.
Madelyn HillDepartment of Neurological Surgery, Dayton Children's Hospital, Dayton, OH, USA.
Gabbie JohnsonDepartment of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.ORCID 0000-0001-9463-4554
Andrew T HaleDepartment of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.
Ahmed BelalDepartment of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, USA.
Shawyon BayganiDepartment of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, USA.
Margaret A OlsenDepartment of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
Randi E ForakerDepartment of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
Christopher R CarpenterDepartment of Emergency Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.ORCID 0000-0002-2603-7157
Yan YanDepartment of Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
Laurie AckermanDepartment of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, USA.
Corina NojeDepartment of Anesthesiology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Eric JacksonDepartment of Neurological Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Erin BurnsDepartment of Pediatrics, Oregon Health and Science University, Portland, OR, USA.
Christina M SayamaDepartment of Neurological Surgery, Oregon Health and Science University, Portland, OR, USA.
Nathan R SeldenDepartment of Neurological Surgery, Oregon Health and Science University, Portland, OR, USA.
Shobhan VachhrajaniDepartment of Neurological Surgery, Dayton Children's Hospital, Dayton, OH, USA.
Chevis N ShannonDepartment of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.
Nathan KuppermannDepartment of Emergency Medicine, University of California-Davis, Davis, CA, USA.
David D LimbrickDepartment of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.

Funding

AHRQ HHS 1F32HS027075-01A1AHRQ HHS F32 HS027075
6 · The paper itself

Abstract

backgroundClinical decision support (CDS) may improve the postneuroimaging management of children with mild traumatic brain injuries (mTBI) and intracranial injuries. While the CHIIDA score has been proposed for this purpose, a more sensitive risk model may have broader use. Consequently, this study's objectives were to: (1) develop a new risk model with improved sensitivity compared to the CHIIDA model and (2) externally validate the new model and CHIIDA model in a multicenter data set.

methodsWe analyzed children ≤18 years old with mTBI and intracranial injuries included in the PECARN head injury data set (2004-2006). We used binary recursive partitioning to predict the composite outcome of neurosurgical intervention, intubation for > 24 h due to TBI, or death due to TBI. The new model was externally validated in a separate data set that included children treated at any one of six centers from 2006 to 2019.

resultsBased on 839 patients from the PECARN data set, a new risk model, the KIIDS-TBI model, was developed that incorporated imaging (e.g., midline shift) and clinical (e.g., Glasgow Coma Scale score) findings. Based on the model-predicted probability of the composite outcome, three cutoffs were evaluated to classify patients as "high risk" for level of care decisions. In the external validation data set consisting of 1,630 patients, the most conservative cutoff (i.e., any predictor present) identified 119 of 119 children with the composite outcome (sensitivity = 100%), but had the lowest specificity (26.3%). The other two decision-making cutoffs had worse sensitivity (94.1%-96.6%) but improved specificity (67.4%-81.3%). The CHIIDA model lacked the most conservative cutoff and otherwise showed the same or slightly worse performance compared to the other two cutoffs.

conclusionsThe KIIDS-TBI model has high sensitivity and moderate specificity for risk stratifying children with mTBI and intracranial injuries. Use of this CDS tool may help improve the safe, resource-efficient management of this important patient population.

Indexed as

Brain ConcussionBrain Injuries, TraumaticCraniocerebral TraumaAdolescentChildGlasgow Coma ScaleHumans

Identifiers

PMID34245632
PMCPMC13032774

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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.