Evidence map›Paper›PMID 29995912›Full record

ArticlePloS one2018

Sub-classifying patients with mild traumatic brain injury: A clustering approach based on baseline clinical characteristics and 90-day and 180-day outcomes.

Bing Si, Gina Dumkrieger, Teresa Wu, Ross Zafonte, Alex B Valadka, David O Okonkwo, Geoffrey T Manley, Lujia Wang, David W Dodick, Todd J Schwedt and 1 more

Abstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

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  18. GCS 15: when mild TBI isn't so mild.Neurological research and practice · 2019
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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

11 authors.

Bing SiSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States of America.
Gina DumkriegerSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States of America.
Teresa WuSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States of America.
Ross ZafonteDepartment of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Massachusetts General Hospital, Brigham and Women's Hospital, Boston, MA, United States of America.
Alex B ValadkaDepartment of Neurosurgery, Virginia Commonwealth University, Richmond, VA, United States of America.
David O OkonkwoDepartment of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, United States of America.
Geoffrey T ManleyDepartment of Neurological Surgery, University of California, San Francisco, CA, United States of America.
Lujia WangSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States of America.
David W DodickDepartment of Neurology, Mayo Clinic, Phoenix, AZ, United States of America.
Todd J SchwedtDepartment of Neurology, Mayo Clinic, Phoenix, AZ, United States of America.ORCID 0000-0002-7780-7086
Jing LiSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States of America.

Funding

Toward new classification criteria for mild and moderate TBI by a data-inclusive cross-study analysis using FITBIRR21HD089035 · NICHD · MAYO CLINIC ARIZONA · PI LI, JING, SCHWEDT, TODD · 2016 to 2017
$436k
NICHD NIH HHS R21 HD089035
6 · The paper itself

Abstract

backgroundThe current classification of traumatic brain injury (TBI) into "mild", "moderate", or "severe" does not adequately account for the patient heterogeneity that still exists within each of these categories. The objective of this study was to identify "sub-groups" of mild TBI (mTBI) patients based on data available at the time of the initial post-TBI patient evaluation and to determine if the sub-grouping correlates with patient outcomes at 90 and 180 days post-TBI.

methodsData from patients in the TRACK-TBI Pilot dataset who had a Glasgow Coma Scale (GCS) score of 13 to 15 at arrival to the Emergency Department and a closed head injury were included. Considering 53 clinical variables that are typically available during the initial evaluation of the patient with mild TBI, sparse heirarchial clustering with cluster quality assessment was used to identify the optimal number of patient sub-groups. Patient sub-groups were then compared for ten outcomes measured at 90 or 180 days post-TBI.

resultsAmongst the 485 patients with mTBI, optimal clustering was based on the inclusion of 12 clinical variables that divided the patients into 5 mild TBI sub-groups. Clinical variables driving the sub-clustering included: gender, employment status, marital status, TBI due to falling, brain CT scan result, systolic blood pressure, diastolic blood pressure, administration of IV fluids in the Emergency Department, alcohol use, tobacco use, history of neurologic disease, and history of psychiatric disease. These 5 mild TBI sub-groups differed in their 90 day and 180 day outcomes within several domains including global outcomes, persistence of TBI-related symptoms, and neuropsychological impairment.

conclusionsSub-groups of patients with mTBI can be identified according to clinical variables that are relatively easy to obtain at the time of initial patient evaluation. A patient's sub-group assignment is associated with multidimensional patient outcomes at 90 and 180 days. These findings support the notion that there are clinically meaningful subgroups of patients amongst those currently classified as having mTBI.

Indexed as

Glasgow Coma ScaleAdultAlcohol DrinkingBlood PressureBrain ConcussionBrain Injuries, TraumaticChronic Traumatic EncephalopathyCluster AnalysisEmploymentFemaleHumansMaleMarital StatusMiddle AgedRisk FactorsSex Factors

Identifiers

PMID29995912
PMCPMC6040703

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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.