Predicting Neurological Recovery after Traumatic Brain Injury in Children: A Systematic Review of Prognostic Models

Huth, Samuel; Slater, Anthony; Waak, Michaela; Barlow, Karen M.; Raman, Sainath

doi:10.1089/neu.2020.7158

Cited by 11 publications

(9 citation statements)

References 41 publications

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“…5 Currently, the best models for predicting outcomes in pediatric TBI are derived from adult studies. 26 Prognostic models from the International Mission for Prognosis and Analysis of Clinical Trials in TBI 26 27 and Corticoid Randomization After Significant Head Injury (CRASH)-TBI trial 26 use a combination of clinical and neuroimaging factors to predict neurologic outcome. However, the feasibility of advanced neuroimaging techniques such as MRI in acutely injured pediatric patients is limited, and thus, it is imperative to further identify bedside variables indicative of prognosis in this population.…”

Section: Discussionmentioning

confidence: 99%

Associations between Electroencephalographic Variables, Early Post-Traumatic Seizure Risk, and Outcomes following Pediatric Severe Traumatic Brain Injury

Nickerson

Villo

Eisner

et al. 2022

J Pediatr Intensive Care

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Early post-traumatic seizures (PTS) are associated with worse outcomes in children with traumatic brain injury (TBI). Our aim was to identify the association between continuous electroencephalogram (cEEG) characteristics and early PTS risk following pediatric severe TBI. We also evaluated the relationship between cEEG background features and outcomes. A single-center retrospective cohort study was performed on children between 0 and 18 years of age admitted to the pediatric intensive care unit from 2016 to 2019 with severe TBI and cEEG monitoring within 7 days of injury. Raw cEEG tracings were reviewed by an epileptologist in accordance with American Clinical Neurophysiology Society (ACNS) Critical Care EEG terminology. Univariate comparisons were made between children with and without early PTS, as well as between those with and without varying cEEG background features. Eighteen children (31%) of the 59 included had early PTS. Interictal abnormalities, inclusive of sporadic spikes and sharp waves, rhythmic delta activity, or lateralized periodic discharges (LPDs) were more common among children with seizures (100 vs. 22%; p < 0.01). LPDs were also more common in the seizure group (44 vs. 2%; p < 0.01). Background discontinuity was associated with worse Glasgow Outcome Scale—Extended Pediatric Version (GOS-E Peds) scores at discharge and 3-, 6-, and 12-month post-discharge (p < 0.01). Lack of reactivity was also associated with worse GOS-E Peds scores at 3-, 6-, and 12-month post-discharge (p < 0.01). Interictal abnormalities and LPDs were each associated with early PTS following pediatric severe TBI. Larger studies should evaluate if high-risk patients would benefit from prolonged cEEG monitoring and/or more aggressive anti-seizure prophylaxis. Discontinuity and lack of variability were associated with worse outcomes. Future studies should attempt to clarify their role as potential early markers of prognosis.

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Section: Discussionmentioning

confidence: 99%

Associations between Electroencephalographic Variables, Early Post-Traumatic Seizure Risk, and Outcomes following Pediatric Severe Traumatic Brain Injury

Nickerson

Villo

Eisner

et al. 2022

J Pediatr Intensive Care

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“…7 Several prediction models to target patients with TBI have been reported. [8][9][10][11][12] However, most incorporated information is available only in the hospital, such as laboratory results or image findings. 8 9 13 In addition, most previous prediction models focused on the outcomes of patients with TBI, [14][15][16] not the identification of TBI.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a prehospital-stage prediction tool for traumatic brain injury: a multicentre retrospective cohort study in Korea

et al. 2022

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ObjectivesPredicting diagnosis and prognosis of traumatic brain injury (TBI) at the prehospital stage is challenging; however, using comprehensive prehospital information and machine learning may improve the performance of the predictive model. We developed and tested predictive models for TBI that use machine learning algorithms using information that can be obtained in the prehospital stage.DesignThis was a multicentre retrospective study.Setting and participantsThis study was conducted at three tertiary academic emergency departments (EDs) located in an urban area of South Korea. The data from adult patients with severe trauma who were assessed by emergency medical service providers and transported to three participating hospitals between 2014 to 2018 were analysed.ResultsWe developed and tested five machine learning algorithms—logistic regression analyses, extreme gradient boosting, support vector machine, random forest and elastic net (EN)—to predict TBI, TBI with intracranial haemorrhage or injury (TBI-I), TBI with ED or admission result of admission or transferred (TBI with non-discharge (TBI-ND)) and TBI with ED or admission result of death (TBI-D). A total of 1169 patients were included in the final analysis, and the proportions of TBI, TBI-I, TBI-ND and TBI-D were 24.0%, 21.5%, 21.3% and 3.7%, respectively. The EN model yielded an area under receiver–operator curve of 0.799 for TBI, 0.844 for TBI-I, 0.811 for TBI-ND and 0.871 for TBI-D. The EN model also yielded the highest specificity and significant reclassification improvement. Variables related to loss of consciousness, Glasgow Coma Scale and light reflex were the three most important variables to predict all outcomes.ConclusionOur results inform the diagnosis and prognosis of TBI. Machine learning models resulted in significant performance improvement over that with logistic regression analyses, and the best performing model was EN.

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“…Severe traumatic brain injury (TBI) refers to severe injuries of the central nervous system such as skull fracture, brain contusion and laceration, and intracranial hematoma caused by various reasons [1]. It's been reported that the incidence of STBI ranks rst in the all types of trauma, accounting for 9% to 21% of trauma in all parts of the body, and the mortality is as high as 50% [2,3]. In recent years, with the improvement of trauma treatment and the development of intensive care medicine, the mortality of STBI has declined, but the quality of life and daily living ability of surviving TBI patients have declined signi cantly [4].…”

Section: Introductionmentioning

confidence: 99%

The pattern of hospital-community-home (HCH) nursing in tracheostomy patients with severe traumatic brain injury: is it feasible?

Yin¹,

Wu²,

Zhou³

et al. 2020

Preprint

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Background: Tracheostomy is very common in patients with severe traumatic brain injury (TBI), and long-term nursing care are needed for those patients. We aimed to evaluate the effects of hospital-community-home (HCH) nursing in those patients. Methods: Tracheostomy patients with severe TBI needing long-term care were included. All patients underwent two months long follow-up. Glasgow coma score (GCS), Karnofsky, Self-Anxiety Scale (SAS) (SAS) and Barthel assessment at the discharge and two months after discharge were evaluated. The tracheostomy related complications were recorded and compared.Results: A total of 60 patients were included. There weren’t significant differences between two groups in the GCS, Karnofsky, SAS and Barthel index at discharge((all p>0.05), the GCS, Karnofsky and Barthel index was all significantly increased after two months follow-up for two groups (all p<0.05), and the GCS, Karnofsky and Barthel index at two months follow-up in HCH group was significantly higher than that of control group(all p<0.05), but the SAS at two months follow-up in HCH group was significantly less than that of control group(p=0.009). The incidence of block of artificial tracheal cannula and readmission in HCH group were significant less than that of control group (all p<0.05).Conclusion: HCH nursing care is feasible in tracheostomy patients with severe TBI, future studies are needed to further evaluate the role of HCH nursing care.

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Predicting Neurological Recovery after Traumatic Brain Injury in Children: A Systematic Review of Prognostic Models

Cited by 11 publications

References 41 publications

Associations between Electroencephalographic Variables, Early Post-Traumatic Seizure Risk, and Outcomes following Pediatric Severe Traumatic Brain Injury

Associations between Electroencephalographic Variables, Early Post-Traumatic Seizure Risk, and Outcomes following Pediatric Severe Traumatic Brain Injury

Development and validation of a prehospital-stage prediction tool for traumatic brain injury: a multicentre retrospective cohort study in Korea

The pattern of hospital-community-home (HCH) nursing in tracheostomy patients with severe traumatic brain injury: is it feasible?

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