scholarly journals Unpicking the Gordian knot: a systems approach to traumatic brain injury care in low-income and middle-income countries

2018 ◽  
Vol 3 (2) ◽  
pp. e000768 ◽  
Author(s):  
Tom Bashford ◽  
P John Clarkson ◽  
David K Menon ◽  
Peter J A Hutchinson
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Low Income ◽  
Systems Approach ◽  
Middle Income ◽  
Middle Income Countries

Association of traumatic brain injury severity and time to definitive care in three low-middle-income European countries

2021 ◽  
pp. injuryprev-2020-044049
Author(s):  
Corinne Peek-Asa ◽  
Madalina Adina Coman ◽  
Alison Zorn ◽  
Nino Chikhladze ◽  
Serghei Cebanu ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Injury Severity ◽  
Road Traffic ◽  
Severe Injury ◽  
Severe Injuries ◽  
Middle Income ◽  
Middle Income Countries ◽  
Low Middle Income Countries ◽  
Traumatic Brain Injury Severity

BackgroundLow-middle-income countries experience among the highest rates of traumatic brain injury in the world. Much of this burden may be preventable with faster intervention, including reducing the time to definitive care. This study examines the relationship between traumatic brain injury severity and time to definitive care in major trauma hospitals in three low-middle-income countries.MethodsA prospective traumatic brain injury registry was implemented in six trauma hospitals in Armenia, Georgia and the Republic of Moldova for 6 months in 2019. Brain injury severity was measured using the Glasgow Coma Scale (GCS) at admission. Time to definitive care was the time from injury until arrival at the hospital. Cox proportionate hazards models predicted time to care by severity, controlling for age, sex, mechanism, mode of transportation, location of injury and country.ResultsAmong 1135 patients, 749 (66.0%) were paediatric and 386 (34.0%) were adults. Falls and road traffic were the most common mechanisms. A higher proportion of adult (23.6%) than paediatric (5.4%) patients had GCS scores indicating moderate (GCS 9–11) or severe injury (GCS 0–8) (p<0.001). Less severe injury was associated with shorter times to care, while more severe injury was associated with longer times to care (HR=1.05, 95% CI 1.01 to 1.09). Age interacted with time to care, with paediatric cases receiving faster care.ConclusionsImplementation of standard triage and transport protocols may reduce mortality and improve outcomes from traumatic brain injury, and trauma systems should focus on the most severe injuries.

244 Traumatic Brain Injury Related Paediatric Mortality and Morbidity in Low- And Middle-Income Countries: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
S Bandyopadhyay ◽  
M Kawka ◽  
K Marks ◽  
G Richards ◽  
E Taylor ◽  
...  
Keyword(s):  
Systematic Review ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Meta Analysis ◽  
Road Traffic Accidents ◽  
Middle Income ◽  
Middle Income Countries ◽  
Low And Middle Income

Abstract Aim Three million cases of paediatric traumatic brain injury (pTBI) occur annually, the majority of which occur in low-and-middle-income countries (LMICs). However, there is a paucity of data on the outcomes of pTBI available. We aimed to systematically review and synthesise the reported morbidity and mortality from pTBI in the published literature about LMICs. Method A systematic review and meta-analysis were conducted. MEDLINE, EMBASE, Global Health, and Global Index Medicus were searched for relevant articles from January 2000 to May 2020. Observational or experimental studies on pTBI (individuals between the ages of 0 to 16 years) in LMICs were included. Morbidity data were descriptively analysed, and a random-effects model was used to pool mortality rates. PROSPERO ID: CRD42020171276. Results We included 145 studies from 38 countries representing 174073 patients with pTBI. Males were twice (95% CI: 1.6 – 2.4) as likely to have a pTBI than females. Where available, mild TBI represented ≥ 60% of all pTBI cases in most reports (n = 24/43, 56%). The commonest cited cause of pTBI was road traffic accidents (n = 16643/43083, 39%), followed by falls (n = 10927/43083, 25%). 4385 patients (n = 4385/18092, 24%) had a reduction from normal function on discharge. On average, there were 6.7 deaths per 100 cases of pTBI. Conclusions Only 38 LMICs have published data on the volume and burden of pTBI in their country. Limited data available suggests that young male children are at a high-risk of pTBIs in LMICs, particularly from road traffic accidents.

scholarly journals Machine learning-based dynamic mortality prediction after traumatic brain injury

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Rahul Raj ◽  
Teemu Luostarinen ◽  
Eetu Pursiainen ◽  
Jussi P. Posti ◽  
Riikka S. K. Takala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Cross Validation ◽  
Characteristic Curve ◽  
Mortality Prediction ◽  
Internal Validation ◽  
Middle Income ◽  
Middle Income Countries ◽  
Validation Technique

AbstractOur aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for intracranial pressure (ICP) for at least 24 h in three ICUs. We used machine learning-based logistic regression modeling to create two algorithms (based on ICP, mean arterial pressure [MAP], cerebral perfusion pressure [CPP] and Glasgow Coma Scale [GCS]) to predict 30-day mortality. We used a stratified cross-validation technique for internal validation. Of 472 included patients, 92 patients (19%) died within 30 days. Following cross-validation, the ICP-MAP-CPP algorithm’s area under the receiver operating characteristic curve (AUC) increased from 0.67 (95% confidence interval [CI] 0.60–0.74) on day 1 to 0.81 (95% CI 0.75–0.87) on day 5. The ICP-MAP-CPP-GCS algorithm’s AUC increased from 0.72 (95% CI 0.64–0.78) on day 1 to 0.84 (95% CI 0.78–0.90) on day 5. Algorithm misclassification was seen among patients undergoing decompressive craniectomy. In conclusion, we present a new concept of dynamic prognostication for patients with TBI treated in the ICU. Our simple algorithms, based on only three and four main variables, discriminated between survivors and non-survivors with accuracies up to 81% and 84%. These open-sourced simple algorithms can likely be further developed, also in low and middle-income countries.

IMPACT and CRASH prognostic models for traumatic brain injury: external validation in a South-American cohort

2020 ◽  
Vol 26 (6) ◽  
pp. 546-554
Author(s):  
Kwankaew Wongchareon ◽  
Hilaire J Thompson ◽  
Pamela H Mitchell ◽  
Jason Barber ◽  
Nancy Temkin
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Low Income ◽  
Characteristic Curve ◽  
External Validation ◽  
Prognostic Models ◽  
South American ◽  
Unfavourable Outcome ◽  
Middle Income ◽  
The Impact

ObjectiveTo develop a robust prognostic model, the more diverse the settings in which the system is tested and found to be accurate, the more likely it will be generalisable to untested settings. This study aimed to externally validate the International Mission for Prognosis and Clinical Trials in Traumatic Brain Injury (IMPACT) and Corticosteroid Randomization after Significant Head Injury (CRASH) models for low-income and middle-income countries using a dataset of patients with severe traumatic brain injury (TBI) from the Benchmark Evidence from South American Trials: Treatment of Intracranial Pressure study and a simultaneously conducted observational study.MethodA total of 550 patients with severe TBI were enrolled in the study, and 466 of those were included in the analysis. Patient admission characteristics were extracted to predict unfavourable outcome (Glasgow Outcome Scale: GOS<3) and mortality (GOS 1) at 14 days or 6 months.ResultsThere were 48% of the participants who had unfavourable outcome at 6 months and these included 38% who had died. The area under the receiver operating characteristic curve (AUC) values were 0.683–0.775 and 0.640–0.731 for the IMPACT and CRASH models respectively. The IMPACT CT model had the highest AUC for predicting unfavourable outcomes, and the IMPACT Lab model had the best discrimination for predicting 6-month mortality. The discrimination for both the IMPACT and CRASH models improved with increasing complexity of the models. Calibration revealed that there were disagreement between observed and predicted outcomes in the IMPACT and CRASH models.ConclusionThe overall performance of all IMPACT and CRASH models was adequate when used to predict outcomes in the dataset. However, some disagreement in calibration suggests the necessity for updating prognostic models to maintain currency and generalisability.

scholarly journals Neurotrauma clinicians’ perspectives on the contextual challenges associated with long-term follow-up following traumatic brain injury in low-income and middle-income countries: a qualitative study protocol

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e041442
Author(s):  
Brandon George Smith ◽  
Charlotte Jane Whiffin ◽  
Ignatius N Esene ◽  
Claire Karekezi ◽  
Tom Bashford ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Qualitative Study ◽  
Health Concern ◽  
Middle Income ◽  
Middle Income Countries ◽  
Semistructured Interviews ◽  
Long Term Follow Up

IntroductionTraumatic brain injury (TBI) is a global public health concern; however, low/middle-income countries (LMICs) face the greatest burden. The WHO recognises the significant differences between patient outcomes following injuries in high-income countries versus those in LMICs. Outcome data are not reliably recorded in LMICs and despite improved injury surveillance data, data on disability and long-term functional outcomes remain poorly recorded. Therefore, the full picture of outcome post-TBI in LMICs is largely unknown.Methods and analysisThis is a cross-sectional pragmatic qualitative study using individual semistructured interviews with clinicians who have experience of neurotrauma in LMICs. The aim of this study is to understand the contextual challenges associated with long-term follow-up of patients following TBI in LMICs. For the purpose of the study, we define ‘long-term’ as any data collected following discharge from hospital. We aim to conduct individual semistructured interviews with 24–48 neurosurgeons, beginning February 2020. Interviews will be recorded and transcribed verbatim. A reflexive thematic analysis will be conducted supported by NVivo software.Ethics and disseminationThe University of Cambridge Psychology Research Ethics Committee approved this study in February 2020. Ethical issues within this study include consent, confidentiality and anonymity, and data protection. Participants will provide informed consent and their contributions will be kept confidential. Participants will be free to withdraw at any time without penalty; however, their interview data can only be withdrawn up to 1 week after data collection. Findings generated from the study will be shared with relevant stakeholders such as the World Federation of Neurosurgical Societies and disseminated in conference presentations and journal publications.

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