Epidemiology of traumatic brain injury older inpatients in Chinese military hospitals, 2001–2007

Predicting hospital outcomes in concussion and TBI: A mixed-effects analysis utilizing the nationwide readmissions database

BACKGROUND AND OBJECTIVES

Traumatic brain injury (TBI) is characterized by a wide range in severity. This variation presents a challenge for predicting outcomes and making management decisions, particularly for patients sustaining less severe injury. We present a novel statistical model for the prediction of hospital outcomes in two propensity-matched cohorts to optimize TBI patient management and counseling.

METHODS

Hospitalized patients diagnosed with TBI were selected from the Nationwide Readmissions Database (NRD) from 2010 to 2019 using ICD-9 and ICD-10 codes. Using propensity score matching for baseline characteristics, patients were sorted by GCS score into two cohorts: 1188 patients with mild to moderate TBI (mTBI, GCS > 8) and 1219 patients with severe TBI (sTBI, GCS ≤ 8). Mixed-effects modeling was implemented, and model performance was evaluated using the Area Under the Curve (AUC). Any variance in ROC model prediction between cohorts was compared using DeLong's test.

RESULTS

After bivariate analysis, the mean length of stay (LOS), hospital cost, and mortality were significantly lower in the mTBI cohort relative to sTBI. GCS scores within the range of 9-15 were predictive of LOS (p < 0.01), with a trend towards significance in the prediction of non-routine discharge (p = 0.06).

CONCLUSION

Using an advanced mixed-effects model, our study found that GCS is an accurate predictor of hospital outcomes after a TBI diagnosis. These results provide insight that may aid in the development of preventative strategies, management decisions, and patient counseling to ensure a safe return to daily life for patients diagnosed with concussion.

Predictive Modeling of Long-Term Care Needs in Traumatic Brain Injury Patients Using Machine Learning

BACKGROUND

Traumatic brain injury (TBI) research often focuses on mortality rates or functional recovery, yet the critical need for long-term care among patients dependent on institutional or Respiratory Care Ward (RCW) support remains underexplored. This study aims to address this gap by employing machine learning techniques to develop and validate predictive models that analyze the prognosis of this patient population.

METHOD

Retrospective data from electronic medical records at Chi Mei Medical Center, encompassing 2020 TBI patients admitted to the ICU between January 2016 and December 2021, were collected. A total of 44 features were included, utilizing four machine learning models and various feature combinations based on clinical significance and Spearman correlation coefficients. Predictive performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated with the DeLong test and SHAP (SHapley Additive exPlanations) analysis.

RESULT

Notably, 236 patients (11.68%) were transferred to long-term care centers. XGBoost with 27 features achieved the highest AUC (0.823), followed by Random Forest with 11 features (0.817), and LightGBM with 44 features (0.813). The DeLong test revealed no significant differences among the best predictive models under various feature combinations. SHAP analysis illustrated a similar distribution of feature importance for the top 11 features in XGBoost, with 27 features, and Random Forest with 11 features.

CONCLUSIONS

Random Forest, with an 11-feature combination, provided clinically meaningful predictive capability, offering early insights into long-term care trends for TBI patients. This model supports proactive planning for institutional or RCW resources, addressing a critical yet often overlooked aspect of TBI care.

Traumatic brain injury in elderly population: A global systematic review and meta-analysis of in-hospital mortality and risk factors among 2.22 million individuals

BACKGROUND

Traumatic brain injury (TBI) among elderly individuals poses a significant global health concern due to the increasing ageing population.

METHODS

We searched PubMed, Cochrane Library, and Embase from database inception to Feb 1, 2024. Studies performed in inpatient settings reporting in-hospital mortality of elderly people (≥60 years) with TBI and/or identifying risk factors predictive of such outcomes, were included. Data were extracted from published reports, in-hospital mortality as our main outcome was synthesized in the form of rates, and risk factors predicting in-hospital mortality was synthesized in the form of odds ratios. Subgroup analyses, meta-regression and dose-response meta-analysis were used in our analyses.

FINDINGS

We included 105 studies covering 2217,964 patients from 30 countries/regions. The overall in-hospital mortality of elderly patients with TBI was 16 % (95 % CI 15 %-17 %) from 70 studies. In-hospital mortality was 5 % (95 % CI, 3 %-7 %), 18 % (95 % CI, 12 %-24 %), 65 % (95 % CI, 59 %-70 %) for mild, moderate and severe subgroups from 10, 7, and 23 studies, respectively. A decrease in in-hospital mortality over years was observed in overall (1981-2022) and in severe (1986-2022) elderly patients with TBI. Older age 1.69 (95 % CI, 1.58-1.82, P < 0.001), male gender 1.34 (95 % CI, 1.25-1.42, P < 0.001), clinical conditions including traffic-related cause of injury 1.22 (95 % CI, 1.02-1.45, P = 0.029), GCS moderate (GCS 9-12 compared to GCS 13-15) 4.33 (95 % CI, 3.13-5.99, P < 0.001), GCS severe (GCS 3-8 compared to GCS 13-15) 23.09 (95 % CI, 13.80-38.63, P < 0.001), abnormal pupillary light reflex 3.22 (95 % CI, 2.09-4.96, P < 0.001), hypotension after injury 2.88 (95 % CI, 1.06-7.81, P = 0.038), polytrauma 2.31 (95 % CI, 2.03-2.62, P < 0.001), surgical intervention 2.21 (95 % CI, 1.22-4.01, P = 0.009), pre-injury health conditions including pre-injury comorbidity 1.52 (95 % CI, 1.24-1.86, P = 0.0020), and pre-injury anti-thrombotic therapy 1.51 (95 % CI, 1.23-1.84, P < 0.001) were related to higher in-hospital mortality in elderly patients with TBI. Subgroup analyses according to multiple types of anti-thrombotic drugs with at least two included studies showed that anticoagulant therapy 1.70 (95 % CI, 1.04-2.76, P = 0.032), Warfarin 2.26 (95 % CI, 2.05-2.51, P < 0.001), DOACs 1.99 (95 % CI, 1.43-2.76, P < 0.001) were related to elevated mortality. Dose-response meta-analysis of age found an odds ratio of 1.029 (95 % CI, 1.024-1.034, P < 0.001) for every 1-year increase in age on in-hospital mortality.

CONCLUSIONS

In the field of elderly patients with TBI, the overall in-hospital mortality and its temporal-spatial feature, the subgroup in-hospital mortalities according to injury severity, and dose-response meta-analysis of age were firstly comprehensively summarized. Substantial key risk factors, including the ones previously not elucidated, were identified. Our study is thus of help in underlining the importance of treating elderly TBI, providing useful information for healthcare providers, and initiating future management guidelines. This work underscores the necessity of integrating elderly TBI treatment and management into broader health strategies to address the challenges posed by the aging global population.

REVIEW REGISTRATION

PROSPERO CRD42022323231.

Traumatic Brain Injury in Select Low- and Middle-Income Countries: A Narrative Review of the Literature

Low- and middle-income countries (LMICs) experience the majority of traumatic brain injuries (TBIs), yet few studies have examined the epidemiology and management strategies of TBI in LMICs. The objective of this narrative review is to discuss the epidemiology of TBI within LMICs, describe the adherence to Brain Trauma Foundation (BTF) guidelines for the management of severe TBI in LMICs, and document TBI management strategies currently used in LMICs. Articles from January 1, 2009 to September 30, 2021 that included patients with TBI greater than 18 years of age in low-, low middle-, and high middle-income countries were queried in PubMed. Search results demonstrated that TBI in LMICs mostly impacts young males involved in road traffic accidents. Within LMICs there are a myriad of approaches to managing TBI with few randomized controlled trials performed within LMICs to evaluate those interventions. More studies are needed in LMICs to establish the effectiveness and appropriateness of BTF guidelines for managing TBI and to help identify methods for managing TBI that are appropriate in low-resource settings. The problem of limited pre- and post-hospital care is a bigger challenge that needs to be considered while addressing management of TBI in LMICs.

Predictive Factors of Return Home and Return to Work for Intensive Care Unit Survivors after Traumatic Brain Injury with a Follow-up Period of 2 Years

Intensive care unit (ICU) survivors after traumatic brain injury (TBI) frequently have serious disabilities with subsequent difficulty in reintegration into society. We aimed to investigate outcomes for ICU survivors after moderate to severe TBI (msTBI) and to identify predictive factors of return home (RH) and return to work (RTW). This single-center retrospective cohort study was conducted on all trauma patients admitted to the emergency ICU of our hospital between 2013 and 2017. Of these patients, adult (age ≥ 18 years) msTBI patients with head Abbreviated Injury Scale ≥ 3 were extracted. We performed univariate/multivariate logistic regression analyses to explore the predictive factors of RH and RTW. Among a total of 146 ICU survivors after msTBI, 107 were included (median follow-up period: 26 months). The RH and RTW rates were 78% and 35%, respectively. Multivariate analyses revealed that the predictive factors of RH were age < 65 years (P < 0.001), HR < 76 bpm (P = 0.015), platelet count ≥ 19 × 10⁴/μL (P = 0.0037), D-dimer < 26 μg/mL (P = 0.034), and Glasgow Coma Scale (GCS) score > 8 (P = 0.0015). Similarly, the predictive factors of RTW were age < 65 years (P < 0.001) and GCS score > 8 (P = 0.0039). This study revealed that “age” and “GCS score on admission” affected RH and RTW for ICU survivors after msTBI.

Reliability and validity of the Rowland Universal Dementia Assessment Scale for patients with traumatic brain injury

Objective and accurate cognitive assessment scales are essential for guiding cognitive rehabilitation following traumatic brain injury (TBI). The aim of this study was to evaluate the reliability and validity of the Rowland Universal Dementia Assessment Scale (RUDAS) for TBI and to verify the clinical application value. Fifty patients with TBI and 32 matched controls were assessed using the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and a newly developed Chinese version of RUDAS. These scales were then compared for internal consistency, inter-rater reliability, test‒retest reliability, content validity, construct validity, and diagnostic efficacy. Among the TBI group, the RUDAS demonstrated acceptable internal consistency (Cronbach's α = 0.733), high inter-rater reliability (intraclass correlation coefficients [ICCs] of 0.910‒0.999), and high test‒retest reliability (total score ICC = 0.938). The correlation coefficients between RUDAS total score and individual subscores were all > 0.5 except for body orientation (r = 0.363), indicating generally good content validity. Total RUDAS scores were moderately correlated with both MMSE total scores (r = 0.701, p < 0.001) and MoCA total scores (r = 0.778, p < 0.001), indicating good construct validity. Receiving operating characteristic curve analysis yielded comparable areas under the curve for diagnostic efficacy (RUDAS, 0.844; MMSE, 0.769; MoCA, 0.824; all p > 0.05). A RUDAS score cutoff of 23.5 distinguished TBI patients from controls with 60% sensitivity and 100% specificity. Therefore, the RUDAS demonstrates both good reliability and validity for evaluating cognitive impairments in TBI patients.

Prediction of functional outcome and discharge destination in patients with traumatic brain injury after post-acute rehabilitation

As the survival rate of traumatic brain injury increases, the burden of care for patients with traumatic brain injury is emerging as a socioeconomic issue and the discharge destination is one of the important outcome measures in the post-acute rehabilitation unit. To investigate the predictors of functional outcome and discharge destination in patients with traumatic brain injury after post-acute rehabilitation. A retrospective review was performed on 86 patients who were admitted to the rehabilitation unit between January 2010 and June 2017. Multiple regression analysis was used as a statistical method to identify the factors affecting Modified Barthel Index and discharge destination. The number of days from traumatic brain injury onset to rehabilitation unit admission (odds ratio = 0.959, P = 0.049), brain surgery for traumatic brain injury management (odds ratio = 0.160, P = 0.021), initial Glasgow Coma Scale score (odds ratio = 1.269, P = 0.022) and Mini-Mental State Examination score at admission (odds ratio = 1.245, P < 0.001) were the predictive factors for higher Modified Barthel Index after rehabilitation. Underlying vascular risk factors (odds ratio = 0.138, P = 0.015), Modified Barthel Index score after rehabilitation (odds ratio = 1.085, P < 0.001) and deductible-free insurance (odds ratio = 0.211, P = 0.032) were the predictive factors of home discharge. The functional outcome of patients with traumatic brain injury after rehabilitation was related to the severity of initial injury, cognitive function at admission and rehabilitation timing. The discharge destination after rehabilitation was related to functional outcome, insurance issues and underlying vascular risk factors.

In-hospital costs after severe traumatic brain injury: A systematic review and quality assessment

Background

The in-hospital treatment of patients with traumatic brain injury (TBI) is considered to be expensive, especially in patients with severe TBI (s-TBI). To improve future treatment decision-making, resource allocation and research initiatives, this study reviewed the in-hospital costs for patients with s-TBI and the quality of study methodology.

Methods

A systematic search was performed using the following databases: PubMed, MEDLINE, Embase, Web of Science, Cochrane library, CENTRAL, Emcare, PsychINFO, Academic Search Premier and Google Scholar. Articles published before August 2018 reporting in-hospital acute care costs for patients with s-TBI were included. Quality was assessed by using a 19-item checklist based on the CHEERS statement.

Results

Twenty-five out of 2372 articles were included. In-hospital costs per patient were generally high and ranged from $2,130 to $401,808. Variation between study results was primarily caused by methodological heterogeneity and variable patient and treatment characteristics. The quality assessment showed variable study quality with a mean total score of 71% (range 48% - 96%). Especially items concerning cost data scored poorly (49%) because data source, cost calculation methodology and outcome reporting were regularly unmentioned or inadequately reported.

Conclusions

Healthcare consumption and in-hospital costs for patients with s-TBI were high and varied widely between studies. Costs were primarily driven by the length of stay and surgical intervention and increased with higher TBI severity. However, drawing firm conclusions on the actual in-hospital costs of patients sustaining s-TBI was complicated due to variation and inadequate quality of the included studies. Future economic evaluations should focus on the long-term cost-effectiveness of treatment strategies and use guideline recommendations and common data elements to improve study quality.

Compare the effect of noninvasive ventilation and tracheotomy in critically ill mechanically ventilated neurosurgical patients: a retrospective observe cohort study

Objective

Patients with severe brain injury is usual at high risk of extubation failure, despite of those with no/minor primary respiratory problem, majority of them still needs long term respiratory support and has severe pulmonary complications. This retrospective study aimed to compare the effect of noninvasive ventilation (NIV) and tracheotomy on the prognosis in critically ill mechanically ventilated neurosurgical patients.

Methods

This is a single center, retrospective observe cohort study. Postoperative patients with brain injury consecutively admitted to ICU from November 1st, 2015 through February 28th, 2017, who had received invasive mechanical ventilation more than 48 h were screened, those who received NIV or tracheotomy procedure, meanwhile with Glasgow Coma Scale (GCS) score between 8 and 13 points before using NIV or undergoing tracheotomy, were retrospectively included in this study. The demographic data and clinical main outcomes such as ICU and hospital mortality, time of mechanical ventilation, length of ICU and hospital were collected. The primary outcome was the incidence of postoperative pulmonary infection between two groups.

Results

77 patients were included in this study. 33 patients received NIV, and 44 patients received tracheotomy through the ICU duration. The incidence of postoperative pulmonary infection in NIV group was significantly lower than that in tracheotomy group (54.5% VS 84.1%, P < 0.05), Application of NIV was associated with shorter duration of invasive mechanical ventilation ([median 123.0 h VS 195.0 h, P < 0.05). Moreover, GCS score at ICU discharge, as well as the difference of GCS score between at admission to ICU and ICU discharge were also better than the tracheotomy group (P < 0.001).

Conclusion

Compared with tracheotomy, use of NIV after extubation in critically ill mechanically ventilated neurosurgical patients may be associated with lower incidence of postoperative pulmonary infection, shorter duration of invasive mechanical ventilation and better improvement in brain function. Further studies need to verify the effect of NIV in this kind of patients.

Unintentional injuries: A profile of hospitalization and risk factors for in-hospital mortality in Beijing, China

INTRODUCTION

Unintentional injuries (UIs) impose a significant burden on low- and middle-income countries (LMICs). However, available UI epidemiological data are limited for LMICs, including China. This article aimed to provide an overview of the UI hospitalization profile, identify risk factors for in-hospital mortality and provide diagnosis-specific survival risk ratios (SRRs) for reference by LMICs using hospital discharge abstract data (DAD) from Beijing, China.

PATIENTS AND METHODS

A cross-sectional study was conducted for patients sustaining UIs requiring admission. Information was retrieved from 138 hospitals in Beijing to describe the demographics, injury nature, mechanisms, severity and hospital outcomes. Multivariate logistic regression was performed to identify and evaluate risk factors for in-hospital mortality for UIs.

RESULTS

Falls (57.1%), transport accidents (19.9%) and exposure to inanimate mechanical forces (16.4%) were the leading causes of UI hospitalization. Falls and transport accidents were responsible for 94.2% of the in-hospital deaths caused by UIs. Injury mechanisms differed among sex (χ² = 5322.1, P <  0.001) and age (χ² = 24,143.3, P <  0.001) groups. Male sex (OR: 1.50, 95% confidence interval (CI): 1.23-1.79), age ≥ 85 years (OR: 16.39, 95% CI: 7.46-36.00), Barthel Index at admission ≤ 60 (OR: 25.78, 95% CI: 13.30-49.95), modified Charlson comorbidity index ≥ 6 (OR: 2.60, 95% CI: 1.91-3.55), International Classification of Diseases-based injury severity score (ICISS) < 0.85 (OR: 15.17, 95% CI: 12.57-18.30), sustaining injuries to the head/neck (OR: 23.20, 95% CI: 7.31-73.64), injuries caused by foreign body entering through natural orifice (OR: 34.00, 95%CI: 6.37-181.54) and injuries resulting from transport accidents (OR: 1.71, 95% CI: 1.41-2.07) were important risk factors for in-hospital mortality for UIs.

CONCLUSIONS

Hospital DAD are an objective and cost-effective data source that allows for a hospital-based perspective of UI epidemiology. Sex, age, functional status at admission, comorbidities, injury nature, severity and mechanism are significantly associated with the in-hospital mortality of UIs in China. This study generates a reference dataset of diagnosis-specific SRRs from a large trauma population in China, which may be more applicable in injury severity estimation using ICISS in LMICs.

Traumatic brain injury in China

China has more patients with traumatic brain injury (TBI) than most other countries in the world, making this condition a major public health concern. Population-based mortality of TBI in China is estimated to be approximately 13 cases per 100 000 people, which is similar to the rates reported in other countries. The implementation of various measures, such as safety legislation for road traffic, establishment of specialised neurosurgical intensive care units, and the development of evidence-based guidelines, have contributed to advancing prevention and care of patients with TBI in China. However, many challenges remain, which are augmented further by regional differences in TBI care. High-level care, such as intracranial pressure monitoring, is not universally available yet. In the past 30 years, the quality of TBI research in China has substantially improved, as evidenced by an increasing number of clinical trials done. The large number of patients with TBI and specialised trauma centres offer unique opportunities for TBI research in China. Furthermore, the formation and development of research collaborations between China and international groups are considered essential to advancing the quality of TBI care and research in China, and to improve quality of life in patients with this condition.