Effects of hospital-acquired pneumonia on long-term recovery and hospital resource utilization following moderate to severe traumatic brain injury

Predicting nosocomial pneumonia risk in level-1 trauma patients: An external validation study using the trauma quality improvement program

BACKGROUND

Early identification of patients at risk of nosocomial pneumonia enables the opportunity for preventative measures, which may improve survival and reduce costs. Therefore, this study aimed to externally validate an existing prediction model (issued by Croce et al.) to predict nosocomial pneumonia in patients admitted to US level-1 trauma centers.

METHODS

A retrospective cohort study including patients admitted to level-1 trauma centers and registered in the TQIP, a US nationwide trauma registry, admitted between 2013-2015 and 2017-2019. The main outcome was total nosocomial pneumonia for the first period and ventilator-associated pneumonia (VAP) for the second. Model discrimination and calibration were assessed before and after recalibration.

RESULTS

The study comprised 902,231 trauma patients (N2013-2015 ​= ​180,601; N2017-2019 ​= ​721,630), with a median age of 52 in both periods, 64-65 ​% male, and approximately 90 ​% sustaining blunt traumatic injury. The median Injury Severity Scores were 13 (2013-2015) versus 9 (2017-2019); median Glasgow Coma Scale scores were 15. Nosocomial pneumonia incidence was 4.4 ​%, VAP incidence was 0.7 ​%. The original model demonstrated good to excellent discrimination for both periods (c-statistic2013-2015 0.84, 95%CI 0.83-0.84; c-statistic2017-2019 0.92, 95%CI 0.91-0.92). After recalibration, discriminatory capacity and calibration for the lower predicted probabilities improved.

CONCLUSIONS

The Croce model can identify patients admitted to US level-1 trauma centers at risk of total nosocomial pneumonia and VAP. Implementing (modified) Croce models in route trauma clinical practice could guide judicious use of preventative measures and prescription of additional non-invasive preventative measures (e.g., increased monitoring, pulmonary physiotherapy) to decrease the occurrence of nosocomial pneumonia in at-risk patients.

Association of Early Dexmedetomidine Utilization With Clinical Outcomes After Moderate-Severe Traumatic Brain Injury: A Retrospective Cohort Study

BACKGROUND

Traumatic brain injury (TBI) is an expensive and common public health problem. Management of TBI oftentimes includes sedation to facilitate mechanical ventilation (MV) for airway protection. Dexmedetomidine has emerged as a potential candidate for improved patient outcomes when used for early sedation after TBI due to its potential modulation of autonomic dysfunction. We examined early sedation patterns, as well as the association of dexmedetomidine exposure with clinical and functional outcomes among mechanically ventilated patients with moderate-severe TBI (msTBI) in the United States.

METHODS

We conducted a retrospective cohort study using data from the Premier dataset and identified a cohort of critically ill adult patients with msTBI who required MV from January 2016 to June 2020. msTBI was defined by head-neck abbreviated injury scale (AIS) values of 3 (serious), 4 (severe), and 5 (critical). We described early continuous sedative utilization patterns. Using propensity-matched models, we examined the association of early dexmedetomidine exposure (within 2 days of intensive care unit [ICU] admission) with the primary outcome of hospital mortality and the following secondary outcomes: hospital length of stay (LOS), days on MV, vasopressor use after the first 2 days of admission, hemodialysis (HD) after the first 2 days of admission, hospital costs, and discharge disposition. All medications, treatments, and procedures were identified using date-stamped hospital charge codes.

RESULTS

The study population included 19,751 subjects who required MV within 2 days of ICU admission. The patients were majority male and white. From 2016 to 2020, the annual percent utilization of dexmedetomidine increased from 4.05% to 8.60%. After propensity score matching, early dexmedetomidine exposure was associated with reduced odds of hospital mortality (odds ratio [OR], 0.59; 95% confidence interval [CI], 0.47-0.74; P < .0001), increased risk for liberation from MV (hazard ratio [HR], 1.20; 95% CI, 1.09-1.33; P = .0003), and reduced LOS (HR, 1.11; 95% CI, 1.01-1.22; P = .033). Exposure to early dexmedetomidine was not associated with odds of HD (OR, 1.14; 95% CI, 0.73-1.78; P = .56), vasopressor utilization (OR, 1.10; 95% CI, 0.78-1.55; P = .60), or increased hospital costs (relative cost ratio, 1.98; 95% CI, 0.93-1.03; P = .66).

CONCLUSIONS

Dexmedetomidine is being utilized increasingly as a sedative for mechanically ventilated patients with msTBI. Early dexmedetomidine exposure may lead to improved patient outcomes in this population.

Hospital-acquired infections as a risk factor for post-traumatic epilepsy: A registry-based cohort study

OBJECTIVE

Hospital-acquired infections are a common complication for patients with moderate or severe traumatic brain injury (TBI), contributing to morbidity and mortality. As infection-mediated immune responses can predispose towards epilepsy, we hypothesized that post-injury hospital-acquired infections increase the risk of post-traumatic epilepsy (PTE).

METHODS

A retrospective cohort study of adults with moderate to severe TBI was conducted using data from the Victorian State Trauma Registry in Australia. Infections were identified from the International Statistical Classification of Diseases and Related Health Problems 10th Revision-Australian Modification (ICD-10-AM) codes, and diagnosis of PTE was determined by the Glasgow Outcome Scale - Extended questionnaire regarding epileptic fits at 24 months follow-up.

RESULTS

Of all TBI patients (n = 15 152), 24% had evidence of having had any type of infection, with the most common being pneumonia, urinary tract, and respiratory infections. Of those who responded to the PTE question at 24 months (n = 1361), 11% had developed PTE. Univariable analysis found that the incidence of PTE was higher in patients who had any type of infection compared to patients without an infection (p < 0.001). After adjustment for covariates associated with both development of PTE and risk of infection, multivariable analysis found a solid association between infection and PTE (adjusted RR = 1.59; 95% CI: 1.11-2.28; p = 0.011). Having any type of complicating infection acquired during admission was also associated with poor GOSE outcomes at subsequent follow-ups (adjusted OR = 0.20; 95% CI: 0.11-0.35, p < 0.001).

SIGNIFICANCE

These findings suggest that hospital-acquired infections contribute to PTE development after TBI. Future investigation into infections as a modifiable target to reduce poor outcomes after TBI is warranted.

PLAIN LANGUAGE SUMMARY

Hospital-acquired infections are common in patients with traumatic brain injuries. A database study of adults with moderate or severe brain injuries in Australia examined whether these infections are associated with the development of epilepsy after a brain injury. 24% of patients had infections, with pneumonia and urinary tract infections being the most common. Of those surveyed 2 years after the injury, 11% developed post-traumatic epilepsy. Patients with infections had a significantly higher risk of epilepsy, even when accounting for other known risk factors, and infections were also linked to poor outcomes more broadly. The study suggests that preventing hospital-acquired infections could be a crucial target for improving outcomes after traumatic brain injuries.

Modelling lung infection with Klebsiella pneumoniae after murine traumatic brain injury

Pneumonia is a common comorbidity in patients with severe traumatic brain injury (TBI), and is associated with increased morbidity and mortality. In this study, we established a model of intratracheal Klebsiella pneumoniae administration in young adult male and female mice, at 4 days following an experimental TBI, to investigate how K. pneumoniae infection influences acute post-TBI outcomes. A dose-response curve determined the optimal dose of K. pneumoniae for inoculation (1 x 10^6 colony forming units), and administration at 4 days post-TBI resulted in transient body weight loss and sickness behaviors (hypoactivity and acute dyspnea). K. pneumoniae infection led to an increase in pro-inflammatory cytokines in serum and bronchoalveolar lavage fluid at 24 h post-infection, in both TBI and sham (uninjured) mice. By 7 days, when myeloperoxidase + neutrophil numbers had returned to baseline in all groups, lung histopathology was observed with an increase in airspace size in TBI + K. pneumoniae mice compared to TBI + vehicle mice. In the brain, increased neuroinflammatory gene expression was observed acutely in response to TBI, with an exacerbated increase in Ccl2 and Hmox1 in TBI + K. pneumoniae mice compared to either TBI or K. pneumoniae alone. However, the presence of neuroinflammatory immune cells in the injured brain, and the extent of damage to cortical and hippocampal brain tissue, was comparable between K. pneumoniae and vehicle-treated mice by 7 days. Examination of the fecal microbiome across a time course did not reveal any pronounced effects of either injury or K. pneumoniae on bacterial diversity or abundance. Together, these findings demonstrate that K. pneumoniae lung infection after TBI induces an acute and transient inflammatory response, primarily localized to the lungs with some systemic effects. However, this infection had minimal impact on secondary injury processes in the brain following TBI. Future studies are needed to evaluate the potential longer-term consequences of this dual-hit insult.

Factors Associated With Time to Decannulation in Patients With Tracheostomy Following Severe Traumatic Brain Injury

BACKGROUND

Prolonged tracheal tube placement following severe traumatic brain injury (TBI) can cause serious complications. Safe removal requires sufficient ability for independent breathing and airway protection. Thus, identifying important factors for time to removal of the tracheal tube (decannulation) is essential for safe and efficient weaning. This study aimed to identify significant factors for time to decannulation in a Danish population of subjects with tracheostomy after TBI.

METHODS

This was a retrospective register-based cohort study. Subjects with moderate and severe TBI and a tracheal tube were selected from the Danish Head Trauma Database between 2011-2021. Time to decannulation was calculated as time from injury to decannulation. Associations between selected explanatory variables representing demographic and clinical characteristics and time to decannulation were analyzed using linear regression models.

RESULTS

A total 324 subjects were included with a median of 44 d to decannulation. Primary analysis found that an improvement in swallowing ability during the initial 4 weeks of rehabilitation was associated with an 8.2 d reduction in time to decannulation (95% CI -12.3 to -4.2, P < .001). Change in overall sensorimotor ability reduced time to decannulation by 0.94 (95% CI -0.78 to -0.10, P = .03) d. Change in cognitive abilities from rehab admission to 4-week follow-up did not significantly affect the number of days to decannulation (P = .66). Secondary analysis showed pneumonia was associated with the largest estimated increase of 24.4 (95% CI 15.9-32.9, P < .001) d and that increased cognitive functioning at rehabilitation admission was associated with a significant reduction in time to decannulation.

CONCLUSIONS

This study found that a change in swallowing ability is a potentially significant factor for reducing time to decannulation. Identifying factors that could explain differences in time to decannulation is essential for patient outcomes, especially if these factors are modifiable and could be targeted in rehabilitation and treatment.

Experimental Models of Hospital-Acquired Infections After Traumatic Brain Injury: Challenges and Opportunities

Patients hospitalized after a moderate or severe traumatic brain injury (TBI) are at increased risk of nosocomial infections, including bacterial pneumonia and other upper respiratory tract infections. Infections represent a secondary immune challenge for vulnerable TBI patients that can lead to increased morbidity and poorer long-term prognosis. This review first describes the clinical significance of infections after TBI, delving into the known mechanisms by which a TBI can alter systemic immunological responses towards an immunosuppressive state, leading to promotion of increased vulnerability to infections. Pulmonary dysfunction resulting from respiratory tract infections is considered in the context of neurotrauma, including the bidirectional relationship between the brain and lungs. Turning to pre-clinical modeling, current laboratory approaches to study experimental TBI and lung infections are reviewed, to highlight findings from the limited key studies to date that have incorporated both insults. Then, practical decisions for the experimental design of animal studies of post-injury infections are discussed. Variables associated with the host animal, the infectious agent (e.g., species, strain, dose, and administration route), as well as the timing of the infection relative to the injury model are important considerations for model development. Together, the purpose of this review is to highlight the significant clinical need for increased pre-clinical research into the two-hit insult of a hospital-acquired infection after TBI to encourage further scientific enquiry in the field.

Construction and validation of a predictive model of pneumonia for ICU patients with traumatic brain injury (TBI)

The incidence of pneumonia in ICU patients with TBI is very high, seriously affecting the prognosis. This study aims to construct a predictive model for pneumonia in ICU patients with TBI and provide help for the prevention of TBI-related pneumonia.Clinical data of ICU patients with TBI were collected from the Medical Information Mart for Intensive Care (MIMIC)-IV database and hospital data. Variables were screened by lasso and multivariate logistic regression to construct a predictive nomogram model, verified in internal validation cohort and external validation cohort by receiver operator characteristic (ROC) curve, calibration curve and decision curve analysis (DCA).A total of 1850 ICU patients with TBI were enrolled in the study from the MIMIC-IV database, including 1298 in the training cohort and 552 in internal validation cohort. The external validation cohort included 240 ICU patients with TBI from hospital data. Nine variables were selected from the training cohort by lasso regression and multivariate logistic regression, and a pneumonia prediction nomogram was constructed. This nomogram has a high discrimination in training, internal validation and external validation cohorts (AUC = 0.857, 0.877, 0.836). The calibration curve and DCA showed that this nomogram had a high calibration and better clinical decision-making efficiency.The nomogram showed excellent discrimination and clinical utility to predict pneumonia, and could identify pneumonia high-risk patients early, thus providing personalised treatment strategies for ICU patients with TBI.

Predictors and Impact of Pneumonia on Adverse Outcomes in Inhalation Injury Patients

Inhalation injury (II) is the third mortality prognostic factor for burn injury following age and burn size. II can lead to pulmonary complications such as pneumonia and acute respiratory distress syndrome (ARDS); all of which have been hypothesized to increase morbidity and mortality in II. Herein, we aimed to identify variables associated with the risk of developing pneumonia and to determine the impact of pneumonia on selected II outcomes. De-identified data from the Prospective Inhalation Study titled Inhalation Injury Scoring System to Predict Inhalation Injury Severity (ISIS) were used. II was confirmed by fiberoptic bronchoscopy. Demographics, injury, and hospital course information were recorded. P < .05 was considered significant. One hundred subjects were included. On univariate analysis, pneumonia was associated with burn severity, race, and receipt of colloid during the first 24 hours. Patients who developed pneumonia spent more time on a ventilator, had longer hospitalizations (LOS) and were more likely to need a tracheostomy. On multivariate analysis, total number of ventilator days was associated with pneumonia (Odd ratio (OR) = 1.122 [1.048-1.200], P = .001). Both pneumonia and receipt of colloid were predictive of increased ventilator days (OR = 2.545 [1.363-4.753], P < .001 and OR = 2.809 [1.548-5.098], P < .001, respectively). Pneumonia was not an independent predictor of LOS, ARDS, or mortality. Pneumonia remains a high-risk complication associated with two times more ventilator days in II. Future research should focus on prevention of pneumonia and the relationship between colloid fluids and pneumonia and early ventilator liberation in II patients.

Machine learning algorithms for prediction of ventilator associated pneumonia in traumatic brain injury patients from the MIMIC-III database

BACKGROUND

Ventilator associated pneumonia (VAP) is a common complication and associated with poor prognosis of traumatic brain injury (TBI) patients.

OBJECTIVES

This study was conducted to explore the predictive performance of different machine-learning algorithms for VAP in TBI patients.

METHODS

TBI patients receiving mechanical ventilation more than 48 hours from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were eligible for the study. The VAP was confirmed based on the ICD-9 code. Included patients were separated to the training cohort and the validation cohort with a ratio of 7:3. Predictive models based on different machine learning algorithms were developed using 5-fold cross validation in the training cohort and then verified in the validation cohort by evaluating the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy and F score.

RESULTS

786 TBI patients from the MIMIC-III were finally included with the VAP incidence of 44.0%. The random forest performed the best on predicting VAP in the training cohort with a AUC of 1.000. The XGBoost and AdaBoost were ranked the second and the third with a AUC of 0.915 and 0.789 in the training cohort. While the AdaBoost performed the best on predicting VAP in the validation cohort with a AUC of 0.706. The XGBoost and random forest were ranked the second and the third with the AUC of 0.685 and 0.683 in the validation cohort. Generally, the random forest and XGBoost were likely to be over-fitting while the AdaBoost was relatively stable in predicting the VAP.

CONCLUSIONS

The AdaBoost performed well and stably on predicting the VAP in TBI patients. Developing programs using AdaBoost in portable electronic devices may effectively assist physicians in assessing the risk of VAP in TBI.

A brain CT-based approach for predicting and analyzing stroke-associated pneumonia from intracerebral hemorrhage

Introduction

Stroke-associated pneumonia (SAP) is a common complication of stroke that can increase the mortality rate of patients and the burden on their families. In contrast to prior clinical scoring models that rely on baseline data, we propose constructing models based on brain CT scans due to their accessibility and clinical universality.

Methods

Our study aims to explore the mechanism behind the distribution and lesion areas of intracerebral hemorrhage (ICH) in relation to pneumonia, we utilized an MRI atlas that could present brain structures and a registration method in our program to extract features that may represent this relationship. We developed three machine learning models to predict the occurrence of SAP using these features. Ten-fold cross-validation was applied to evaluate the performance of models. Additionally, we constructed a probability map through statistical analysis that could display which brain regions are more frequently impacted by hematoma in patients with SAP based on four types of pneumonia.

Results

Our study included a cohort of 244 patients, and we extracted 35 features that captured the invasion of ICH to different brain regions for model development. We evaluated the performance of three machine learning models, namely, logistic regression, support vector machine, and random forest, in predicting SAP, and the AUCs for these models ranged from 0.77 to 0.82. The probability map revealed that the distribution of ICH varied between the left and right brain hemispheres in patients with moderate and severe SAP, and we identified several brain structures, including the left-choroid-plexus, right-choroid-plexus, right-hippocampus, and left-hippocampus, that were more closely related to SAP based on feature selection. Additionally, we observed that some statistical indicators of ICH volume, such as mean and maximum values, were proportional to the severity of SAP.

Discussion

Our findings suggest that our method is effective in classifying the development of pneumonia based on brain CT scans. Furthermore, we identified distinct characteristics, such as volume and distribution, of ICH in four different types of SAP.

A predictive model using risk factor categories for hospital-acquired pneumonia in patients with aneurysmal subarachnoid hemorrhage

Objectives

To identify risk factors for hospital-acquired pneumonia (HAP) in patients with aneurysmal subarachnoid hemorrhage (aSAH) and establish a predictive model to aid evaluation.

Methods

The cohorts of 253 aSAH patients were divided into the HAP group (n = 64) and the non-HAP group (n = 189). Univariate and multivariate logistic regression were performed to identify risk factors. A logistic model (Model-Logit) was established based on the independent risk factors. We used risk factor categories to develop a model (Model-Cat). Receiver operating characteristic curves were generated to determine the cutoff values. Areas under the curves (AUCs) were calculated to assess the accuracy of models and single factors. The Delong test was performed to compare the AUCs.

Results

The multivariate logistic analysis showed that the age [p = 0.012, odds ratio (OR) = 1.059, confidence interval (CI) = 1.013-1.107], blood glucose (BG; >7.22 mmol/L; p = 0.011, OR = 2.781, CI = 1.263-6.119), red blood distribution width standard deviation (RDW-SD; p = 0.024, OR = 1.118, CI = 1.015-1.231), and Glasgow coma scale (GCS; p < 0.001, OR = 0.710, CI = 0.633-0.798) were independent risk factors. The Model-Logit was as follows: Logit(P) = -5.467 + 0.057 * Age + 1.023 * BG (>7.22 mmol/L, yes = 1, no = 0) + 0.111 * RDW-SD-0.342 * GCS. The AUCs values of the Model-Logit, GCS, age, BG (>7.22 mmol/L), and RDW-SD were 0.865, 0.819, 0.634, 0.698, and 0.625, respectively. For clinical use, the Model-Cat was established. In the Model-Cat, the AUCs for GCS, age, BG, and RDW-SD were 0.850, 0.760, 0.700, 0.641, and 0.564, respectively. The AUCs of the Model-Logit were insignificantly higher than the Model-Cat (Delong test, p = 0.157). The total points from -3 to 4 and 5 to 14 were classified as low- and high-risk levels, respectively.

Conclusions

Age, BG (> 7.22 mmol/L), GCS, and RDW-SD were independent risk factors for HAP in aSAH patients. The Model-Cat was convenient for practical evaluation. The aSAH patients with total points from 5 to 14 had a high risk for HAP, suggesting the need for more attention during treatment.

Which hospital-acquired conditions matter the most in trauma? An evidence-based approach for prioritizing trauma program improvement

BACKGROUND

Prevention of hospital-acquired conditions (HACs) is a focus of trauma center quality improvement. The relative contributions of various HACs to postinjury hospital outcomes are unclear. We sought to quantify and compare the impacts of six HACs on early clinical outcomes and resource utilization in hospitalized trauma patients.

METHODS

Adult patients from the 2013 to 2016 American College of Surgeons Trauma Quality Improvement Program Participant Use Data Files who required 5 days or longer of hospitalization and had an Injury Severity Score of 9 or greater were included. Multiple imputation with chained equations was used for observations with missing data. The frequencies of six HACs and five adverse outcomes were determined. Multivariable Poisson regression with log link and robust error variance was used to produce relative risk estimates, adjusting for patient-, hospital-, and injury-related factors. Risk-adjusted population attributable fractions estimates were derived for each HAC-outcome pair, with the adjusted population attributable fraction estimate for a given HAC-outcome pair representing the estimated percentage decrease in adverse outcome that would be expected if exposure to the HAC had been prevented.

RESULTS

A total of 529,856 patients requiring 5 days or longer of hospitalization were included. The incidences of HACs were as follows: pneumonia, 5.2%; urinary tract infection, 3.4%; venous thromboembolism, 3.3%; surgical site infection, 1.3%; pressure ulcer, 1.3%; and central line-associated blood stream infection, 0.2%. Pneumonia demonstrated the strongest association with in-hospital outcomes and resource utilization. Prevention of pneumonia in our cohort would have resulted in estimated reductions of the following: 22.1% for end organ dysfunction, 7.8% for mortality, 8.7% for prolonged hospitalization, 7.1% for prolonged intensive care unit stay, and 6.8% for need for mechanical ventilation. The impact of other HACs was comparatively small.

CONCLUSION

We describe a method for comparing the contributions of HACs to outcomes of hospitalized trauma patients. Our findings suggest that trauma program improvement efforts should prioritize pneumonia prevention.

LEVEL OF EVIDENCE

Therapeutic/Care Management; Level IV.

Inflammation and the role of infection: Complications and treatment options following neurotrauma

Traumatic brain injury can have devastating consequences for patients and extended hospital stays and recovery course. Recent data indicate that the initial insult causes profound changes to the immune system and leads to a pro-inflammatory state. This alteration in homeostasis predisposes patients to an increased risk of infection and underlying autoimmune conditions. Increased emphasis has been placed on understanding this process both in the clinical and preclinical literature. This review highlights the intrinsic inflammatory conditions that can occur within the initial hospital stay, discusses long-term immune consequences, highlights emerging treatment options, and delves into important pathways currently being investigated with preclinical models.

Neural stem cell therapy in conjunction with curcumin loaded in niosomal nanoparticles enhanced recovery from traumatic brain injury

Despite a great amount of effort, there is still a need for reliable treatments of traumatic brain injury (TBI). Recently, stem cell therapy has emerged as a new avenue to address neuronal regeneration after TBI. However, the environment of TBI lesions exerts negative effects on the stem cells efficacy. Therefore, to maximize the beneficial effects of stem cells in the course of TBI, we evaluated the effect of human neural stem/progenitor cells (hNS/PCs) and curcumin-loaded niosome nanoparticles (CM-NPs) on behavioral changes, brain edema, gliosis, and inflammatory responses in a rat model of TBI. After TBI, hNS/PCs were transplanted within the injury site and CM-NPs were orally administered for 10 days. Finally, the effect of combination therapy was compared to several control groups. Our results indicated a significant improvement of general locomotor activity in the hNS/PCs + CM-NPs treatment group compared to the control groups. We also observed a significant improvement in brain edema in the hNS/PCs + CM-NPs treatment group compared to the other groups. Furthermore, a significant decrease in astrogliosis was seen in the combined treatment group. Moreover, TLR4-, NF-κB-, and TNF-α- positive cells were significantly decreased in hNS/PCs + CM-NPs group compared to the control groups. Taken together, this study indicated that combination therapy of stem cells with CM-NPs can be an effective therapy for TBI.

Development and Validation of a Functionally Relevant Comorbid Health Index in Adults Admitted to Inpatient Rehabilitation for Traumatic Brain Injury

Several studies have characterized comorbidities among individuals with traumatic brain injury (TBI); however, there are few validated TBI comorbidity indices. Widely used indices (e.g., Elixhauser Comorbidity Index [ECI]) were developed in other patient populations and anchor to mortality or healthcare utilization, not functioning, and notably exclude conditions known to co-occur with TBI. The objectives of this study were to develop and validate a functionally relevant TBI comorbidity index (Fx-TBI-CI) and to compare prognostication of the Fx-TBI-CI with the ECI. We used data from the eRehabData database to divide the sample randomly into a training sample (N = 21,292) and an internal validation sample (N = 9166). We used data from the TBI Model Systems National Database as an external validation sample (N = 1925). We used least absolute shrinkage and selection operator (LASSO) regression to narrow the list of functionally relevant conditions from 39 to 12. In internal validation, the Fx-TBI-CI explained 14.1% incremental variance over an age and sex model predicting the Functional Independence Measure (FIM) Motor subscale at inpatient rehabilitation discharge, compared with 2.4% explained by the ECI. In external validation, the Fx-TBI-CI explained 4.9% incremental variance over age and sex and 3.8% over age, sex, and Glasgow Coma Scale score,compared with 2.1% and 1.6% incremental variance, respectively, explained by the ECI. An unweighted Sum Condition Score including the same conditions as the Fx-TBI-CI conferred similar prognostication. Although the Fx-TBI-CI had only modest incremental variance over demographics and injury severity in predicting functioning in external validation, the Fx-TBI-CI outperformed the ECI in predicting post-TBI function.

Long-term follow-up of neurodegenerative phenomenon in severe traumatic brain injury using MRI

BACKGROUND

Traumatic brain injury (TBI) lesions are known to evolve over time, but the duration and consequences of cerebral remodeling are unclear. Degenerative mechanisms occurring in the chronic phase after TBI could constitute "tertiary" lesions related to the neurological outcome.

OBJECTIVE

The objective of this prospective study of severe TBI was to longitudinally evaluate the volume of white and grey matter structures and white matter integrity with 2 time-point multimodal MRI.

METHODS

Longitudinal MRI follow-up was obtained for 11 healthy controls (HCs) and 22 individuals with TBI (mean [SD] 60 [15] months after injury) along with neuropsychological assessments. TBI individuals were classified in the "favourable" recovery group (Glasgow Outcome Scale Extended [GOSE] 6-8) and "unfavourable" recovery group (GOSE 3-5) at 5 years. Variation in brain volumes (3D T1-weighted image) and white matter integrity (diffusion tensor imaging [DTI]) were quantitatively assessed over time and used to predict neurological outcome.

RESULTS

TBI individuals showed a marked decrease in volumes of whole white matter (median -11.4% [interquartile range -5.8; -14.6]; p <0.001) and deep grey nuclear structures (-17.1% [-10.6; -20.5]; p <0.001). HCs did not show any significant change over the same time period. Median volumetric loss in several brain regions was higher with GOSE 3-5 than 6-8. These lesions were associated with lower fractional anisotropy and higher mean diffusivity at baseline. Volumetric variations were positively correlated with normalized fractional anisotropy and negatively with normalized mean diffusivity at baseline and follow-up. A computed predictive model with baseline DTI showed good accuracy to predict neurological outcome (area under the receiver operating characteristic curve 0.82 [95% confidence interval 0.81-0.83]) Conclusions. We characterised the striking atrophy of deep brain structures after severe TBI. DTI imaging in the subacute phase can predict the occurrence and localization of these tertiary lesions as well as long-term neurological outcome.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT00577954. Registered on October 2006.

Neutrophil-to-Lymphocyte Ratios and Infections after Traumatic Brain Injury: Associations with Hospital Resource Utilization and Long-Term Outcome

Traumatic brain injury (TBI) induces immune dysfunction that can be captured clinically by an increase in the neutrophil-to-lymphocyte ratio (NLR). However, few studies have characterized the temporal dynamics of NLR post-TBI and its relationship with hospital-acquired infections (HAI), resource utilization, or outcome. We assessed NLR and HAI over the first 21 days post-injury in adults with moderate-to-severe TBI (n = 196) using group-based trajectory (TRAJ), changepoint, and mixed-effects multivariable regression analysis to characterize temporal dynamics. We identified two groups with unique NLR profiles: a high (n = 67) versus a low (n = 129) TRAJ group. High NLR TRAJ had higher rates (76.12% vs. 55.04%, p = 0.004) and earlier time to infection (p = 0.003). In changepoint-derived day 0–5 and 6–20 epochs, low lymphocyte TRAJ, early in recovery, resulted in more frequent HAIs (p = 0.042), subsequently increasing later NLR levels (p ≤ 0.0001). Both high NLR TRAJ and HAIs increased hospital length of stay (LOS) and days on ventilation (p ≤ 0.05 all), while only high NLR TRAJ significantly increased odds of unfavorable six-month outcome as measured by the Glasgow Outcome Scale (GOS) (p = 0.046) in multivariable regression. These findings provide insight into the temporal dynamics and interrelatedness of immune factors which collectively impact susceptibility to infection and greater hospital resource utilization, as well as influence recovery.

A Population-Based Study of Pre-Existing Health Conditions in Traumatic Brain Injury

Health factors impacting both the occurrence of, and recovery from traumatic brain injury (TBI) vary in complexity, and present genuine challenges to researchers and healthcare professionals seeking to characterize injury consequences and determine prognosis. However, attempts to clarify causal links between injury characteristics and clinical outcomes (including mortality) often compel researchers to exclude pre-existing health conditions (PECs) in their samples, including psychiatric history, medication usage, and other comorbid conditions. In this pre-registered population-based study (total starting n = 939,123 patients), we examined trends in PEC incidence over 22 years in the state of Pennsylvania (1997-2019) in individuals sustaining TBI (n = 169,452) and individuals with orthopedic injury (n = 87,637). The goal was to determine how PECs interact with age and injury severity to influence short-term outcomes. A further goal was to determine whether number of PECs, or specific PEC clusters contributed to worse outcomes within the TBI cohort, compared with orthopedic injury alone. Primary findings indicate that PECs significantly influenced mortality within the TBI cohort; patients having four or more PECs were associated with approximately a two times greater likelihood of dying in acute care (odds ratio [OR] 1.9). Additionally, cluster analyses revealed four distinct PEC clusters that are age and TBI severity dependent. Overall, the likelihood of zero PECs hovers at ∼25%, which is critical to consider in TBI outcomes work and could potentially contribute to the challenges facing intervention science with regard to reproducibility of findings.

Early chronic systemic inflammation and associations with cognitive performance after moderate to severe TBI

BACKGROUND

Cognitive dysfunction adversely effects multiple functional outcomes and social roles after TBI. We hypothesize that chronic systemic inflammation exacerbates cognitive deficits post-injury and diminishes functional cognition and quality of life (QOL). Yet few studies have examined relationships between inflammation and cognition after TBI. Associations between early chronic serum inflammatory biomarker levels, cognitive outcomes, and QOL 6-months and 12-months after moderate-to-severe TBI were identified using unweighted (uILS) and weighted (wILS) inflammatory load score (ILS) formation.

METHODS

Adults with moderate-to-severe TBI (n ​= ​157) completed neuropsychological testing, the Functional Impairment Measure Cognitive Subscale (FIM-Cog) and self-reported Percent Back to Normal scale 6 months (n ​= ​139) and 12 months (n ​= ​136) post-injury. Serial serum samples were collected 1-3 months post-TBI. Cognitive composite scores were created as equally weighted means of T-scores derived from a multidimensional neuropsychological test battery. Median inflammatory marker levels associated with 6-month and 12-month cognitive composite T-scores (p ​< ​0.10) were selected for ILS formation. Markers were quartiled, and quartile ranks were summed to generate an uILS. Marker-specific β-weights were derived using penalized ridge regression, multiplied by standardized marker levels, and summed to generate a wILS. ILS associations with cognitive composite scores were assessed using multivariable linear regression. Structural equation models assessed ILS influences on functional cognition and QOL using 12-month FIM-Cog and Percent Back to Normal scales.

RESULTS

ILS component markers included: IL-1β, TNF-α, sIL-4R, sIL-6R, RANTES, and MIP-1β. Increased sIL-4R levels were positively associated with overall cognitive composite T-scores in bivariate analyses, while remaining ILS markers were negatively associated with cognition. Multivariable receiver operator curves (ROC) showed uILS added 14.98% and 31.93% relative improvement in variance captured compared to the covariates only base model (age, sex, education, Glasgow Coma Scale score) when predicting cognitive composite scores at 6 and 12 months, respectively; wILS added 33.99% and 36.87% relative improvement in variance captured. Cognitive composite mediated wILS associations with FIM-Cog scores at 12 months, and both cognitive composite and FIM-Cog scores mediated wILS associations with QOL.

CONCLUSIONS

Early chronic inflammatory burden is associated with cognitive performance post-TBI. wILS explains greater variance in cognitive composite T-scores than uILS. Linking inflammatory burden associated with cognitive deficits to functional outcome post-TBI demonstrates the potential impact of immunotherapy interventions aimed at improving cognitive recovery post-TBI.

Management of COVID-19-Positive Patient with Bilateral Longitudinal Petrous Fractures: A Case Report

Temporal bone fractures are bilateral in 8 to 29% of cases. High index of suspicion of coronavirus disease 2019 (COVID-19) infection is important while dealing with any patient with special concern to the surgical and traumatized patients to reach a good patient outcome especially during the pandemic of COVID-19. We describe in this report the management of COVID-19-positive patient with bilateral longitudinal petrous fractures and moderate head injury.

Mechanical ventilation in neurocritical care setting: A clinical approach

Neuropatients often require invasive mechanical ventilation (MV). Ideal ventilator settings and respiratory targets in neuro patients are unclear. Current knowledge suggests maintaining protective tidal volumes of 6-8 ml/kg of predicted body weight in neuropatients. This approach may reduce the rate of pulmonary complications, although it cannot be easily applied in a neuro setting due to the need for special care to minimize the risk of secondary brain damage. Additionally, the weaning process from MV is particularly challenging in these patients who cannot control the brain respiratory patterns and protect airways from aspiration. Indeed, extubation failure in neuropatients is very high, while tracheostomy is needed in one-third of the patients. The aim of this manuscript is to review and describe the current management of invasive MV, weaning, and tracheostomy for the main four subpopulations of neuro patients: traumatic brain injury, acute ischemic stroke, subarachnoid hemorrhage, and intracerebral hemorrhage.

The influence of the COVID-19 pandemic on traumatic brain injuries in Tyrol: experiences from a state under lockdown

Purpose

In February 2020, the federal state of Tyrol in Austria has become one of the epicenters of the COVID-19 pandemic. Tyrol is known for numerous skiing areas. Thus, winter sport resorts became a starting point for COVID-19 infections spreading towards the rest of the state, Austria and other countries, leading to a mandatory quarantine for almost a million people, who were placed under a curfew and restrictions in daily life. Additionally, all ski resorts and hotels were closed. We aimed to analyze the influence of the COVID-19 quarantine on traumatic brain injury (TBI) cases in Tyrol.

Methods

We retrospectively compared demographical and injury characteristics from all TBI patients within the 2020 strict quarantine period with the respective time periods from 2016 to 2019. As our department is the only neurosurgical unit in Tyrol, all patients with moderate or severe TBI are transferred to our hospital.

Results

During 3 weeks of the full quarantine period, the weekly TBI cases load decreased significantly in comparison to the same time periods in the years 2016–2019. Furthermore, concomitant skull fractures decreased significantly (p < 0.016), probably reflecting different causative mechanisms. The other demographical and injury characteristics and particularly falls at home stayed relatively unchanged.

Conclusion

TBI remained an important contributor to the neurosurgical workflow during the COVID-19 pandemic. Strategies to ensure neurosurgical care also under pandemic-induced lockdown are important.