Neuropsychological Characteristics of the Confusional State Following Traumatic Brain Injury

Acorus calamus Linn.: A novel neuroprotective approach for traumatic brain injury in Drosophila melanogaster

BACKGROUND

Traumatic brain injury (TBI) causes substantial mortality and morbidity globally. Current treatments only alleviate symptoms and do not halt secondary injury progression.

OBJECTIVES

Evaluate the neuroprotective potential of Acorus calamus Linn. (AC) in a Drosophila melanogaster model of high-impact TBI.

METHODS

Fruit flies (Drosophila melanogaster) of the Oregon R + strain were administered hydroalcoholic extracts of Acorus calamus Linn. (HAEAC) at concentrations of 25 and 50 µg/mL, 24 h and continuously for 72 h, respectively, following TBI induction. Mortality rate, locomotor function, neurotransmitter levels, and oxidative stress markers were assessed at 24 and 72 h post-injury as outcomemeasures.

RESULTS

AC significantly reduced post-TBI mortality and improved locomotor function in a dose-dependent manner. Additionally, AC increased acetylcholinesterase, gamma-aminobutyric acid, serotonin, and dopamine levels while reducing glutamate. It also boosted antioxidant activity (superoxide dismutase, glutathione, and catalase) and lowered markers of oxidative damage (malondialdehyde, nitrite).

CONCLUSIONS

AC mitigated behavioral deficits, oxidative damage, and neurotransmitter imbalance in fruit flies after TBI. These findings indicate AC may be more effective than individual drugs for TBI therapy. Further research into its neuroprotective phytochemicals is warranted.

Traumatic Brain Injury Patients Admitted on High-Census Days Receive Less Critical Care and Have an Increased Risk for Delirium

INTRODUCTION

The utilization of healthcare services in a growing population has raised concerns about its impact on clinical outcomes. Studies have shown that increased hospital census is associated with higher admission rates and unnecessary consults, tests, and procedures in various areas of healthcare. Traumatic brain injuries (TBIs), a significant concern due to their potential for long-term disabilities, are commonly encountered in intensive care units (ICUs) and are a leading cause of patient mortality. Despite extensive research on various aspects of TBI, the effect of the patient census on TBI outcomes remains unexplored. This study aims to investigate the relationship between healthcare provider patient census and clinical outcomes in TBI patients at a level I trauma center.

METHODS

A retrospective review was conducted from 2017 to 2022. The mean number of patients per day in the trauma service was determined, with patients below this average considered to be present on low-census days and those above it on high-census days. Patient demographics, mechanisms of injury, vital signs, TBI severity, and associated injuries were analyzed. Adjusted regression analyses were conducted.

RESULTS

Over the study period, 1,527 TBI patients were identified. Demographics were similar between patients admitted on high- and low-census days. Patients with moderate TBI were 30% less likely to be admitted to the ICU on high-census days, whereas there was no difference in ICU admission for patients with mild or severe TBI. Delirium was significantly higher in patients admitted on high-census days compared to those on low-census days. This was further identified to be predominantly driven by patients with mild TBI admitted on high-census days.

CONCLUSION

While most outcomes remained consistent, significant rates of delirium were found in our mild TBI patients admitted on high-census days suggesting the need for additional factors in the evaluation of these patients on admission. This study also reveals potential under-triage in moderate TBI patients on high-census days as they had significantly lower rates of ICU admission. These findings emphasize the need for further investigations to optimize patient care strategies within the context of fluctuating healthcare system demands.

Radiological features of brain hemorrhage through automated segmentation from computed tomography in stroke and traumatic brain injury

Introduction

Radiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study.

Methods

Non-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Hemorrhage location frequency maps were generated. The number of estimated haematoma clusters was correlated with the total haematoma volume. Ground truth expert annotations were available for one ICH site; hence, a comparison was made with the estimated haematoma volumes. Segmentation volume estimates were used in a receiver operator characteristics (ROC) analysis for all samples (i.e., bleed detected) and then specifically for one site with few TBI-bleed cases.

Results

The hemorrhage frequency maps showed spatial patterns of estimated lesions consistent with ICH or TBI-bleed presentations. There was a positive correlation between the estimated number of clusters and total haematoma volume for each site (correlation range: 0.45-0.74; each p-value < 0.01) and evidence of ICH between-site differences. Relative to hand-drawn annotations for one ICH site, the VIOLA-AI segmentation mask achieved a median Dice Similarity Coefficient of 0.82 (interquartile range: 0.78 and 0.83), resulting in a small overestimate in the haematoma volume by a median of 0.47 mL (interquartile range: 0.04 and 1.75 mL). The bleed detection ROC analysis for the whole sample gave a high area-under-the-curve (AUC) of 0.92 (with sensitivity and specificity of 83.28% and 95.41%); however, when considering only the mild head injury site, the TBI-bleed detection gave an AUC of 0.70.

Discussion

An open-source segmentation tool was used to visualize hemorrhage locations across multiple data sources and revealed quantitative hemorrhage site differences. The automated total hemorrhage volume estimate correlated with a per-participant hemorrhage cluster count. ROC results were moderate-to-high. The VIOLA-AI tool had promising results and might be useful for various types of intracranial hemorrhage.

Patterns of proactive interference in CVLT-II: evidence of a low-organized, disorganized, and highly organized learning style

OBJECTIVE

Previous studies have interpreted proactive interference (PI) either as indicating executive dysfunction or a normal process indicating deep level encoding. We investigated these competing models of PI in a large clinical sample using cluster analyses. We expected to find clusters defined by high PI but otherwise characterized by either EF impairment or of good memory performance.

METHOD

File records of 731 patients with neurological or psychiatric disorders were analyzed. PI-scores, false positive recognition errors, and semantic organization scores on the California Verbal Learning Test-II (CVLT-II) were subjected to cluster analyses. Clusters were compared regarding buildup and release from PI, memory performance and strategy measures, measures of intelligence, EF, and processing speed.

RESULTS

The analyses revealed six analyzable clusters. Two clusters showed no buildup of PI and normal release from PI. Discriminability was impaired both in List A and B. Learning acquisition and speeded measures of EF were reduced. One cluster showed both buildup of PI and problems with releasing from PI, and particularly impaired discriminability of List B. Semantic organization was low. Learning consolidation and EF speeded measures were impaired. Two other clusters showed buildup of PI, but no problem with release. Learning was highly organized, and they showed good memory and normal neuropsychological performance.

CONCLUSIONS

Results shows differentiation between a low organized EF dysfunction pattern with no PI, a disorganized PI pattern also indicating EF dysfunction and a highly organized pattern where PI seems to be the price to pay for high effort put into the learning process.

The role of the Neuropsychologist across the stages of recovery from acquired brain injury: a summary from the pediatric rehabilitation Neuropsychology collaborative

Neuropsychologists working in a pediatric neurorehabilitation setting provide care for children and adolescents with acquired brain injuries (ABI) and play a vital role on the interdisciplinary treatment team. This role draws on influences from the field of clinical neuropsychology and its pediatric subspecialty, as well as rehabilitation psychology. This combination of specialties is uniquely suited for working with ABI across the continuum of recovery. ABI recovery often involves a changing picture that spans across stages of recovery (e.g., disorders of consciousness, confusional state, acute cognitive impairment), where each stage presents with distinctive characteristics that warrant a specific evidence-based approach. Assessment and intervention are used reciprocally to inform diagnostics, treatment, and academic planning, and to support patient and family adjustment. Neuropsychologists work with the interdisciplinary teams to collect and integrate data related to brain injury recovery and use this data for treatment planning and clinical decision making. These approaches must often be adapted and adjusted in real time as patients recover, demanding a dynamic expertise that is currently not supported through formal training curriculum or practice guidelines. This paper outlines the roles and responsibilities of pediatric rehabilitation neuropsychologists across the stages of ABI recovery with the goal of increasing awareness in order to continue to develop and formalize this role.

The Traumatic Brain Injury Model Systems National Database: A Review of Published Research

The Traumatic Brain Injury Model Systems (TBIMS) is the largest longitudinal TBI data set in the world. Our study reviews the works using TBIMS data for analysis in the last 5 years. A search (2015–2020) was conducted across PubMed, EMBASE, and Google Scholar for studies that used the National Institute on Disability, Independent Living and Rehabilitation Research NIDILRR/VA-TBIMS data. Search terms were as follows: [“TBIMS” national database] within PubMed and Google Scholar, and [“TBIMS” AND national AND database] on EMBASE. Data sources, study foci (in terms of data processing and outcomes), study outcomes, and follow-up information usage were collected to categorize the studies included in this review. Variable usage in terms of TBIMS' form-based variable groups and limitations from each study were also noted. Assessment was made on how TBIMS' objectives were met by the studies. Of the 74 articles reviewed, 23 used TBIMS along with other data sets. Fifty-four studies focused on specific outcome measures only, 6 assessed data aspects as a major focus, and 13 explored both. Sample sizes of the included studies ranged from 11 to 15,835. Forty-two of the 60 longitudinal studies assessed follow-up from 1 to 5 years, and 15 studies used 10 to 25 years of the same. Prominent variable groups as outcome measures were “Employment,” “FIM,” “DRS,” “PART-O,” “Satisfaction with Life,” “PHQ-9,” and “GOS-E.” Limited numbers of studies were published regarding tobacco consumption, the Brief Test of Adult Cognition by Telephone (BTACT), the Supervision Rating Scale (SRS), general health, and comorbidities as variables of interest. Generalizability was the most significant limitation mentioned by the studies. The TBIMS is a rich resource for large-sample longitudinal analyses of various TBI outcomes. Future efforts should focus on under-utilized variables and improving generalizability by validation of results across large-scale TBI data sets to better understand the heterogeneity of TBI.

More than amnesia: prospective cohort study of an integrated novel assessment of the cognitive and behavioural features of PTA

Background and Objective:

Post-traumatic amnesia (PTA) is an early significant stage of recovery from traumatic brain injury (TBI). Current prospective PTA scales do not assess the full range of PTA symptomatology. This study conducted a novel integrated assessment of cognition and behaviour during PTA.

Method:

Twenty-four moderate-to-severe TBI participants in PTA and 23 TBI controls emerged from PTA were matched for age, gender, and years of education. All completed PTA measures (Galveston Orientation and Amnesia Test: GOAT, Westmead Post-traumatic Amnesia Scale: WPTAS), a cognitive battery; and behaviour ratings scored by 2 independent raters (informant and staff).

Results:

Significantly poorer performance was found during PTA for attention, processing speed, delayed verbal free recall and recognition, and visual learning. A large effect size was found for category fluency only. Behaviour ratings were significantly higher during PTA. Five behaviours were rated as high frequency (>50%) by both raters: Inattention, Impulsivity, Sleep Disturbance, Daytime Arousal, and Self-Monitoring. Prospective PTA measures produced significantly different duration estimates from 2 days (GOAT vs. WPTAS 1st day) to 9 days (WPTAS 1st day vs. 3-day). The WPTAS correlated most highly with processing speed and language tasks; whilst the GOAT correlated most highly with language and executive control of verbal memory.

Conclusion:

New prospective measures are needed that integrate core cognitive and behavioural features are brief, easy to administer, and capable of measuring emergence. The term PTA is a misnomer that requires revision to better accommodate the clinical syndrome.