Early Seizure Prophylaxis in Mild and Moderate Traumatic Brain Injury: A Systematic Review and Meta-Analysis.

Importance: Guidelines recommend seizure prophylaxis for early posttraumatic seizures (PTS) after severe traumatic brain injury (TBI). Use of antiseizure medications for early seizure prophylaxis after mild or moderate TBI remains controversial. Objective: To determine the association between seizure prophylaxis and risk reduction for early PTS in mild and moderate TBI. Data Sources: PubMed, Google Scholar, and Web of Science (January 1, 1991, to April 18, 2023) were systematically searched. Study Selection: Observational studies of adult patients presenting to trauma centers in high-income countries with mild (Glasgow Coma Scale [GCS], 13-15) and moderate (GCS, 9-12) TBI comparing rates of early PTS among patients with seizure prophylaxis with those without seizure prophylaxis. Data Extraction and Synthesis: The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) reporting guidelines were used. Two authors independently reviewed all titles and abstracts, and 3 authors reviewed final studies for inclusion. A meta-analysis was performed using a random-effects model with absolute risk reduction. Main Outcome Measures: The main outcome was absolute risk reduction of early PTS, defined as seizures within 7 days of initial injury, in patients with mild or moderate TBI receiving seizure prophylaxis in the first week after injury. A secondary analysis was performed in patients with only mild TBI. Results: A total of 64 full articles were reviewed after screening; 8 studies (including 5637 patients) were included for the mild and moderate TBI analysis, and 5 studies (including 3803 patients) were included for the mild TBI analysis. The absolute risk reduction of seizure prophylaxis for early PTS in mild to moderate TBI (GCS, 9-15) was 0.6% (95% CI, 0.1%-1.2%; P = .02). The absolute risk reduction for mild TBI alone was similar 0.6% (95% CI, 0.01%-1.2%; P = .04). The number needed to treat to prevent 1 seizure was 167 patients. Conclusion and Relevance: Seizure prophylaxis after mild and moderate TBI was associated with a small but statistically significant reduced risk of early posttraumatic seizures after mild and moderate TBI. The small absolute risk reduction and low prevalence of early seizures should be weighed against potential acute risks of antiseizure medications as well as the risk of inappropriate continuation beyond 7 days.

Insights into epileptogenesis from post-traumatic epilepsy.

Post-traumatic epilepsy (PTE) accounts for 5% of all epilepsies. The incidence of PTE after traumatic brain injury (TBI) depends on the severity of injury, approaching one in three in groups with the most severe injuries. The repeated seizures that characterize PTE impair neurological recovery and increase the risk of poor outcomes after TBI. Given this high risk of recurrent seizures and the relatively short latency period for their development after injury, PTE serves as a model disease to understand human epileptogenesis and trial novel anti-epileptogenic therapies. Epileptogenesis is the process whereby previously normal brain tissue becomes prone to recurrent abnormal electrical activity, ultimately resulting in seizures. In this Review, we describe the clinical course of PTE and highlight promising research into epileptogenesis and treatment using animal models of PTE. Clinical, imaging, EEG and fluid biomarkers are being developed to aid the identification of patients at high risk of PTE who might benefit from anti-epileptogenic therapies. Studies in preclinical models of PTE have identified tractable pathways and novel therapeutic strategies that can potentially prevent epilepsy, which remain to be validated in humans. In addition to improving outcomes after TBI, advances in PTE research are likely to provide therapeutic insights that are relevant to all epilepsies.

Development and prospective validation of an artificial intelligence-based smartphone app for rapid intraoperative pituitary adenoma identification.

BACKGROUND: Intraoperative pathology consultation plays a crucial role in tumor surgery. The ability to accurately and rapidly distinguish tumor from normal tissue can greatly impact intraoperative surgical oncology management. However, this is dependent on the availability of a specialized pathologist for a reliable diagnosis. We developed and prospectively validated an artificial intelligence-based smartphone app capable of differentiating between pituitary adenoma and normal pituitary gland using stimulated Raman histology, almost instantly. METHODS: The study consisted of three parts. After data collection (part 1) and development of a deep learning-based smartphone app (part 2), we conducted a prospective study that included 40 consecutive patients with 194 samples to evaluate the app in real-time in a surgical setting (part 3). The smartphone app's sensitivity, specificity, positive predictive value, and negative predictive value were evaluated by comparing the diagnosis rendered by the app to the ground-truth diagnosis set by a neuropathologist. RESULTS: The app exhibits a sensitivity of 96.1% (95% CI: 89.9-99.0%), specificity of 92.7% (95% CI: 74-99.3%), positive predictive value of 98% (95% CI: 92.2-99.8%), and negative predictive value of 86.4% (95% CI: 66.2-96.8%). An external validation of the smartphone app on 40 different adenoma tumors and a total of 191 scanned SRH specimens from a public database shows a sensitivity of 93.7% (95% CI: 89.3-96.7%). CONCLUSIONS: The app can be readily expanded and repurposed to work on different types of tumors and optical images. Rapid recognition of normal versus tumor tissue during surgery may contribute to improved intraoperative surgical management and oncologic outcomes. In addition to the accelerated pathological assessments during surgery, this platform can be of great benefit in community hospitals and developing countries, where immediate access to a specialized pathologist during surgery is limited.

In tumor surgery, precise identification of abnormal tissue during surgical removal of the tumor is paramount. Traditional methods rely on the availability of specialized pathologists for a reliable diagnosis, which could be a limitation in many hospitals. Our study introduces a user-friendly smartphone app that quickly and precisely diagnoses pituitary tumors, powered by artificial intelligence (AI), which is the simulation of human intelligence in machines for tasks like learning, reasoning, problem-solving, and decision-making. Through data collection, app development, and validation, our findings demonstrate that the app can rapidly and accurately identify tumors in real-time. External validation further confirmed its effectiveness in detecting tumor tissue collected from a different source. This AI-driven app could contribute to elevating surgical precision, particularly in settings lacking immediate access to specialized pathologists.

Automated Preoperative and Postoperative Volume Estimates Risk of Retreatment in Chronic Subdural Hematoma: A Retrospective, Multicenter Study.

BACKGROUND AND OBJECTIVES: Several neurosurgical pathologies, ranging from glioblastoma to hemorrhagic stroke, use volume thresholds to guide treatment decisions. For chronic subdural hematoma (cSDH), with a risk of retreatment of 10%-30%, the relationship between preoperative and postoperative cSDH volume and retreatment is not well understood. We investigated the potential link between preoperative and postoperative cSDH volumes and retreatment. METHODS: We performed a retrospective chart review of patients operated for unilateral cSDH from 4 level 1 trauma centers, February 2009-August 2021. We used a 3-dimensional deep learning, automated segmentation pipeline to calculate preoperative and postoperative cSDH volumes. To identify volume thresholds, we constructed a receiver operating curve with preoperative and postoperative volumes to predict cSDH retreatment rates and selected the threshold with the highest Youden index. Then, we developed a light gradient boosting machine to predict the risk of cSDH recurrence. RESULTS: We identified 538 patients with unilateral cSDH, of whom 62 (12%) underwent surgical retreatment within 6 months of the index surgery. cSDH retreatment was associated with higher preoperative (122 vs 103 mL; P < .001) and postoperative (62 vs 35 mL; P < .001) volumes. Patients with >140 mL preoperative volume had nearly triple the risk of cSDH recurrence compared with those below 140 mL, while a postoperative volume >46 mL led to an increased risk for retreatment (22% vs 6%; P < .001). On multivariate modeling, our model had an area under the receiver operating curve of 0.76 (95% CI: 0.60-0.93) for predicting retreatment. The most important features were preoperative and postoperative volume, platelet count, and age. CONCLUSION: Larger preoperative and postoperative cSDH volumes increase the risk of retreatment. Volume thresholds may allow identification of patients at high risk of cSDH retreatment who would benefit from adjunct treatments. Machine learning algorithm can quickly provide accurate estimates of preoperative and postoperative volumes.

Subarachnoid Hemorrhage Outcomes in an Endovascular Right of First Refusal Neurosurgical Environment.

BACKGROUND: Randomized controlled trials demonstrate that endovascular techniques yield improved outcomes compared with microsurgical approaches. However, not all patients are suitable candidates for endovascular management. This study aimed to determine if healthy patients managed microsurgically could achieve functional outcomes comparable to patients managed endovascularly. METHODS: Patients treated for ruptured aneurysmal subarachnoid hemorrhage at 2 level 1 stroke centers from January 2012 through December 2020 were retrospectively reviewed. All cases were evaluated in an endovascular right of first refusal neurosurgical environment. We collected relevant clinical and follow-up data and created a generalized linear model to identify differences between patients treated endovascularly versus microsurgically. A propensity score model accounting for these differences was used to predict patient outcomes. Functional outcomes were independently assessed using the modified Rankin Scale (mRS) with good functional outcome defined as modified Rankin Scale score <3. RESULTS: The study included 588 patients (211 microsurgical, 377 endovascular); median age was 58 years (interquartile range: 40-86 years); in-hospital mortality was 13%. Age, aneurysm size, and aneurysm location significantly predicted treatment modality (all P < 0.05). After greedy-type matching (210 microsurgical, 210 endovascular), patients managed microsurgically were less likely to be discharged home (odds ratio = 0.6, 95% confidence interval 0.4-0.9, P = 0.01). Functional differences disappeared over time; patients in the 2 treatment arms had similar functional outcomes at 3 months (odds ratio = 1.1, 95% confidence interval 0.7-1.8, P = 0.66) and 1 year after subarachnoid hemorrhage (odds ratio = 1.3, 95% confidence interval 0.8-2.1, P = 0.38). CONCLUSIONS: In an endovascular right of first refusal neurosurgical environment, practitioners can treat patients who are not good endovascular candidates microsurgically and achieve functional outcomes comparable to patients managed endovascularly.

Interaction of admission platelet count with current medications and the risk for chronic subdural recurrence.

OBJECTIVE: Chronic subdural hematoma (cSDH) has a reported 10%-24% rate of recurrence after surgery, and prognostic models for recurrence have produced equivocal results. The objective of this study was to leverage a data mining algorithm, chi-square automatic interaction detection (CHAID), which can incorporate continuous, nominal, and binary data into a decision tree, to identify the most robust predictors of repeat surgery for cSDH patients. METHODS: This was a retrospective cohort study of all patients with SDH from two level 1 trauma centers at a single institution. All patients underwent cSDH evacuation performed by 15 neurosurgeons between 2011 and 2020. The primary outcome was the rate of repeat surgery for recurrent cSDH following the initial evacuation. The authors used CHAID to identify relevant predictors of repeat surgery, including age, sex, comorbidities, postsurgical complications, platelet count prior to the first procedure, midline shift prior to the first procedure, hematoma volume, and preoperative use of anticoagulants, antiplatelets, or statins. RESULTS: Sixty (13.8%) of 435 study-eligible patients (average age 74.0 years) had a cSDH recurrence. These patients had 2.0 times greater odds of having used anticoagulants. The final CHAID model had an overall accuracy of 87.4% and an area under the curve of 0.76. According to the model, the predictor with the strongest association with cSDH recurrence was admission platelet count. Approximately 26% of patients (n = 23/87) with an admission platelet count < 157 × 109/L had a cSDH recurrence, whereas none of the 44 patients with admission platelets > 313 × 109/L had a recurrence. Approximately 17% of patients in the 157-313 × 109/L platelet group who had used preoperative statins required a second procedure, which was associated with a 2.3 times increased risk for repeat surgery compared to those who had not used statins preoperatively. Among those who had not used preoperative statins, a platelet count ≤ 179 × 109/L on admission for the first procedure was the strongest differentiator for a second surgery (n = 5/22 [23%]), which increased the risk of recurrence by 4.5 times. Among the patients using preoperative statins, the use of anticoagulants was the strongest differentiator for requiring repeat surgery (n = 11/33 [33%]). CONCLUSIONS: The described model identified platelet count on admission as the most important predictor of repeat cSDH surgery, followed by preoperative statin use and anticoagulant use. Critical cutoffs for platelet count were identified, which future studies should evaluate to determine if they are modifiable or reflective of underlying disease states.

Applying the Sliding Scale Approach to Quantifying Functional Outcomes Up to Two Years After Severe Traumatic Brain Injury.

Outcomes after severe traumatic brain injury (TBI) can be represented by a sliding score that compares actual functional recovery to that predicted by illness severity models. This approach has been applied in clinical trials because of its statistical efficiency and interpretability but has not been used to describe change in functional recovery over time. The objective of this study was to use a sliding scoring system to describe the magnitude of change in Glasgow Outcome Scale Extended (GOSE) score at 6, 12, and 24 months after severe TBI and to compare patients who improved after 6 months to those who did not. This study included consecutive severe TBI patients (Glasgow Coma Scale ≤8; n = 482) from a single center. We grouped patients into four strata based on probability of unfavorable outcome (GOSE = 1-4) using the International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) model, selected a dichotomous GOSE threshold within each stratum, and compared each patient's GOSE to this threshold to calculate a score (GOSE-Sliding Scale [SS]) from -5 to +4 at 6, 12, and 24 months. We compared GOSE-SS at 6 months with GOSE-SS at 12 and 24 months and also compared characteristics of participants who improved after 6 months with characteristics of those who did not using χ2 and t tests. Compared with at 6 months, 40% of patients (n = 74) had improved GOSE-SS at 12 months, and 53% had improved GOSE-SS by 24 months (n = 72). Among those who improved at 12 months, the average magnitude of improvement was 1.7 ± 0.9 and among those who improved at 24 months, the average magnitude of improvement was 1.9 ± 1.0. Those who improved their GOSE-SS score from 6 to 24 months had longer hospital stays (mean-difference = 8.6 days; p = 0.03), longer intensive care unit (ICU) stays (mean-difference = 5.5 days; p = 0.02), and longer ventilator time (mean-difference = 5 days; p = 0.02) than those who worsened. These results support an optimistic long-term outlook for severe TBI patients and emphasize the importance of long-term follow-up in severe TBI survivors.

Comparison of machine learning models to predict long-term outcomes after severe traumatic brain injury.

OBJECTIVE: An estimated 1.5 million people die every year worldwide from traumatic brain injury (TBI). Physicians are relatively poor at predicting long-term outcomes early in patients with severe TBI. Machine learning (ML) has shown promise at improving prediction models across a variety of neurological diseases. The authors sought to explore the following: 1) how various ML models performed compared to standard logistic regression techniques, and 2) if properly calibrated ML models could accurately predict outcomes up to 2 years posttrauma. METHODS: A secondary analysis of a prospectively collected database of patients with severe TBI treated at a single level 1 trauma center between November 2002 and December 2018 was performed. Neurological outcomes were assessed at 3, 6, 12, and 24 months postinjury with the Glasgow Outcome Scale. The authors used ML models including support vector machine, neural network, decision tree, and naïve Bayes models to predict outcome across all 4 time points by using clinical information available on admission, and they compared performance to a logistic regression model. The authors attempted to predict unfavorable versus favorable outcomes (Glasgow Outcome Scale scores of 1-3 vs 4-5), as well as mortality. Models' performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) with 95% confidence interval and balanced accuracy. RESULTS: Of the 599 patients in the database, the authors included 501, 537, 469, and 395 at 3, 6, 12, and 24 months posttrauma. Across all time points, the AUCs ranged from 0.71 to 0.85 for mortality and from 0.62 to 0.82 for unfavorable outcomes with various modeling strategies. Decision tree models performed worse than all other modeling approaches for multiple time points regarding both unfavorable outcomes and mortality. There were no statistically significant differences between any other models. After proper calibration, the models had little variation (0.02-0.05) across various time points. CONCLUSIONS: The ML models tested herein performed with equivalent success compared with logistic regression techniques for prognostication in TBI. The TBI prognostication models could predict outcomes beyond 6 months, out to 2 years postinjury.

Interpretable machine learning model for imaging-based outcome prediction after cardiac arrest.

INTRODUCTION: Early identification of brain injury patterns in computerized tomography (CT) imaging is crucial for post-cardiac arrest prognostication. Lack of interpretability of machine learning prediction reduces trustworthiness by clinicians and prevents translation to clinical practice. We aimed to identify CT imaging patterns associated with prognosis with interpretable machine learning. METHODS: In this IRB-approved retrospective study, we included consecutive comatose adult patients hospitalized at a single academic medical center after resuscitation from in- and out-of-hospital cardiac arrest between August 2011 and August 2019 who underwent unenhanced CT imaging of the brain within 24 hours of their arrest. We decomposed the CT images into subspaces to identify interpretable and informative patterns of injury, and developed machine learning models to predict patient outcomes (i.e., survival and awakening status) using the identified imaging patterns. Practicing physicians visually examined the imaging patterns to assess clinical relevance. We evaluated machine learning models using 80%-20% random data split and reported AUC values to measure the model performance. RESULTS: We included 1284 subjects of whom 35% awakened from coma and 34% survived hospital discharge. Our expert physicians were able to visualize decomposed image patterns and identify those believed to be clinically relevant on multiple brain locations. For machine learning models, the AUC was 0.710 ± 0.012 for predicting survival and 0.702 ± 0.053 for predicting awakening, respectively. DISCUSSION: We developed an interpretable method to identify patterns of early post-cardiac arrest brain injury on CT imaging and showed these imaging patterns are predictive of patient outcomes (i.e., survival and awakening status).

Outcomes and Treatment Algorithm in Glioblastoma Patients 80 Years and Older.

OBJECTIVE: The current standard of care for patients with glioblastoma (GBM) is maximal safe resection followed by adjuvant radiation therapy with concurrent temozolomide chemotherapy. Previous studies that identified this treatment regimen focused on younger patients with GBM. The proportion of patients with GBM over the age of 80 years is increasing. We investigate whether elderly patients benefit from the current standard of care with additional maximal safe resection. METHODS: Clinical, operative, radiographic, demographic, genetic, and outcomes data were retrospectively collected for patients treated for histologically confirmed World Health Organization grade 4 GBM at University of Pittsburgh Medical Center from 2009 to 2020. Only patients 80 years and older were included (n = 123). Statistically significant values were set at P < 0.05. RESULTS: A univariate Cox proportional hazards analysis of GBM patients aged >80 years identified the use of temozolomide, radiation, Karnofsky Performance Status (KPS) > 70, and methylguanine DNA methyltransferase methylation with increased overall survival (OS). Further multivariate Cox proportional hazards model analysis showed that the variables identified in the univariate analysis passed multicollinearity testing, and that use of temozolomide, KPS >70, and gross total resection were shown to significantly impact survival. Survival analysis showed that patients with biopsy alone had a shorter median OS compared with patients who received resection, temozolomide, and radiation (P < 0.0001, median OS 1.6 vs. 7.5 months). Additionally, patients who underwent biopsy and then received temozolomide and radiation had a shorter median OS when compared with patients who received resection, temozolomide, and radiation (P = 0.0047, median OS 3.6 vs. 7.5 months). CONCLUSIONS: For elderly patients with KPS >70, GTR followed by radiation and temozolomide is associated with maximum OS.

Predicting posttraumatic epilepsy using admission electroencephalography after severe traumatic brain injury.

OBJECTIVE: Posttraumatic epilepsy (PTE) develops in as many as one third of severe traumatic brain injury (TBI) patients, often years after injury. Analysis of early electroencephalographic (EEG) features, by both standardized visual interpretation (viEEG) and quantitative EEG (qEEG) analysis, may aid early identification of patients at high risk for PTE. METHODS: We performed a case-control study using a prospective database of severe TBI patients treated at a single center from 2011 to 2018. We identified patients who survived 2 years postinjury and matched patients with PTE to those without using age and admission Glasgow Coma Scale score. A neuropsychologist recorded outcomes at 1 year using the Expanded Glasgow Outcomes Scale (GOSE). All patients underwent continuous EEG for 3-5 days. A board-certified epileptologist, blinded to outcomes, described viEEG features using standardized descriptions. We extracted 14 qEEG features from an early 5-min epoch, described them using qualitative statistics, then developed two multivariable models to predict long-term risk of PTE (random forest and logistic regression). RESULTS: We identified 27 patients with and 35 without PTE. GOSE scores were similar at 1 year (p = .93). The median time to onset of PTE was 7.2 months posttrauma (interquartile range = 2.2-22.2 months). None of the viEEG features was different between the groups. On qEEG, the PTE cohort had higher spectral power in the delta frequencies, more power variance in the delta and theta frequencies, and higher peak envelope (all p < .01). Using random forest, combining qEEG and clinical features produced an area under the curve of .76. Using logistic regression, increases in the delta:theta power ratio (odds ratio [OR] = 1.3, p < .01) and peak envelope (OR = 1.1, p < .01) predicted risk for PTE. SIGNIFICANCE: In a cohort of severe TBI patients, acute phase EEG features may predict PTE. Predictive models, as applied to this study, may help identify patients at high risk for PTE, assist early clinical management, and guide patient selection for clinical trials.

Advances in surgical approaches for refractory pituitary adenomas.

Refractory pituitary adenomas are difficult to control tumors that progress through optimal surgical, medical, and radiation management. Repeat surgery is a valuable tool to reduce tumor volume for more effective radiation and/or medical therapy, and to decompress critical neurovascular structures. Advances in surgical techniques and technologies, including minimally invasive cranial approaches, intraoperative MRI suites, and cranial nerve monitoring, have improved surgical outcomes and expanded indications. Today, repeat transsphenoidal surgery has similar complications rates to upfront surgery in historical cohorts. The decision to operate on refractory adenomas should be made with multidisciplinary teams, balancing the benefit of tumor reduction with the potential for complications, including cranial nerve injury, carotid injury, and cerebrospinal fluid leak.

Radiosurgery outcomes in infratentorial juvenile pilocytic astrocytomas.

PURPOSE: To assess survival and neurological outcomes for patients who underwent primary or salvage stereotactic radiosurgery (SRS) for infratentorial juvenile pilocytic astrocytomas (JPA). METHODS: Between 1987 and 2022, 44 patients underwent SRS for infratentorial JPA. Twelve patients underwent primary SRS and 32 patients underwent salvage SRS. The median patient age at SRS was 11.6 years (range, 2-84 years). Prior to SRS, 32 patients had symptomatic neurological deficits, with ataxia as the most common symptom in 16 patients. The median tumor volume was 3.22 cc (range, 0.16-26.6 cc) and the median margin dose was 14 Gy (range, 9.6-20 Gy). RESULTS: The median follow-up was 10.9 years (range, 0.42-26.58 years). Overall survival (OS) after SRS was 97.7% at 1-year, and 92.5% at 5- and 10-years. Progression free survival (PFS) after SRS was 95.4% at 1-year, 79.0% at 5-years, and 61.4% at 10-years. There is not a significant difference in PFS between primary and salvage SRS patients (p = 0.79). Younger age correlated with improved PFS (HR 0.28, 95% CI 0.063-1.29, p = 0.021). Sixteen patients (50%) had symptomatic improvements while 4 patients (15.6%) had delayed onset of new symptoms related to tumor progression (n = 2) or treatment related complications (n = 2). Tumor volumetric regression or disappearance after radiosurgery was found in 24 patients (54.4%). Twelve patients (27.3%) had delayed tumor progression after SRS. Additional management of tumor progression included repeat surgery, repeat SRS, and chemotherapy. CONCLUSIONS: SRS was a valuable alternative to initial or repeat resection for deep seated infratentorial JPA patients. We found no survival differences between patients who had primary and salvage SRS.

Performance of CRASH and IMPACT Prognostic Models for Traumatic Brain Injury at 12 and 24 Months Post-Injury.

The Corticoid Randomization after Significant Head Injury (CRASH) and International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) prognostic models are the most reported prognostic models for traumatic brain injury (TBI) in the scientific literature. However, these models were developed and validated to predict 6-month unfavorable outcome and mortality, and growing evidence supports continuous improvements in functional outcome after severe TBI up to 2 years post-injury. The purpose of this study was to evaluate CRASH and IMPACT model performance beyond 6 months post-injury to include 12 and 24 months post-injury. Discriminative validity remained consistent over time and comparable to earlier recovery time points (area under the curve = 0.77-0.83). Both models had poor fit for unfavorable outcomes, explaining less than one quarter of the variation in outcomes for severe TBI patients. The CRASH model had significant values for the Hosmer-Lemeshow test at 12 and 24 months, indicating poor model fit past the previous validation point. There is concern in the scientific literature that TBI prognostic models are being used by neurotrauma clinicians to support clinical decision making despite the goal of the models' development being to support research study design. The results of this study indicate that the CRASH and IMPACT models should not be used in routine clinical practice because of poor model fit that worsens over time and the large, unexplained variance in outcomes.

Association of Posttraumatic Epilepsy With Long-term Functional Outcomes in Individuals With Severe Traumatic Brain Injury.

BACKGROUND AND OBJECTIVE: Nearly one-third of patients with severe traumatic brain injury (TBI) develop posttraumatic epilepsy (PTE). The relationship between PTE and long-term outcomes is unknown. We tested whether, after controlling for injury severity and age, PTE is associated with worse functional outcomes after severe TBI. METHODS: We performed a retrospective analysis of a prospective database of patients with severe TBI treated from 2002 through 2018 at a single level 1 trauma center. Glasgow Outcome Scale (GOS) was collected at 3, 6, 12, and 24 months postinjury. We used repeated-measures logistic regression predicting GOS, dichotomized as favorable (GOS 4-5) and unfavorable (GOS 1-3), and a separate logistic model predicting mortality at 2 years. We used predictors as defined by the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) base model (i.e., age, pupil reactivity, and GCS motor score), PTE status, and time. RESULTS: Of 392 patients who survived to discharge, 98 (25%) developed PTE. The proportion of patients with favorable outcomes at 3 months did not differ between those with and without PTE (23% [95% Confidence Interval [CI]: 15%-34%] vs 32% [95% CI: 27%-39%]; p = 0.11) but was significantly lower at 6 (33% [95% CI: 23%-44%] vs 46%; [95% CI: 39%-52%] p = 0.03), 12 (41% [95% CI: 30%-52%] vs 54% [95% CI: 47%-61%]; p = 0.03), and 24 months (40% [95% CI: 47%-61%] vs 55% [95% CI: 47%-63%]; p = 0.04). This was driven by higher rates of GOS 2 (vegetative) and 3 (severe disability) outcomes in the PTE group. By 2 years, the incidence of GOS 2 or 3 was double in the PTE group (46% [95% CI: 34%-59%]) compared with that in the non-PTE group (21% [95% CI: 16%-28%]; p < 0.001), while mortality was similar (14% [95% CI: 7%-25%] vs 23% [95% CI: 17%-30%]; p = 0.28). In multivariate analysis, patients with PTE had lower odds of favorable outcome (odds radio [OR] 0.1; 95% CI: 0.1-0.4; p < 0.001), but not mortality (OR 0.9; 95% CI: 0.1-1.9; p = 0.46). DISCUSSION: Posttraumatic epilepsy is associated with impaired recovery from severe TBI and poor functional outcomes. Early screening and treatment of PTE may improve patient outcomes.

Hunt-Hess Score at 48 Hours Improves Prognostication in Grade 5 Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND: Patients with Hunt-Hess (HH)5 aneurysmal subarachnoid hemorrhage (SAH) have high mortality rates. Despite an initial moribund exam, a subset of patients progress to favorable outcomes. OBJECTIVE: To evaluate the utility of delayed HH grading to improve prognostication. METHODS: We retrospectively reviewed patients undergoing treatment of ruptured aneurysms at two level 1 stroke centers from January 2012 through December 2020. We collected relevant clinical information and developed a multivariate cox regression model to identify independent predictors of mortality. To evaluate the utility of delayed examinations in predicting outcomes, we re-assessed the HH grade at 48 hours post admission and constructed a logistic regression model with potential confounders to predict mortality. RESULTS: From 2012 to 2020, 621 patients underwent treatment for aneurysmal SAH. We identified 63 HH5 patients (10%) with a mean age of 58 years. Among these patients, the median length of stay was 14 days, with 3 patients passing away within 48 hours. The overall mortality rate was 63% at 24 months. To predict mortality, our cox regression model found only age to be significant (P = 0.002). Delayed HH grading improved prognostication at 48 hours and remained significant on multivariate analysis as a predictor of mortality (P = 0.0001). We observed a significant difference in mortality between patients HH5 and patients HH4 or lower at 48 hours (P = 0.0003). CONCLUSIONS: Delayed reassessment of HH grade 48 hours postadmission is a predictor of mortality, suggesting reassessment at 48 hours in high grade SAH leads to better prognostication.

Prognostic Models for Traumatic Brain Injury Have Good Discrimination but Poor Overall Model Performance for Predicting Mortality and Unfavorable Outcomes.

BACKGROUND: The most extensively validated prognostic models for traumatic brain injury (TBI) are the Corticoid Randomization after Significant Head Injury (CRASH) and International Mission on Prognosis and Analysis of Clinical Trials (IMPACT). Model characteristics outside of area under the curve (AUC) are rarely reported. OBJECTIVE: To report the discriminative validity and overall model performance of the CRASH and IMPACT models for prognosticating death at 14 days (CRASH) and 6 months (IMPACT) and unfavorable outcomes at 6 months after TBI. METHODS: This retrospective cohort study included prospectively collected patients with severe TBI treated at a single level I trauma center (n = 467). CRASH and IMPACT percent risk values for the given outcome were computed. Unfavorable outcome was defined as a Glasgow Outcome Scale-Extended score of 1 to 4 at 6 months. Binary logistic regressions and receiver operating characteristic analyses were used to differentiate patients from the CRASH and IMPACT prognostic models. RESULTS: All models had low R 2 values (0.17-0.23) with AUC values from 0.77 to 0.81 and overall accuracies ranging from 72.4% to 78.3%. Sensitivity (35.3-50.0) and positive predictive values (66.7-69.2) were poor in the CRASH models, while specificity (52.3-53.1) and negative predictive values (58.1-63.6) were poor in IMPACT models. All models had unacceptable false positive rates (20.8%-33.3%). CONCLUSION: Our results were consistent with previous literature regarding discriminative validity (AUC = 0.77-0.81). However, accuracy and false positive rates of both the CRASH and IMPACT models were poor.

Gonococcal Infection and Ventriculoperitoneal Shunts.

ABSTRACT: Neisseria gonorrhoea e and Chlamydia trachomatis are pathogens commonly isolated in pelvic inflammatory disease. Neisseria gonorrhoea e may uncommonly spread outside the urogenital tract to cause complications. We present 2 cases of adolescents with ventriculoperitoneal shunt infection due to N. gonorrhoea e, requiring shunt externalization.

Multicenter and prospective trial of anti-epileptics for early seizure prevention in mild traumatic brain injury with a positive computed tomography scan.

Background: Posttraumatic seizures (PTSs) are a major source of disability after traumatic brain injury (TBI). The Brain Trauma Foundation Guidelines recommend prophylactic anti-epileptics (AEDs) for early PTS in severe TBI, but high-quality evidence is lacking in mild TBI. Methods: To determine the benefit of administering prophylactic AEDs, we performed a prospective and multicenter study evaluating consecutive patients who presented to a Level 1 trauma center from January 2017 to December 2020. We included all patients with mild TBI defined as Glasgow Coma Scale (GCS) 13-15 and a positive head computed tomography (CT). Patients were excluded for previous seizure history, current AED use, or a neurosurgical procedure. Patients were given a prophylactic 7-day course of AEDs on a week-on versus week-off basis and followed with in-person clinic visits, in-hospital evaluation, or a validated phone questionnaire. Results: Four hundred and ninety patients were enrolled, 349 (71.2%) had follow-up, and 139 (39.8%) were given prophylactic AEDs. There was no difference between seizure rates for the prophylactic AED group (0.7%) and those without (2.9%; P = 0.25). Patients who had a PTS were on average older (81.4 years) than patients without a seizure (64.8 years; P = 0.02). Seizure rate increased linearly by age groups: <60 years old (0%); 60-70 years old (1.7%); 70-80 years old (2.3%); and >80 years old (4.6%). Conclusion: Prophylactic AEDs did not provide a benefit for PTS reduction in mild TBI patients with a positive CT head scan.

Mice lacking proSAAS display alterations in emotion, consummatory behavior and circadian entrainment.

ProSAAS is a neuroendocrine protein that is cleaved by neuropeptide-processing enzymes into more than a dozen products including the bigLEN and PEN peptides, which bind and activate the receptors GPR171 and GPR83, respectively. Previous studies have suggested that proSAAS-derived peptides are involved in physiological functions that include body weight regulation, circadian rhythms and anxiety-like behavior. In the present study, we find that proSAAS knockout mice display robust anxiety-like behaviors in the open field, light-dark emergence and elevated zero maze tests. These mutant mice also show a reduction in cued fear and an impairment in fear-potentiated startle, indicating an important role for proSAAS-derived peptides in emotional behaviors. ProSAAS knockout mice exhibit reduced water consumption and urine production relative to wild-type controls. No differences in food consumption and overall energy expenditure were observed between the genotypes. However, the respiratory exchange ratio was elevated in the mutants during the light portion of the light-dark cycle, indicating decreased fat metabolism during this period. While proSAAS knockout mice show normal circadian patterns of activity, even upon long-term exposure to constant darkness, they were unable to shift their circadian clock upon exposure to a light pulse. Taken together, these results show that proSAAS-derived peptides modulate a wide range of behaviors including emotion, metabolism and the regulation of the circadian clock.