Medicine's Glass Slipper: The PAVAEX Boot and 20th Century Negative Pressure Therapy.

Before the 20th century, peripheral artery disease (PAD) manifested as extreme pain, chronic wounds, and, eventually, gangrene requiring amputation. Despite this, it was rarely diagnosed. However, at the turn of the century, Western medicine shifted focus from infectious to chronic illnesses, and with this change, physicians' engagement with PAD transformed. Aiming to mitigate long-term injury, physicians now worked to identify and treat vessel disease to restore meaningful blood circulation. This article explores the development and deployment of a new device resulting from this refocus, the PAssive VAscular EXerciser (PAVAEX) Boot, and its role as a creative response to a previously intractable clinical problem. The PAVAEX Boot, designed in 1933 by vascular surgeons Louis G. Herrmann and Mont R. Reid, was one of the few interventions for PAD at the time. Based on the observation that continuous negative pressure results in vasoconstriction, while short bursts transiently increase blood flow, the PAVAEX Boot utilized intermittent negative pressure to enhance peripheral vascular perfusion. Well-marketed and praised throughout the 1930s, it vanished from public writing and academic literature just 20 years later. However, negative pressure wound therapy resurged in the late 20th century, and though its inventors failed to recognize the precedent of the PAVAEX Boot, many of these devices and therapies are rooted in identical theories. We examine why the PAVAEX Boot faded from use and argue that the device remains a crucial advancement in negative pressure therapy.

Safety, Efficacy, and Clinical Outcomes of Dexmedetomidine for Sedation in Traumatic Brain Injury: A Scoping Review.

Dexmedetomidine is a promising alternative sedative agent for moderate-severe Traumatic brain injury (TBI) patients. Although the data are limited, the posited benefits of dexmedetomidine in this population are a reduction in secondary brain injury compared with current standard sedative regimens. In this scoping review, we critically appraised the literature to examine the effects of dexmedetomidine in patients with moderate-severe TBI to examine the safety, efficacy, and cerebral and systemic physiological outcomes within this population. We sought to identify gaps in the literature and generate directions for future research. Two researchers and a librarian queried PubMed, Embase, Scopus, and APA PsycINFO databases. Of 920 studies imported for screening, 11 were identified for inclusion in the review. The primary outcomes in the included studied were cerebral physiology, systemic hemodynamics, sedation levels and delirium, and the presence of paroxysmal sympathetic hyperactivity. Dexmedetomidine dosing ranged from 0.2 to 1 ug/kg/h, with 3 studies using initial boluses of 0.8 to 1.0 ug/kg over 10 minutes. Dexmedetomidine used independently or as an adjunct seems to exhibit a similar hemodynamic safety profile compared with standard sedation regimens, albeit with transient episodes of bradycardia and hypotension, decrease episodes of agitation and may serve to alleviate symptoms of sympathetic hyperactivity. This scoping review suggests that dexmedetomidine is a safe and efficacious sedation strategy in patients with TBI. Given its rapid onset of action and anxiolytic properties, dexmedetomidine may serve as a feasible sedative for TBI patients.

Utilization and Outcomes of Extracorporeal Membrane Oxygenation Following Traumatic Brain Injury in the United States.

Objectives: Describe contemporary ECMO utilization patterns among patients with traumatic brain injury (TBI) and examine clinical outcomes among TBI patients requiring ECMO. Design: Retrospective cohort study. Setting: Premier Healthcare Database (PHD) between January 2016 to June 2020. Subjects: Adult patients with TBI who were mechanically ventilated and stratified by exposure to ECMO. Results: Among patients exposed to ECMO, we examined the following clinical outcomes: hospital LOS, ICU LOS, duration of mechanical ventilation, and hospital mortality. Of our initial cohort (n = 59,612), 118 patients (0.2%) were placed on ECMO during hospitalization. Most patients were placed on ECMO within the first 2 days of admission (54.3%). Factors associated with ECMO utilization included younger age (OR 0.96, 95% CI (0.95-0.97)), higher injury severity score (ISS) (OR 1.03, 95% CI (1.01-1.04)), vasopressor utilization (2.92, 95% CI (1.90-4.48)), tranexamic acid utilization (OR 1.84, 95% CI (1.12-3.04)), baseline comorbidities (OR 1.06, 95% CI (1.03-1.09)), and care in a teaching hospital (OR 3.04, 95% CI 1.31-7.05). A moderate degree (ICC = 19.5%) of variation in ECMO use was explained at the individual hospital level. Patients exposed to ECMO had longer median (IQR) hospital and ICU length of stay (LOS) [26 days (11-36) versus 9 days (4-8) and 19.5 days (8-32) versus 5 days (2-11), respectively] and a longer median (IQR) duration of mechanical ventilation [18 days (8-31) versus 3 days (2-8)]. Patients exposed to ECMO experienced a hospital mortality rate of 33.9%, compared to 21.2% of TBI patients unexposed to ECMO. Conclusions: ECMO utilization in mechanically ventilated patients with TBI is rare, with significant variation across hospitals. The impact of ECMO on healthcare utilization and hospital mortality following TBI is comparable to non-TBI conditions requiring ECMO. Further research is necessary to better understand the role of ECMO following TBI and identify patients who may benefit from this therapy.

Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports.

Importance: Clinical text reports from head computed tomography (CT) represent rich, incompletely utilized information regarding acute brain injuries and neurologic outcomes. CT reports are unstructured; thus, extracting information at scale requires automated natural language processing (NLP). However, designing new NLP algorithms for each individual injury category is an unwieldy proposition. An NLP tool that summarizes all injuries in head CT reports would facilitate exploration of large data sets for clinical significance of neuroradiological findings. Objective: To automatically extract acute brain pathological data and their features from head CT reports. Design, Setting, and Participants: This diagnostic study developed a 2-part named entity recognition (NER) NLP model to extract and summarize data on acute brain injuries from head CT reports. The model, termed BrainNERD, extracts and summarizes detailed brain injury information for research applications. Model development included building and comparing 2 NER models using a custom dictionary of terms, including lesion type, location, size, and age, then designing a rule-based decoder using NER outputs to evaluate for the presence or absence of injury subtypes. BrainNERD was evaluated against independent test data sets of manually classified reports, including 2 external validation sets. The model was trained on head CT reports from 1152 patients generated by neuroradiologists at the Yale Acute Brain Injury Biorepository. External validation was conducted using reports from 2 outside institutions. Analyses were conducted from May 2020 to December 2021. Main Outcomes and Measures: Performance of the BrainNERD model was evaluated using precision, recall, and F1 scores based on manually labeled independent test data sets. Results: A total of 1152 patients (mean [SD] age, 67.6 [16.1] years; 586 [52%] men), were included in the training set. NER training using transformer architecture and bidirectional encoder representations from transformers was significantly faster than spaCy. For all metrics, the 10-fold cross-validation performance was 93% to 99%. The final test performance metrics for the NER test data set were 98.82% (95% CI, 98.37%-98.93%) for precision, 98.81% (95% CI, 98.46%-99.06%) for recall, and 98.81% (95% CI, 98.40%-98.94%) for the F score. The expert review comparison metrics were 99.06% (95% CI, 97.89%-99.13%) for precision, 98.10% (95% CI, 97.93%-98.77%) for recall, and 98.57% (95% CI, 97.78%-99.10%) for the F score. The decoder test set metrics were 96.06% (95% CI, 95.01%-97.16%) for precision, 96.42% (95% CI, 94.50%-97.87%) for recall, and 96.18% (95% CI, 95.151%-97.16%) for the F score. Performance in external institution report validation including 1053 head CR reports was greater than 96%. Conclusions and Relevance: These findings suggest that the BrainNERD model accurately extracted acute brain injury terms and their properties from head CT text reports. This freely available new tool could advance clinical research by integrating information in easily gathered head CT reports to expand knowledge of acute brain injury radiographic phenotypes.

Hospital Revisits for Post-Ischemic Stroke Epilepsy after Acute Stroke Interventions.

OBJECTIVES: Improvements in acute stroke care have led to an increase in ischemic stroke survivors, who are at risk for development of post-ischemic stroke epilepsy (PISE). The impact of therapies such as thrombectomy and thrombolysis on risk of hospital revisits for PISE is unclear. We utilized administrative data to investigate the association between stroke treatment and PISE-related visits. MATERIALS AND METHODS: Using claims data from California, New York, and Florida, we performed a retrospective analysis of adult survivors of acute ischemic strokes. Patients with history of epilepsy, trauma, infections, or tumors were excluded. Included patients were followed for a primary outcome of revisits for seizures or epilepsy. Cox proportional hazards regression was used to identify covariates associated with PISE. RESULTS: In 595,545 included patients (median age 74 [IQR 21], 52% female), the 6-year cumulative rate of PISE-related revisit was 2.20% (95% CI 2.16-2.24). In multivariable models adjusting for demographics, comorbidities, and indicators of stroke severity, IV-tPA (HR 1.42, 95% CI 1.31-1.54, p<0.001) but not MT (HR 1.62, 95% CI 0.90-1.50, p=0.2) was associated with PISE-related revisit. Patients who underwent decompressive craniectomy experienced a 2-fold increase in odds for returning with PISE (HR 2.35, 95% CI 1.69-3.26, p<0.001). In-hospital seizures (HR 4.06, 95% CI 3.76-4.39, p<0.001) also elevated risk for PISE. SIGNIFICANCE: We demonstrate that ischemic stroke survivors who received IV-tPA, underwent decompressive craniectomy, or experienced acute seizures were at increased risk PISE-related revisit. Close attention should be paid to these patients with increased potential for long-term development of and re-hospitalization for PISE.

Predictors of Surrogate Decision Makers Selecting Life-Sustaining Therapy for Severe Acute Brain Injury Patients: An Analysis of US Population Survey Data.

BACKGROUND: Patients with a severe acute brain injury admitted to the intensive care unit often have a poor neurological prognosis. In these situations, a clinician is responsible for conducting a goals-of-care conversation with the patient's surrogate decision makers. The diversity in thought and background of surrogate decision makers can present challenges during these conversations. For this reason, our study aimed to identify predictive characteristics of US surrogate decision makers' favoring life-sustaining treatment (LST) over comfort measures only for patients with severe acute brain injury. METHODS: We analyzed data from a cross-sectional survey study that had recruited 1588 subjects from an online probability-based US population sample. Seven hundred and ninety-two subjects had randomly received a hypothetical scenario regarding a relative intubated with severe acute brain injury with a prognosis of severe disability but with the potential to regain some consciousness. Seven hundred and ninety-six subjects had been randomized to a similar scenario in which the relative was projected to remain vegetative. For each scenario, we conducted univariate analyses and binary logistic regressions to determine predictors of LST selection among available respondent characteristics. RESULTS: 15.0% of subjects selected LST for the severe disability scenario compared to 11.4% for the vegetative state scenario (p = 0.07), with those selecting LST in both groups expressing less decisional certainty. For the severe disability scenario, independent predictors of LST included having less than a high school education (adjusted OR = 2.87, 95% CI = 1.23-6.76), concern regarding prognostic accuracy (7.64, 3.61-16.15), and concern regarding the cost of care (4.07, 1.80-9.18). For the vegetative scenario, predictors included the youngest age group (30-44 years, 3.33, 1.02-10.86), male gender (3.26, 1.75-6.06), English as a second language (2.94, 1.09-7.89), Evangelical Protestant (3.72, 1.28-10.84) and Catholic (4.01, 1.72-9.36) affiliations, and low income (< $25 K). CONCLUSION: Several demographic and decisional characteristics of US surrogate decision makers predict LST selection for patients with severe brain injury with varying degrees of poor prognosis. Surrogates concerned about the cost of medical care may nevertheless be inclined to select LST, albeit with high levels of decisional uncertainty, for patients projected to have severe disabilities.

Detecting Seizures and Epileptiform Abnormalities in Acute Brain Injury.

PURPOSE OF REVIEW: Acute brain injury (ABI) is a broad category of pathologies, including traumatic brain injury, and is commonly complicated by seizures. Electroencephalogram (EEG) studies are used to detect seizures or other epileptiform patterns. This review seeks to clarify EEG findings relevant to ABI, explore practical barriers limiting EEG implementation, discuss strategies to leverage EEG monitoring in various clinical settings, and suggest an approach to utilize EEG for triage. RECENT FINDINGS: Current literature suggests there is an increased morbidity and mortality risk associated with seizures or patterns on the ictal-interictal continuum (IIC) due to ABI. Further, increased use of EEG is associated with better clinical outcomes. However, there are many logistical barriers to successful EEG implementation that prohibit its ubiquitous use. Solutions to these limitations include the use of rapid EEG systems, non-expert EEG analysis, machine learning algorithms, and the incorporation of EEG data into prognostic models.