Thematic Analysis of Psychosocial Stressors and Adaptive Coping Strategies Among Informal Caregivers of Patients Surviving ICU Admission for Coma.

BACKGROUND: Family caregivers of patients with severe acute brain injury (SABI) admitted to intensive care units (ICUs) with coma experience heightened emotional distress stemming from simultaneous stressors. Stress and coping frameworks can inform psychosocial intervention development by elucidating common challenges and ways of navigating such experiences but have yet to be employed with this population. The present study therefore sought to use a stress and coping framework to characterize the stressors and coping behaviors of family caregivers of patients with SABI hospitalized in ICUs and recovering after coma. METHODS: Our qualitative study recruited a convenience sample from 14 US neuroscience ICUs. Participants were family caregivers of patients who were admitted with ischemic stroke, intracerebral hemorrhage, subarachnoid hemorrhage, traumatic brain injury, or hypoxic-ischemic encephalopathy; had experienced a comatose state for > 24 h; and completed or were scheduled for tracheostomy and/or gastrostomy tube placement. Participants were recruited < 7 days after transfer out of the neuroscience ICU. We conducted live online video interviews from May 2021 to January 2022. One semistructured interview per participant was recorded and subsequently transcribed. Recruitment was stopped when thematic saturation was reached. We deductively derived two domains using a stress and coping framework to guide thematic analysis. Within each domain, we inductively derived themes to comprehensively characterize caregivers' experiences. RESULTS: We interviewed 30 caregivers. We identified 18 themes within the two theory-driven domains, including ten themes describing practical, social, and emotional stressors experienced by caregivers and eight themes describing the psychological and behavioral coping strategies that caregivers attempted to enact. Nearly all caregivers described using avoidance or distraction as an initial coping strategy to manage overwhelming emotions. Caregivers also expressed awareness of more adaptive strategies (e.g., cultivation of positive emotions, acceptance, self-education, and soliciting social and medical support) but had challenges employing them because of their heightened emotional distress. CONCLUSIONS: In response to substantial stressors, family caregivers of patients with SABI attempted to enact various psychological and behavioral coping strategies. They described avoidance and distraction as less helpful than other coping strategies but had difficulty engaging in alternative strategies because of their emotional distress. These findings can directly inform the development of additional resources to mitigate the long-term impact of acute psychological distress among this caregiver population.

Chlorophyll-based model underpinned by measured inherent optical properties of Jerlov water types.

An existing chlorophyll-based model has been updated and re-calibrated using measured data describing Jerlov water types, harvested from the World-wide Ocean Optics Database. This study has provided new chlorophyll concentration data, and used them in conjunction with recently published spectra of absorption and scattering coefficients to create an updated parameter set that describes eight of the 10 Jerlov water types. The updated model is consistent with other data, and it interprets the measured characteristics in terms of underlying properties. Techniques for inter-conversion between inherent and apparent optical properties have been further investigated, and the improved precision has uncovered new challenges that have been addressed using empirical techniques.

Vertebral and Basilar Artery Dissection in a Patient With Alport Syndrome.

Basilar artery occlusion (BAO) is a rare cause of stroke associated with significant morbidity and mortality. It is most frequently thromboembolic in nature, but may be caused by vertebral artery dissection. We present a case of BAO in a 36-year-old woman with Alport syndrome. She was treated with emergent thrombectomy via the right vertebral artery with return to baseline neurological status. Her clinical status deteriorated later the same day and she was found to have re-occlusion. Repeat thrombectomy was complicated by persistent re-occlusion requiring 7 passes to achieve reperfusion. Unfortunately, her neurological exam remained poor and she was transitioned to comfort care, expiring on admission day 3. An autopsy demonstrated acute dissection of the left vertebral artery, basilar artery, and bilateral posterior cerebral arteries. Alport syndrome is a type IV collagenopathy most known for causing kidney disease. It may also be associated with vascular fragility as type IV collagen forms a significant component of the vascular basement membrane. There are reports of aortic, coronary, and cervical dissections, but few reports of intracranial dissections in patients with Alport syndrome. While iatrogenic dissection cannot be ruled out, the histological findings in this case are most consistent with spontaneous arterial dissection as the cause of her initial neurologic presentation. This highlights the need for further investigation into the relationship between Alport syndrome and vascular fragility and should alert clinicians to the possibility of intracranial dissection in patients with AS.

Transcranial Color-Coded Sonography With Angle Correction As a Screening Tool for Raised Intracranial Pressure.

OBJECTIVES: Transcranial Doppler (TCD) has been evaluated as a noninvasive intracranial pressure (ICP) assessment tool. Correction for insonation angle, a potential source of error, with transcranial color-coded sonography (TCCS) has not previously been reported while evaluating ICP with TCD. Our objective was to study the accuracy of TCCS for detection of ICP elevation, with and without the use of angle correction. DESIGN: Prospective study of diagnostic accuracy. SETTING: Academic neurocritical care unit. PATIENTS: Consecutive adults with invasive ICP monitors. INTERVENTIONS: Ultrasound assessment with TCCS. MEASUREMENTS AND MAIN RESULTS: End-diastolic velocity (EDV), time-averaged peak velocity (TAPV), and pulsatility index (PI) were measured in the bilateral middle cerebral arteries with and without angle correction. Concomitant mean arterial pressure (MAP) and ICP were recorded. Estimated cerebral perfusion pressure (CPP) was calculated as estimated CPP (CPPe) = MAP × (EDV/TAPV) + 14, and estimated ICP (ICPe) = MAP-CPPe. Sixty patients were enrolled and 55 underwent TCCS. Receiver operating characteristic curve analysis of ICPe for detection of invasive ICP greater than 22 mm Hg revealed area under the curve (AUC) 0.51 (0.37-0.64) without angle correction and 0.73 (0.58-0.84) with angle correction. The optimal threshold without angle correction was ICPe greater than 18 mm Hg with sensitivity 71% (29-96%) and specificity 28% (16-43%). With angle correction, the optimal threshold was ICPe greater than 21 mm Hg with sensitivity 100% (54-100%) and specificity 30% (17-46%). The AUC for PI was 0.61 (0.47-0.74) without angle correction and 0.70 (0.55-0.92) with angle correction. CONCLUSIONS: Angle correction improved the accuracy of TCCS for detection of elevated ICP. Sensitivity was high, as appropriate for a screening tool, but specificity remained low.

Bright-light distractions and visual performance.

Visual distractions pose a significant risk to transportation safety, with laser attacks against aircraft pilots being a common example. This study used a research-grade High Dynamic Range (HDR) display to produce bright-light distractions for 12 volunteer participants performing a combined visual task across central and peripheral visual fields. The visual scene had an average luminance of 10 cd∙m-2 with targets of approximately 0.5° angular size, while the distractions had a maximum luminance of 9,000 cd∙m-2 and were 3.6° in size. The dependent variables were the mean fixation duration during task execution (representative of information processing time), and the critical stimulus duration required to support a target level of performance (representative of task efficiency). The experiment found a statistically significant increase in mean fixation duration, rising from 192 ms without distractions to 205 ms with bright-light distractions (p = 0.023). This indicates a decrease in visibility of the low contrast targets or an increase in cognitive workload that required greater processing time for each fixation in the presence of the bright-light distractions. Mean critical stimulus duration was not significantly affected by the distraction conditions used in this study. Future experiments are suggested to replicate driving and/or piloting tasks and employ bright-light distractions based on real-world data, and we advocate the use of eye-tracking metrics as sensitive measures of changes in performance.

AI-Based Decision Support System for Traumatic Brain Injury: A Survey.

Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.

An interpretable neural network for outcome prediction in traumatic brain injury.

BACKGROUND: Traumatic Brain Injury (TBI) is a common condition with potentially severe long-term complications, the prediction of which remains challenging. Machine learning (ML) methods have been used previously to help physicians predict long-term outcomes of TBI so that appropriate treatment plans can be adopted. However, many ML techniques are "black box": it is difficult for humans to understand the decisions made by the model, with post-hoc explanations only identifying isolated relevant factors rather than combinations of factors. Moreover, such models often rely on many variables, some of which might not be available at the time of hospitalization. METHODS: In this study, we apply an interpretable neural network model based on tropical geometry to predict unfavorable outcomes at six months from hospitalization in TBI patients, based on information available at the time of admission. RESULTS: The proposed method is compared to established machine learning methods-XGBoost, Random Forest, and SVM-achieving comparable performance in terms of area under the receiver operating characteristic curve (AUC)-0.799 for the proposed method vs. 0.810 for the best black box model. Moreover, the proposed method allows for the extraction of simple, human-understandable rules that explain the model's predictions and can be used as general guidelines by clinicians to inform treatment decisions. CONCLUSIONS: The classification results for the proposed model are comparable with those of traditional ML methods. However, our model is interpretable, and it allows the extraction of intelligible rules. These rules can be used to determine relevant factors in assessing TBI outcomes and can be used in situations when not all necessary factors are known to inform the full model's decision.

Measured IOPs of Jerlov water types.

Inherent optical properties (IOPs) of typical ocean waters have been derived from a worldwide database of measured parameters. The optical quality of the world's oceans can be described in terms of their Jerlov water type, ranging from the clearest Jerlov I to the most turbid Jerlov 9C. These Jerlov classifications are defined in terms of an apparent optical property known as the downwelling diffuse attenuation coefficient (Kd). There is a need to relate these Jerlov water types to their IOPs, namely their absorption coefficient, a, and scattering coefficient, b. However, robust values of a and b for Jerlov water types have not previously existed. This study used the World-wide Ocean Optics Database to derive a series of experimentally measured a and b values for six Jerlov water types. Using data science techniques to group measurements in time and space, over 13.5 million data points were consolidated into 53 measured values for a and b. Established models were subsequently applied to generate a complete table of absorption and scattering coefficients from 300 to 800 nm for Jerlov IB to Jerlov 5C. The analysis includes the influence of changes in the solar zenith angle and the scattering phase function. These data are recommended for use in applications where IOPs are required to describe Jerlov water types.

Trans-Ocular Brain Impedance Indices Predict Pressure Reactivity Index Changes in a Porcine Model of Hypotension and Cerebral Autoregulation Perturbation.

BACKGROUND: Cerebrovascular autoregulation (CA) is a protective mechanism that enables the cerebral vasculature to automodulate tone in response to changes in cerebral perfusion pressure to ensure constant levels of cerebral blood flow (CBF) and oxygen delivery. CA can be impaired after neurological injury and contributes to secondary brain injury. In this study, we report novel impedance indices using trans-ocular brain impedance (TOBI) during controlled systemic hemorrhage and hypotension to assess CA in comparison with pressure reactivity index (PRx). METHODS: Yorkshire swine were instrumented to record intracranial pressure (ICP), mean arterial pressure (MAP), and CBF. TOBI was recorded using electrocardiographic electrodes placed on the closed eyelids. Impedance changes (dz) were recorded in response to introducing an alternating current (0.4 mA) through the electrodes. MAP, ICP, and CBF were also measured. Animals were subjected to a controlled hemorrhage to remove 30-40% of each animal's total blood volume over 25-35 min. Hemorrhage was titrated to reach an MAP of approximately 35 mm Hg and end-tidal carbon dioxide above 28 mm Hg. PRx was calculated as a moving Pearson correlation between MAP and ICP. TOBI indices were calculated as the amplitude of the respiratory-induced changes in dz. DZx was calculated as a moving Pearson correlation between dz and MAP. TOBI indices (dz and DZx) were compared with hemodynamic indicators and PRx. RESULTS: dz was shown to be highly correlated with MAP, ICP, cerebral perfusion pressure, and CBF (r = - 0.823, - 0.723, - 0.813, and - 0.726), respectively (p < 0.0001). During hemorrhage, cerebral perfusion pressure and CBF had a mean percent decrease (standard deviation) from baseline of - 54.2% (12.5%) and - 28.3% (14.7%), respectively, whereas dz increased by 277% (268%). Receiver operator characteristics and precision-recall curves demonstrated high predictive performance of DZx when compared with PRx with an area under the curve above 0.82 and 0.89 for receiver operator characteristic and precision-recall curves, respectively, with high sensitivity and positive predictive power. CONCLUSIONS: TOBI indices appear to track changes in PRx and hemodynamics that affect CA during hemorrhage-induced hypotension. TOBI may offer a suitable, less invasive surrogate to PRx for monitoring and assessing CA.

Incidence, Predictors and Outcomes of Delirium in Critically Ill Patients With COVID-19.

BACKGROUND AND PURPOSE: A variety of neurological manifestations have been attributed to COVID-19, but there is currently limited evidence regarding risk factors and outcomes for delirium in critically ill patients with COVID-19. The purpose of this study was to identify delirium in a large cohort of ICU patients with COVID-19, and to identify associated features and clinical outcomes at the time of hospital discharge. METHODS: This is an observational cohort study of 213 consecutive patients admitted to an ICU for COVID-19 respiratory illness. Delirium was diagnosed by trained abstractors using the CHART-DEL instrument. The associations between key clinical features, sedation and delirium were examined, as were the impacts of delirium on clinical outcomes. RESULTS: Delirium was identified in 57.3% of subjects. Delirious patients were more likely to receive mechanical ventilation, had lower P: F ratios, higher rates of renal replacement therapy and ECMO, and were more likely to receive enteral benzodiazepines. Only mechanical ventilation remained a significant predictor of delirium in a logistic regression model. Mortality was not significantly different, but delirious patients experienced greater mechanical ventilation duration, ICU/hospital lengths of stay, worse functional outcomes at discharge, and were less likely to be discharged home. CONCLUSIONS: Delirium is common in critically ill patients with COVID-19 and appears to be associated with greater disease severity. When present, delirium is associated with worse functional status at discharge, but not increased mortality. Additional studies are necessary to determine the generalizability of these results and the impact of delirium on longer-term cognitive and functional outcomes.

Screening Computed Tomography Angiography to Identify Patients at Low Risk for Delayed Cerebral Ischemia Following Aneurysmal Subarachnoid Hemorrhage.

Introduction: Delayed cerebral ischemia (DCI) occurs during a risk period of 3-21 days following aneurysmal subarachnoid hemorrhage (aSAH) and is associated with worse outcomes. The identification of patients at low risk for DCI might permit triage to less intense monitoring and management. While large-vessel vasospasm (LVV) is a distinct clinical entity from DCI, the presence of moderate-to-severe LVV is associated with a higher risk of DCI. Our hypothesis was that the absence of moderate-to-severe LVV on screening computed tomographic angiography (CTA) performed within the first few days of the DCI risk period will accurately identify patients at low risk for subsequent DCI. Methods: This was a retrospective cohort study. Our institutional SAH outcomes registry was queried for all aSAH patients admitted in 2016-2019 who underwent screening CTA brain between days 4 and 8 following ictus. We excluded patients diagnosed with DCI prior to the first CTA performed during this time period. All variables are prospectively entered into the registry, and outcomes including DCI and LVV are prospectively adjudicated. We evaluated the predictive value and accuracy of moderate-to-severe LVV on CTA performed 4-8 days following ictus for the prediction of subsequent DCI. Results: A total of 243 aSAH patients were admitted during the study timeframe. Of the 54 patients meeting the eligibility criteria, 11 (20%) had moderate-to-severe LVV on the screening CTA study performed during the risk period. Seven of the 11 (64%) patients with moderate-to-severe LVV on the days 4-8 screening CTA vs. six of 43 (14%) patients without, subsequently developed DCI. On multivariate analysis, the presence of LVV on days 4-8 screening CTA was an independent predictor of DCI (odds ratio 10.26, 95% CI 1.69-62.24, p = 0.011). NPV for the subsequent development of DCI was 86% (95% CI 77-92%). Sensitivity was 54% (25-81%), specificity 90% (77-97%), and positive predictive value 64% (38-83%). Conclusions: The presence of moderate-to-severe LVV on screening CTA performed between days 4 and 8 following aSAH was an independent predictor of DCI, but achieved only moderate diagnostic accuracy, with NPV 86% and sensitivity 54%. Complementary risk-stratification strategies are likely necessary.

Safety and Efficacy of 23.4% Sodium Chloride Administered via Peripheral Venous Access for the Treatment of Cerebral Herniation and Intracranial Pressure Elevation.

BACKGROUND: Sodium chloride (NaCl) 23.4% solution has been shown to reduce intracranial pressure (ICP) and reverse transtentorial herniation. A limitation of 23.4% NaCl is its high osmolarity (8008 mOsm/l) and the concern for tissue injury or necrosis following extravasation when administered via peripheral venous access. The use of this agent is therefore often limited to central venous or intraosseous routes of administration. Our objective was to evaluate the safety and efficacy of administration of 23.4% NaCl via peripheral venous access compared with administration via central venous access. METHODS: We reviewed pharmacy records to identify all administrations of 23.4% NaCl at our institution between December 2017 and February 2020. Medical records were then reviewed to identify complications, such as extravasation, soft tissue injury or necrosis, hypotension (mean arterial pressure less than 65 mm Hg), pulmonary edema, hemolysis, and osmotic demyelination. We also compared the change in physiological variables, such as ICP, mean arterial pressure, cerebral perfusion pressure, and heart rate, as well as laboratory values, such as sodium, chloride, bicarbonate, creatinine, and hemoglobin, following administration of 23.4% NaCl via the peripheral and central venous routes. RESULTS: We identified 299 administrations of 23.4% NaCl (242 central and 57 peripheral) in 141 patients during the study period. There was no documented occurrence of soft tissue injury or necrosis in any patient. One patient developed hypotension following central administration. Among the 38 patients with ICP monitoring at the time of drug administration, there was no significant difference in median ICP reduction (- 13 mm Hg [central] vs. - 24 mm Hg [peripheral], p = 0.21) or cerebral perfusion pressure augmentation (16 mm Hg [central] vs. 15 mm Hg [peripheral], p = 0.87) based on route of administration. CONCLUSIONS: Peripheral venous administration of 23.4% NaCl is safe and achieves a reduction in ICP equivalent to that achieved by administration via central venous access.

A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication.

Prognosis of the long-term functional outcome of traumatic brain injury is essential for personalized management of that injury. Nonetheless, accurate prediction remains unavailable. Although machine learning has shown promise in many fields, including medical diagnosis and prognosis, such models are rarely deployed in real-world settings due to a lack of transparency and trustworthiness. To address these drawbacks, we propose a machine learning-based framework that is explainable and aligns with clinical domain knowledge. To build such a framework, additional layers of statistical inference and human expert validation are added to the model, which ensures the predicted risk score's trustworthiness. Using 831 patients with moderate or severe traumatic brain injury to build a model using the proposed framework, an area under the receiver operating characteristic curve (AUC) and accuracy of 0.8085 and 0.7488 were achieved, respectively, in determining which patients will experience poor functional outcomes. The performance of the machine learning classifier is not adversely affected by the imposition of statistical and domain knowledge "checks and balances". Finally, through a case study, we demonstrate how the decision made by a model might be biased if it is not audited carefully.

Volumetric quantification of aneurysmal subarachnoid hemorrhage independently predicts hydrocephalus and seizures.

OBJECTIVE: Hydrocephalus and seizures greatly impact outcomes of patients with aneurysmal subarachnoid hemorrhage (aSAH); however, reliable tools to predict these outcomes are lacking. The authors used a volumetric quantitative analysis tool to evaluate the association of total aSAH volume with the outcomes of shunt-dependent hydrocephalus and seizures. METHODS: Total hemorrhage volume following aneurysm rupture was retrospectively analyzed on presentation CT imaging using a custom semiautomated computer program developed in MATLAB that employs intensity-based k-means clustering to automatically separate blood voxels from other tissues. Volume data were added to a prospectively maintained aSAH database. The association of hemorrhage volume with shunted hydrocephalus and seizures was evaluated through logistic regression analysis and the diagnostic accuracy through analysis of the area under the receiver operating characteristic curve (AUC). RESULTS: The study population comprised 288 consecutive patients with aSAH. The mean total hemorrhage volume was 74.9 ml. Thirty-eight patients (13.2%) developed seizures. The mean hemorrhage volume in patients who developed seizures was significantly higher than that in patients with no seizures (mean difference 17.3 ml, p = 0.01). In multivariate analysis, larger hemorrhage volume on initial CT scan and hemorrhage volume > 50 ml (OR 2.81, p = 0.047, 95% CI 1.03-7.80) were predictive of seizures. Forty-eight patients (17%) developed shunt-dependent hydrocephalus. The mean hemorrhage volume in patients who developed shunt-dependent hydrocephalus was significantly higher than that in patients who did not (mean difference 17.2 ml, p = 0.006). Larger hemorrhage volume and hemorrhage volume > 50 ml (OR 2.45, p = 0.03, 95% CI 1.08-5.54) were predictive of shunt-dependent hydrocephalus. Hemorrhage volume had adequate discrimination for the development of seizures (AUC 0.635) and shunted hydrocephalus (AUC 0.629). CONCLUSIONS: Hemorrhage volume is an independent predictor of seizures and shunt-dependent hydrocephalus in patients with aSAH. Further evaluation of aSAH quantitative volumetric analysis may complement existing scales used in clinical practice and assist in patient prognostication and management.

Novel Algorithm for Automated Optic Nerve Sheath Diameter Measurement Using a Clustering Approach.

INTRODUCTION: Using ultrasound to measure optic nerve sheath diameter (ONSD) has been shown to be a useful modality to detect elevated intracranial pressure. However, manual assessment of ONSD by a human operator is cumbersome and prone to human errors. We aimed to develop and test an automated algorithm for ONSD measurement using ultrasound images and compare it to measurements performed by physicians. MATERIALS AND METHODS: Patients were recruited from the Neurological Intensive Care Unit. Ultrasound images of the optic nerve sheath from both eyes were obtained using an ultrasound unit with an ocular preset. Images were processed by two attending physicians to calculate ONSD manually. The images were processed as well using a novel computerized algorithm that automatically analyzes ultrasound images and calculates ONSD. Algorithm-measured ONSD was compared with manually measured ONSD using multiple statistical measures. RESULTS: Forty-four patients with an average/Standard Deviation (SD) intracranial pressure of 14 (9.7) mmHg were recruited and tested (with a range between 1 and 57 mmHg). A t-test showed no statistical difference between the ONSD from left and right eyes (P > 0.05). Furthermore, a paired t-test showed no significant difference between the manually and algorithm-measured ONSD with a mean difference (SD) of 0.012 (0.046) cm (P > 0.05) and percentage error of difference of 6.43% (P = 0.15). Agreement between the two operators was highly correlated (interclass correlation coefficient = 0.8, P = 0.26). Bland-Altman analysis revealed mean difference (SD) of 0.012 (0.046) (P = 0.303) and limits of agreement between -0.1 and 0.08. Receiver Operator Curve analysis yielded an area under the curve of 0.965 (P < 0.0001) with high sensitivity and specificity. CONCLUSION: The automated image-analysis algorithm calculates ONSD reliably and with high precision when compared to measurements obtained by expert physicians. The algorithm may have a role in computer-aided decision support systems in acute brain injury.

Trans-ocular brain impedance index for assessment of cerebral autoregulation in a porcine model of cerebral hemodynamic perturbation.

Cerebrovascular autoregulation (CA) is often impaired following traumatic brain injury. Established technologies and metrics used to assess CA are invasive and conducive for measurement, but not for continuous monitoring. We developed a trans-ocular brain impedance (TOBI) method that may provide non-invasive and continuous indices to assess CA. In this study, we monitored impedance metrics such as respiratory-induced impedance amplitude changes (dz) as well as a novel impedance index (DZx), which is a moving Pearson correlation between mean arterial pressure (MAP) and dz. Yorkshire swine were instrumented to continuously record ICP, MAP, and cerebral blood flow (CBF). TOBI was recorded by placement of standard ECG electrodes on closed eyelids and connected to a data acquisition system. MAP, ICP and CBF were manipulated utilizing an intravenous vasopressor challenge. TOBI indices (dz and DZx) were compared to the hemodynamic indicators as well as pressure reactivity index (PRx). During the vasopressor challenge, dz was highly correlated with ICP, CPP, and CBF (r = < - 0.49, p < 0.0001). ICP, CPP, and CBF had a mean percent increase (standard deviation) from baseline of 29(23.2)%, 70(25)%, and 37(72.6)% respectively while dz decreased by 31(15.6)%. Receiver operator curve test showed high predictive performance of DZx when compared to PRx with area under the curve above 0.86, with high sensitivity and specificity. Impedance indices appear to track changes in PRx and hemodynamics that affect cerebral autoregulation. TOBI may be a suitable less invasive surrogate to PRx and capable of tracking cerebral autoregulation.

Length of Stay Beyond Medical Readiness in a Neurosurgical Patient Population and Associated Healthcare Costs.

BACKGROUND: Length of stay beyond medical readiness (LOS-BMR) leads to increased expenses and higher morbidity related to hospital-acquired conditions. OBJECTIVE: To determine the proportion of admitted neurosurgical patients who have LOS-BMR and associated risk factors and costs. METHODS: We performed a prospective, cohort analysis of all neurosurgical patients admitted to our institution over 5 mo. LOS-BMR was assessed daily by the attending neurosurgeon and neuro-intensivist with a standardized criterion. Univariate and multivariate logistic regressions were performed. RESULTS: Of the 884 patients admitted, 229 (25.9%) had a LOS-BMR. The average LOS-BMR was 2.7 ± 3.1 d at an average daily cost of $9 148.28 ± $12 983.10, which resulted in a total cost of $2 076 659.32 over the 5-mo period. Patients with LOS-BMR were significantly more likely to be older and to have hemiplegia, dementia, liver disease, renal disease, and diabetes mellitus. Patients with a LOS-BMR were significantly more likely to be discharged to a subacute rehabilitation/skilled nursing facility (40.2% vs 4.1%) or an acute/inpatient rehabilitation facility (22.7% vs 1.7%, P < .0001). Patients with Medicare insurance were more likely to have a LOS-BMR, whereas patients with private insurance were less likely (P = .048). CONCLUSION: The most common reason for LOS-BMR was inefficient discharge of patients to rehabilitation and nursing facilities secondary to unavailability of beds at discharge locations, insurance clearance delays, and family-related issues.

Automated Segmentation and Severity Analysis of Subdural Hematoma for Patients with Traumatic Brain Injuries.

Detection and severity assessment of subdural hematoma is a major step in the evaluation of traumatic brain injuries. This is a retrospective study of 110 computed tomography (CT) scans from patients admitted to the Michigan Medicine Neurological Intensive Care Unit or Emergency Department. A machine learning pipeline was developed to segment and assess the severity of subdural hematoma. First, the probability of each point belonging to the hematoma region was determined using a combination of hand-crafted and deep features. This probability provided the initial state of the segmentation. Next, a 3D post-processing model was applied to evolve the initial state and delineate the hematoma. The recall, precision, and Dice similarity coefficient of the proposed segmentation method were 78.61%, 76.12%, and 75.35%, respectively, for the entire population. The Dice similarity coefficient was 79.97% for clinically significant hematomas, which compared favorably to an inter-rater Dice similarity coefficient. In volume-based severity analysis, the proposed model yielded an F1, recall, and specificity of 98.22%, 98.81%, and 92.31%, respectively, in detecting moderate and severe subdural hematomas based on hematoma volume. These results show that the combination of classical image processing and deep learning can outperform deep learning only methods to achieve greater average performance and robustness. Such a system can aid critical care physicians in reducing time to intervention and thereby improve long-term patient outcomes.