24 research outputs found
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Long-Term Outcomes of Randomized Controlled Trials Comparing Percutaneous Left Atrial Appendage Closure to Oral Anticoagulation for Nonvalvular Atrial Fibrillation: A Meta-Analysis.
BACKGROUND: Oral anticoagulation (OAC) has been considered the standard of care for stroke prophylaxis for patients with nonvalvular atrial fibrillation; however, many individuals are unable or unwilling to take long-term OAC. The safety and efficacy of percutaneous left atrial appendage closure (LAAC) have been controversial, and new trial data have recently emerged. We therefore sought to perform an updated meta-analysis of randomized clinical trials (RCTs) comparing OAC to percutaneous LAAC, focusing on individual clinical endpoints. METHODS: We performed a systematic search of the MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials from January 2000 through December 2021 for all RCTs comparing percutaneous LAAC to OAC in patients with nonvalvular atrial fibrillation. Fixed and random effects meta-analyses of hazard ratios (HRs) were performed using the longest follow-up duration available by intention-to-treat. The prespecified primary endpoint was all-cause mortality. RESULTS: Three RCTs enrolling 1516 patients were identified. The weighted mean follow-up was 54.7 months. LAAC was associated with a reduced risk of all-cause mortality (HR 0.76; 95% confidence interval [CI], 0.59-0.96; p = 0.023), hemorrhagic stroke (HR 0.24; 95% CI, 0.09-0.61; p = 0.003), and major nonprocedural bleeding (HR 0.52; 95% CI, 0.37-0.74; p < 0.001). There was no significant difference between LAAC and OAC for any other endpoints. CONCLUSIONS: The available evidence from RCTs suggests LAAC therapy is associated with reduced long-term risk of death compared with OAC. This may be driven by reductions in hemorrhagic stroke and major nonprocedural bleeding. There were no significant differences in the risk of all stroke. Further large-scale clinical trials are needed to validate these findings
COVID-19 and Thrombotic or Thromboembolic Disease: Implications for Prevention, Antithrombotic Therapy, and Follow-up
Coronavirus disease 2019 (COVID-19), a viral respiratory illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may predispose patients to thrombotic disease, both in the venous and arterial circulations, due to excessive inflammation, platelet activation, endothelial dysfunction, and stasis. In addition, many patients receiving antithrombotic therapy for thrombotic disease may develop COVID-19, which can have implications for choice, dosing, and laboratory monitoring of antithrombotic therapy. Moreover, during a time with much focus on COVID-19, it is critical to consider how to optimize the available technology to care for patients without COVID-19 who have thrombotic disease. Herein, we review the current understanding of the pathogenesis, epidemiology, management and outcomes of patients with COVID-19 who develop venous or arterial thrombosis, and of those with preexisting thrombotic disease who develop COVID-19, or those who need prevention or care for their thrombotic disease during the COVID-19 pandemic.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155446/1/Bikdeli-2020-COVID-19 and Thrombotic or Thromb.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155446/3/DeepBluepermissions_agreement-CCBYandCCBY-NC_ORCID_Barnes.docxhttps://deepblue.lib.umich.edu/bitstream/2027.42/155446/4/license_rdf.rdfDescription of Bikdeli-2020-COVID-19 and Thrombotic or Thromb.pdf : ArticleDescription of DeepBluepermissions_agreement-CCBYandCCBY-NC_ORCID_Barnes.docx : Deep Blue sharing agreemen
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Electroencephalogram Monitoring in Critical Care
Seizures are common in critically ill patients. Electroencephalogram (EEG) is a tool that enables clinicians to provide continuous brain monitoring and to guide treatment decisions-brain telemetry. EEG monitoring has particular utility in the intensive care unit as most seizures in this setting are nonconvulsive. Despite the increased use of EEG monitoring in the critical care unit, it remains underutilized. In this review, we summarize the utility of EEG and different EEG modalities to monitor patients in the critical care setting
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Status epilepticus-time is brain and treatment considerations
Purpose of review Status epilepticus is a neurological emergency associated with high morbidity and mortality. There is a lack of robust data to guide the management of this neurological emergency beyond the initial treatment. This review examines recent literature on treatment considerations including the choice of continuous anesthetics or adjunctive anticonvulsant, the cause of the status epilepticus, and use of nonpharmacologic therapies. Recent findings Status epilepticus remains undertreated and mortality persists to be unchanged over the past 30 years. New anticonvulsant choices, such as levetiracetam and lacosamide have been explored as alternative emergent therapies. Anecdotal reports on the use of other generation anticonvulsants and nonpharmacologic therapies for the treatment of refractory and super-refractory status epilepticus have been described. Finally, recent evidence has examined etiology-guided management of status epilepticus in certain patient populations, such as immune-mediated, paraneoplastic or infectious encephalitis and anoxic brain injury. Randomized clinical trials are needed to determine the role for newer generation anticonvulsants and nonpharmacologic modalities for the treatment of epilepticus remains and evaluate the long-term outcomes associated with continuous anesthetics
The Influence of Therapeutics on Prognostication After Cardiac Arrest.
PURPOSE OF REVIEW: The goal of this review is to highlight the influence of therapeutic maneuvers on neuro-prognostication measures administered to comatose survivors of cardiac arrest. We focus on the effect of sedation regimens in the setting of targeted temperature management (TTM), one of the principle interventions known to improve neurological recovery after cardiac arrest. Further, we discuss the critical need for novel markers, as well as refinement of existing markers, among patients receiving extracorporeal membrane oxygenation (ECMO) in the setting of failed conventional resuscitation, known as extracorporeal cardiopulmonary resuscitation (ECPR).
RECENT FINDINGS: Automated pupillometry may have some advantage over standard pupillary examination for prognostication following TTM, sedation, or the use of ECMO after cardiac arrest. New serum biomarkers such as Neurofilament light chain have shown good predictive abilities and need further validation in these populations. There is a high-level uncertainty in brain death declaration protocols particularly related to apnea testing and appropriate ancillary tests in patients receiving ECMO. Both sedation and TTM alone and in combination have been shown to affect prognostic markers to varying degrees. The optimal approach to analog-sedation is unknown, and requires further study. Moreover, validation of known prognostic markers, as well as brain death declaration processes in patients receiving ECMO is warranted. Data on the effects of TTM, sedation, and ECMO on biomarkers (e.g., neuron-specific enolase) and electrophysiology measures (e.g., somatosensory-evoked potentials) is sparse. The best approach may be one customized to the individual patient, a precision-medicine approach
Interpretable Forecasting of Physiology in the ICU Using Constrained Data Assimilation and Electronic Health Record Data
Prediction of physiologic states are important in medical practice because
interventions are guided by predicted impacts of interventions. But prediction
is difficult in medicine because the generating system is complex and difficult
to understand from data alone, and the data are sparse relative to the
complexity of the generating processes due to human costs of data collection.
Computational machinery can potentially make prediction more accurate, but,
working within the constraints of realistic clinical data makes robust
inference difficult because the data are sparse, noisy and nonstationary. This
paper focuses on prediction given sparse, non-stationary, electronic health
record data in the intensive care unit (ICU) using data assimilation, a broad
collection of methods that pairs mechanistic models with inference machinery
such as the Kalman filter. We find that to make inference with sparse clinical
data accurate and robust requires advancements beyond standard DA methods
combined with additional machine learning methods. Specifically, we show that
combining the newly developed constrained ensemble Kalman filter with machine
learning methods can produce substantial gains in robustness and accuracy while
minimizing the data requirements. We also identify limitations of Kalman
filtering methods that lead to new problems to be overcome to make inference
feasible in clinical settings using realistic clinical data
Perceptions Regarding the SARS-CoV-2 Pandemic\u27s Impact on Neurocritical Care Delivery: Results From a Global Survey
BACKGROUND: The SARS-CoV-2 (COVID-19) pandemic has impacted many facets of critical care delivery. METHODS: An electronic survey was distributed to explore the pandemic\u27s perceived impact on neurocritical care delivery between June 2020 and March 2021. Variables were stratified by World Bank country income level, presence of a dedicated neurocritical care unit (NCCU) and experiencing a COVID-19 patient surge. RESULTS: Respondents from 253 hospitals (78.3% response rate) from 47 countries (45.5% low/middle income countries; 54.5% with a dedicated NCCU; 78.6% experienced a first surge) participated in the study. Independent of country income level, NCCU and surge status, participants reported reductions in NCCU admissions (67%), critical care drug shortages (69%), reduction in ancillary services (43%) and routine diagnostic testing (61%), and temporary cancellation of didactic teaching (44%) and clinical/basic science research (70%). Respondents from low/middle income countries were more likely to report lack of surge preparedness (odds ratio [OR], 3.2; 95% confidence interval [CI], 1.8-5.8) and struggling to return to prepandemic standards of care (OR, 12.2; 95% CI, 4.4-34) compared with respondents from high-income countries. Respondents experiencing a surge were more likely to report conversion of NCCUs and general-mixed intensive care units (ICUs) to a COVID-ICU (OR 3.7; 95% CI, 1.9-7.3), conversion of non-ICU beds to ICU beds (OR, 3.4; 95% CI, 1.8-6.5), and deviations in critical care and pharmaceutical practices (OR, 4.2; 95% CI 2.1-8.2). Respondents from hospitals with a dedicated NCCU were less likely to report conversion to a COVID-ICU (OR, 0.5; 95% CI, 0.3-0.9) or conversion of non-ICU to ICU beds (OR, 0.5; 95% CI, 0.3-0.9). CONCLUSION: This study reports the perceived impact of the COVID-19 pandemic on global neurocritical care delivery, and highlights shortcomings of health care infrastructures and the importance of pandemic preparedness
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Correction: Common Data Elements for Disorders of Consciousness: Recommendations from the Electrophysiology Working Group
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Common Data Elements for Disorders of Consciousness: Recommendations from the Electrophysiology Working Group
BACKGROUNDElectroencephalography (EEG) has long been recognized as an important tool in the investigation of disorders of consciousness (DoC). From inspection of the raw EEG to the implementation of quantitative EEG, and more recently in the use of perturbed EEG, it is paramount to providing accurate diagnostic and prognostic information in the care of patients with DoC. However, a nomenclature for variables that establishes a convention for naming, defining, and structuring data for clinical research variables currently is lacking. As such, the Neurocritical Care Society's Curing Coma Campaign convened nine working groups composed of experts in the field to construct common data elements (CDEs) to provide recommendations for DoC, with the main goal of facilitating data collection and standardization of reporting. This article summarizes the recommendations of the electrophysiology DoC working group. METHODSAfter assessing previously published pertinent CDEs, we developed new CDEs and categorized them into "disease core," "basic," "supplemental," and "exploratory." Key EEG design elements, defined as concepts that pertained to a methodological parameter relevant to the acquisition, processing, or analysis of data, were also included but were not classified as CDEs. RESULTSAfter identifying existing pertinent CDEs and developing novel CDEs for electrophysiology in DoC, variables were organized into a framework based on the two primary categories of resting state EEG and perturbed EEG. Using this categorical framework, two case report forms were generated by the working group. CONCLUSIONSAdherence to the recommendations outlined by the electrophysiology working group in the resting state EEG and perturbed EEG case report forms will facilitate data collection and sharing in DoC research on an international level. In turn, this will allow for more informed and reliable comparison of results across studies, facilitating further advancement in the realm of DoC research