24 research outputs found

    COVID-19 and Thrombotic or Thromboembolic Disease: Implications for Prevention, Antithrombotic Therapy, and Follow-up

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    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

    The Influence of Therapeutics on Prognostication After Cardiac Arrest.

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    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

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    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

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    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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