Neurologic complications of cardiac arrest

Gill, Rick; Teitcher, Michael; Ruland, Sean

doi:10.1016/b978-0-12-819814-8.00029-9

Cited by 6 publications

(6 citation statements)

References 104 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neurologic complications in COVID-19–related heart and lung injury occur through mechanisms discussed earlier. For further review, please refer to the article “Neurologic Complications of COVID-19” by Christine Gill, MD, and Tracey A. Cho, MD, FAAN, 83 in this issue of Continuum .…”

Section: Effects Of Covid-19–associated Cardiorespiratory Illnessmentioning

confidence: 99%

Neurologic Complications of Cardiac and Pulmonary Disease

Mainali¹

2023

CONTINUUM: Lifelong Learning in Neurology

View full text Add to dashboard Cite

OBJECTIVE The heart and lungs work as a functional unit through a complex interplay. The cardiorespiratory system is responsible for the delivery of oxygen and energy substrates to the brain. Therefore, diseases of the heart and lungs can lead to various neurologic illnesses. This article reviews various cardiac and pulmonary pathologies that can lead to neurologic injury and discusses the relevant pathophysiologic mechanisms. LATEST DEVELOPMENTS We have lived through unprecedented times over the past 3 years with the emergence and rapid spread of the COVID-19 pandemic. Given the effects of COVID-19 on the lungs and heart, an increased incidence of hypoxic-ischemic brain injury and stroke associated with cardiorespiratory pathologies has been observed. Newer evidence has questioned the benefit of induced hypothermia in patients with out-of-hospital cardiac arrest. Further, global collaborative initiatives such as the Curing Coma Campaign are underway with the goal of improving the care of patients with coma and disorders of consciousness, including those resulting from cardiac and pulmonary pathologies. ESSENTIAL POINTS The neurologic complications of cardiorespiratory disorders are common and present in various forms such as stroke or hypoxic and anoxic injury related to cardiac or respiratory failure. With the emergence of the COVID-19 pandemic, neurologic complications have increased in recent years. Given the intimate and interdependent dynamics of the heart, lungs, and brain, it is crucial for neurologists to be aware of the interplay between these organs.

show abstract

Section: Effects Of Covid-19–associated Cardiorespiratory Illnessmentioning

confidence: 99%

Neurologic Complications of Cardiac and Pulmonary Disease

Mainali¹

2023

CONTINUUM: Lifelong Learning in Neurology

View full text Add to dashboard Cite

show abstract

“…Cardiac arrest (CA) is still a globally important public health concern, 1 affecting 550 000 individuals annually in China, 2 almost 375 000 people every year in Europe, 3 and around 600 000 cases each year in the United States 4 . Although, over time, there has been improvement in the care of patients during and after CA, the general prognosis of these patients is still not ideal, especially their neurological outcome 5,6 . A recent large study from two nationwide, prospective registries in Denmark 7 showed that out‐of‐hospital CA (OHCA) patients (26.84%) have a significantly lower rate to show the return of spontaneous circulation (ROSC) than in‐hospital CA (IHCA) patients (48.50%), and the overall survival rate over 1 year for both the OHCA and IHCA patients is only 12.62% and 18.45%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Relationship between initial red cell distribution width and ΔRDW and mortality in cardiac arrest patients

Zhong,

Zhang,

et al. 2023

ESC Heart Failure

View full text Add to dashboard Cite

AimsThere has been a lack of research examining the relationship between red cell distribution width (RDW) and the prognosis of cardiac arrest (CA) patients. The prognostic value of the changes in RDW during intensive care unit (ICU) hospitalization for CA patients has not been investigated. This study aims to investigate the correlation between RDW measures at ICU admission and RDW changes during ICU hospitalization and the prognosis of CA patients and then develop a nomogram that predicts the risk of mortality of these patients.Methods and resultsA retrospective cohort study is used to collect clinical characteristics of CA patients (>18 years) that are on their first admission to ICU with RDW data measured from the Medical Information Mart for Intensive Care IV Version 2.0 database. Patients are randomly divided into a development cohort (75%) and a validation cohort (25%). The primary outcome is 30 and 360 day all‐cause mortality. ΔRDW is defined as the RDW on ICU discharge minus RDW on ICU admission. A multivariate Cox regression model is applied to test whether the RDW represents an independent risk factor that affects the all‐cause mortality of these patients. Meanwhile, the dose–response relationship between the RDW and the mortality is described by restricted cubic spine (RCS). A prediction model is constructed using a nomogram, which is then assessed using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). A total of 1278 adult CA patients are included in this study. We found that non‐survivors have a higher level of RDW and ΔRDW compared with survivors, and the mortality rate is higher in the high RDW group than in the normal RDW group. The Kaplan–Meier survival curve indicates that patients in the normal RDW group had a higher cumulative survival rate at 30 and 360 days than those in the high RDW group (log‐rank test, χ2 = 36.710, χ2 = 54.960, both P values <0.05). The multivariate Cox regression analysis shows that elevated RDW at ICU admission (>15.50%) is an independent predictor of 30 [hazard ratio = 1.451, 95% confidence interval (CI) = 1.181–1.782, P < 0.001] and 360 day (hazard ratio = 1.393, 95% CI = 1.160–1.671, P < 0.001) all‐cause mortality among CA patients, and an increase in RDW during ICU hospitalization (ΔRDW ≥ 0.4%) can serve as an independent predictor of mortality among these patients. A non‐linear relationship between the RDW measured at ICU admission and the increased risk of mortality rate of these patients is shown by the RCS. This study established and validated a nomogram based on six variables, anion gap, first‐day Sequential Organ Failure Assessment score, cerebrovascular disease, malignant tumour, norepinephrine use, and RDW, to predict mortality risk in CA patients. The consistency indices of 30 and 360 day mortality of CA patients in the validation cohort are 0.721 and 0.725, respectively. The nomogram proved to be well calibrated in the validation cohort. DCA curves indicated that the nomogram provided a higher net benefit over a wide, reasonable range of threshold probabilities for predicting mortality in CA patients and could be adapted for clinical decision‐making.ConclusionsElevated RDW levels on ICU admission and rising RDW during ICU hospitalization are powerful predictors of all‐cause mortality for CA patients at 30 and 360 days, and they can be used as potential clinical biomarkers to predict the bad prognosis of these patients. The newly developed nomogram, which includes RDW, demonstrates high efficacy in predicting the mortality of CA patients.

show abstract

“…The post-resuscitation care for IHCA was analyzed in a study by Girotra et al [6], which included targeted temperature management, early coronary angiography, and comprehensive critical care. The analysis of therapeutic interventions and prognostication of IHCA's neurological complications have also been presented [7]. Simultaneously, an analysis of the induced consciousness method during CPR [8] and the impact of various airway management methods (e.g., endotracheal intubation and supraglottic airway) on the outcome of OHCA [9] were proposed.…”

Section: Introductionmentioning

confidence: 99%

An intelligent diagnosis and treatment system for in-hospital cardiac arrest based on deep reinforcement learning in the Utstein style

Shao

Zhang

Chen

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Both in-hospital and out-of-hospital cardiac arrest have several causes and complexities. Therefore, it is difficult to analyze and create targeted treatment plans for cardiac arrest. Moreover, even basic patient informationis insufficient or missing in many cases. To address these challenges, we developed an intelligent diagnosis and treatment system for cardiopulmonary resuscitation and restoration of spontaneous circulation to reasonably complete the diagnosis and treatment process and improve the success rate of treatment during in-hospital cardiac arrest. Methods: Using 10 years of data from Utstein style in-hospital cardiac arrest registries in seven hospitals in China’s Hebei Province, we developed a two-stage intelligent diagnosis and treatment system based on the basic situation of different cases during cardiac arrest using deep reinforcement learning. This approach is aimed at maximizing the success rates of cardiopulmonary resuscitation and maintaining blood pressure at an optimal value during the restoration of spontaneous circulation. We employed Monte Carlo simulation to analyze the uncertainty. Results: When comparing the actual diagnosis and treatment with those based on other models during cardiopulmonary resuscitation and restoration of spontaneous circulation in different conditions (sufficient and insufficient basic patient information), the average reward scores of the treatment plan provided by the intelligent diagnosis and treatment system developed in this study increased to 9.9–23 from 2.39. Conclusions: The intelligent diagnosis and treatment system for in-hospital cardiac arrest based on deep reinforcement learning under the Utstein mode developed in this study can propose a reasonable diagnosis and treatment plan when in-hospital cardiac arrest occurs. Moreover, it can effectively induce cardiopulmonary resuscitation and restoration of spontaneous circulation processes in different patients even when basic patient informationis insufficient or missing.

show abstract

Neurologic complications of cardiac arrest

Cited by 6 publications

References 104 publications

Neurologic Complications of Cardiac and Pulmonary Disease

Neurologic Complications of Cardiac and Pulmonary Disease

Relationship between initial red cell distribution width and ΔRDW and mortality in cardiac arrest patients

An intelligent diagnosis and treatment system for in-hospital cardiac arrest based on deep reinforcement learning in the Utstein style

Contact Info

Product

Resources

About