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Heart Arrest

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Value of laboratory results in addition to vital signs in a machine learning algorithm to predict in-hospital cardiac arrest: A single-center retrospective cohort study.

PloS one
BACKGROUND: Although machine learning-based prediction models for in-hospital cardiac arrest (IHCA) have been widely investigated, it is unknown whether a model based on vital signs alone (Vitals-Only model) can perform similarly to a model that cons...

Resting-State Brain Activity for Early Prediction Outcome in Postanoxic Patients in a Coma with Indeterminate Clinical Prognosis.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Early outcome prediction of postanoxic patients in a coma after cardiac arrest proves challenging. Current prognostication relies on multimodal testing, using clinical examination, electrophysiologic testing, biomarkers, and s...

Outcome prediction with resting-state functional connectivity after cardiac arrest.

Scientific reports
Predicting outcome in comatose patients after successful cardiopulmonary resuscitation is challenging. Our primary aim was to assess the potential contribution of resting-state-functional magnetic resonance imaging (RS-fMRI) in predicting neurologica...

Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography.

Scandinavian journal of trauma, resuscitation and emergency medicine
BACKGROUND: In-hospital cardiac arrest is a major burden in health care. Although several track-and-trigger systems are used to predict cardiac arrest, they often have unsatisfactory performances. We hypothesized that a deep-learning-based artificial...

A predictive framework in healthcare: Case study on cardiac arrest prediction.

Artificial intelligence in medicine
Data-driven healthcare uses predictive analytics to enhance decision-making and personalized healthcare. Developing prognostic models is one of the applications of predictive analytics in medical environments. Various studies have used machine learni...

Prediction of Neurological Outcomes in Out-of-hospital Cardiac Arrest Survivors Immediately after Return of Spontaneous Circulation: Ensemble Technique with Four Machine Learning Models.

Journal of Korean medical science
BACKGROUND: We performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning...

Rapid Response System Restructure: Focus on Prevention and Early Intervention.

Critical care nursing quarterly
This article describes the staged restructure of the rapid response program into a dedicated 24/7 proactive rapid response system in a quaternary academic medical center in the southern United States. Rapid response nurses (RRNs) completed clinical l...

Automated Assessment of Brain CT After Cardiac Arrest-An Observational Derivation/Validation Cohort Study.

Critical care medicine
OBJECTIVES: Prognostication of outcome is an essential step in defining therapeutic goals after cardiac arrest. Gray-white-matter ratio obtained from brain CT can predict poor outcome. However, manual placement of regions of interest is a potential s...

Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest.

Critical care medicine
OBJECTIVES: Prognostication of neurologic status among survivors of in-hospital cardiac arrests remains a challenging task for physicians. Although models such as the Cardiac Arrest Survival Post-Resuscitation In-hospital score are useful for predict...