Active clinical trials for "Heart Arrest"

A cardiac arrest is often preceeded by a varying period of physiological deterioration which if acted upon may prevent the cardiac arrest. We aim to review patients presenting to the ambulance service with cardiac arrest so see if they had contacted the ambulance service in the preceeding 48 hrs to understand if warning symptoms were missed or not acted upon appropriately.

Early prognosis of cardiac arrest - nearly 40000 new cases per year in France - has been extensively studied, highlighting a poor outcome (less than 8% at hospital discharge). However, little is known on mid and long-term prognostic factors and how these patients do survive from the event. Using a regional cohort, the aim of the study is to describe long-term survival rate of cardiac arrest survivors, and to assess the influence of treatment strategies on survival and functional outcome.

The aim of this study is to investigate the predictive value of the presence or absence of cardiac activity by bedside ultrasound during cardiac arrest. The investigators anticipate based on previous research that patients who suffer a cardiac arrest and have cardiac activity on bedside ultrasound will have a greater chance of surviving to hospital admission. The investigators hypothesis is that the mortality rate in patients in cardiac arrest with no cardiac activity by ultrasound will have a mortality rate of 100%.

Sudden cardiac death remains one of the major leading causes of death. Therapeutic hypothermia is a validated standard procedure to avoid or minimize cognitive deficits after cardiac arrest. To assess the efficiency of different cooling methods and further improve these methods, the investigators collected blood samples to measure the neuron specific enolase (NSE) in patients treated with invasive cooling as compared to patients treated with non-invasive cooling.

The main goal of this project is to help 9-1-1 emergency medical dispatchers save the lives of more cardiac arrest victims. The investigators will develop teaching tools to help the dispatchers recognize abnormal breathing that may indicate a victim as having a cardiac arrest. After training sessions, the investigators will see if dispatchers can get better at recognizing abnormal breathing, how often they give CPR instructions, and if use of the teaching tool will increase bystander CPR and the number of victims leaving the hospital alive.

Veno-arterial extracorporeal life support (VA-ELS) is used to support patients with acute cardiac failure. In that context, sufficient drainable venous volume is crucial for reliable and adequate support. To date, no reliable measurement method exists to monitor drainable volume adequately. Furthermore, it is still unresolved how to diagnose adequate cardiac recovery. Previous (pre)clinical studies showed that the calculation of the dynamic filling index may provide a valuable parameter to monitor the drainable volume in patients supported by VAELS. In addition, a case report showed that measurement of the dynamic filling index could successfully be used to estimate cardiac recovery in a single patient supported by VA-ELS in the ICU. This study will assess the dynamic filling index as a representation for drainable volume and cardiac recovery using standard recorded perfusion data in patients supported by VA-ELS

Early prognostication in resuscitated cardiac arrest (CA) patients, within the first day after admission in the intensive care unit (ICU), remains difficult. Optic nerve sheath diameter (ONSD) measurement could predict increased intracranial pressure. Preliminary works showed that ONSD measurement could be used to predict outcome in post-CA patients. The aim of this study was to assess the ability of bedside ONSD ultrasonographic measurement to predict survival and Cerebral Performance Category (CPC) score at hospital discharge in post-cardiac arrest patients treated with therapeutic hypothermia (TH).

Background: Foreign body airway obstruction (FBAO) is often described as an uncommon cause of Out of Hospital Cardiac Arrest (OHCA) accounting for approximately 1.4% of all OHCA. Reported incidents rates of FBAO causing cardiac arrest are unclear, and first aid by layperson are not well described. The aim of the epidemiological part of the study is: to investigate information on actions taken by EMS-personnel and laypersons to investigate outcomes of hypoxic Cardiac Arrest due to foreign body airway obstruction in Denmark to increase overall survival. propose new guidelines and strategies to increase survival from OHCA caused by FBAO. The aim of advanced text-string search algorithm part of the study is - To investigate if an advanced text-string search algorithm can identify FBAO in medical records with high sensitivity Methods: National data will be collected from the verified 2016-2019 Danish OHCA register, and cases with FBAO prior to OHCA will selected via a direct marking by external validation and advanced text search. Patients reported as indisputably deceased (late signs of death) was excluded. Incidence rates per 100.000 citizens, survival rates to hospital and first aid actions by layperson are presented. A pilot study have been conducted in regional data from 2016-2019 and the study group have concluded that, a national study is feasible with the current amount of data and the used methodology. Expected outcome: This study will enable targeted campaigns aimed at increasing survival from OHCA caused by FBAO. Potential campaigns might target the food items provided to potential vulnerable groups and guide focus for first aid recommendations. Further, with a deeper understanding of which airway management procedures most often are successful, it will be possible to improve EMS treatments of vulnerable groups. Finally, a novel method of extracting information from the electronic medical records will be developed creating the foundation for future works on other prehospital conditions

The overall incidence of cardiorespiratory arrest in Europe is estimated at 350,000 to 700,000 cases per year. Survival rate is estimated at 10.7% for all rhythm disorders combined. Several examples of AI application in the medical field exist. Ting et al have developed a computer tool capable of diagnosing the presence of diabetic retinopathy with excellent power. In resuscitation, Celi et al proposed a tool capable of predicting the need for crystalloid vascular filling during a systemic inflammatory state. In Nature in 2018, Komorowski demonstrated the efficacy of AI in the hemodynamic management of sepsis. In a study of the renal response to fluid challenge, Zhang et al. demonstrate the effectiveness of the learning machine. Objectives: Determination of an algorithm capable of predicting the mortality of patients admitted to intensive care units (ICU) for ACR from hospitalization reports (CRH). Also use of the algorithm to predict the risk of recurrence of the arrest, the duration of mechanical ventilation, the appearance of sepsis, the development of organ failure, prediction of the CPC (Cerebral Performance Category), time to obtain catecholamine withdrawal, the appearance of acute renal failure with or without the need for extra-renal purification (EER) and duration under EER, the average length of stay. This project is part of a larger, nationwide project with greater power, and includes all the data generated during hospitalization in intensive care. Method: an estimated total number of patients included in this study to be between 300 and 500. The population will come from the intensive care units of Nice, Antibes, Cannes, Grasse. Inclusion will be retrospective, on CRH, CR of CT imaging (cerebral and thoraco-abdomino-pelvic), MRI, EEG, and daily follow-up words, from 2014 to the end of 2020. After anonymisation, application of semantisation using natural language processing (NLP) methods. The data to be extracted are entered in a document written by intensive care physicians. These data will then be stored in a database. In order to meet the main objective, we will develop a computer algorithm capable of predicting mortality in the study population. This algorithm, based on a large database, can be designed using machine learning or even deep learning techniques depending on the amount of data to be processed.

While regular exercise is known to prevent cardiovascular disorders, exercise might also trigger acute cardiac events such as cardiac arrest. This study examines the incidence, prognosis and outcomes of out-of-hospital cardiac arrest (OHCA) related to exercise in the general population of Denmark. This retrospective cohort study examins all OHCAs in the Danish Cardiac Arrest Registry from 2016 to 2019 in Denmark. OHCA related to exercise were identified in the nationwide electronic database and coupled to the patient register. OHCA related to exercise was defined as occurring during or up to one hour after exercise of moderate or vigorous intensity. Descriptive statistics are used together with an adjusted multivariate logistic regression model to assess predefined factors of interest.