Last update 01 Nov 2024

Sinus Arrest, Cardiac

Last update 01 Nov 2024

Basic Info

Synonyms

Cardiac Sinus Arrest, Cardiac Sinus Arrests, Cardiac Sinus Pause

+ [9]

Introduction

The omission of atrial activation that is caused by transient cessation of impulse generation at the SINOATRIAL NODE. It is characterized by a prolonged pause without P wave in an ELECTROCARDIOGRAM. Sinus arrest has been associated with sleep apnea (REM SLEEP-RELATED SINUS ARREST).

Drugs associated with Sinus Arrest, Cardiac

Sodium Chloride/Potassium Chloride/Magnesium Chloride/Calcium Chloride Hydrate/Sodium Bicarbonate

Target-

Mechanism-

Active Org.

Fuso Pharmaceutical Industries Ltd.

Originator Org.

Fuso Pharmaceutical Industries Ltd.

Active Indication

Sinus Arrest, Cardiac

Inactive Indication-

Drug Highest PhaseApproved

First Approval Ctry. / Loc.

First Approval Date19 Nov 1999

Clinical Trials associated with Sinus Arrest, Cardiac

NCT06166277 / RecruitingNot ApplicableIIT

Cardioneuroablation for the Management of Patients With Recurrent Vasovagal Syncope and Symptomatic Bradyarrhythmias: The CNA-FWRD Registry

The CNA-FWRD Registry is an international prospective observational multicenter registry of patients with symptomatic vasovagal syncope and bradyarrhythmias managed under routine care by medical therapy and radiofrequency catheter ablation of ganglionated plexi or permanent pacemaker implantation.

Start Date01 Aug 2024

Sponsor / Collaborator

Rush University Medical Center

100 Clinical Results associated with Sinus Arrest, Cardiac

100 Translational Medicine associated with Sinus Arrest, Cardiac

0 Patents (Medical) associated with Sinus Arrest, Cardiac

298

Literatures (Medical) associated with Sinus Arrest, Cardiac

01 Aug 2024·Pacing and Clinical Electrophysiology

Use of a leadless pacemaker in the management of swallow syncope: A case report

Article

Author: Tofler, Geoffrey ; Chia, Karin K M ; Brieger, Daniel G

AbstractA 41‐year‐old male presented with syncope whilst eating and was subsequently demonstrated to have recurrent symptomatic sinus pauses whilst swallowing. Following the exclusion of structural heart disease, he was diagnosed with swallow syncope, an uncommon variant of neurocardiogenic syncope. To avoid long‐term complications of a transvenous pacemaker, the case was managed with a leadless pacemaker which resulted in complete resolution of symptoms.

01 Aug 2024·Pediatric Emergency Care

Extracorporeal Cardiopulmonary Resuscitation

Review

Author: Stratton, Michael ; Edmunds, Katherine

AbstractExtracorporeal cardiopulmonary resuscitation (eCPR) is increasingly being used for refractory cardiac arrest for both in-hospital and out-of-hospital cardiac arrests. The term eCPR refers to cannulating a patient to an extracorporeal membrane oxygenation (ECMO) circuit to provide perfusion after cardiac arrest refractory to standard cardiopulmonary resuscitation. Extracorporeal cardiopulmonary resuscitation has been shown to offer increased survival benefit among a select group of adult and pediatric patients experiencing refractory cardiac arrests, both in hospital and out of hospital. Extracorporeal cardiopulmonary resuscitation should be considered when (1) the cardiac arrest is witnessed, (2) the patient receives high-quality cardiopulmonary resuscitation, (3) the patient is at or in close proximity to an ECMO center, (4) there is a reversible cause for the cardiac arrest where the perfusion from the ECMO circuit serves as a bridge to recovery, and (5) the treating facility has a robust multidisciplinary system in place to facilitate rapidly moving patients from site of arrest to site of cannulation to intensive care unit. To develop an eCPR system of care, a multidisciplinary team consisting of prehospital, emergency medicine, in-hospital, proceduralist, perfusionist, and intensive care medical professionals must be established who support the use of eCPR for refractory cardiac arrest. The future of eCPR is the development of systems of care that use eCPR for a narrow subset of pediatric out-of-hospital cardiac arrests.

01 Aug 2024·Medicina Intensiva (English Edition)

Application of a machine learning model for early prediction of in-hospital cardiac arrests: Retrospective observational cohort study

Article

Author: Pruenza Garcia-Hinojosa, C ; Martin Garijo, E. ; López Ramos, D ; Martin Garijo, E ; Borges-Sa, M. ; Fiorella Sarubbo, M. ; Serrano García, A. ; Gutiérrez Madroñal, L. ; López Ramos, D. ; Socias Crespí, L. ; Gutiérrez Madroñal, L ; Borges-Sa, M ; Fiorella Sarubbo, M ; Socias Crespí, L ; Pruenza Garcia-Hinojosa, C. ; Serrano García, A

OBJECTIVETo describe the results of the application of a Machine Learning (ML) model to predict in-hospital cardiac arrests (ICA) 24 hours in advance in the hospital wards.DESIGNRetrospective observational cohort study.SETTINGHospital Wards.PATIENTSData were extracted from the hospital's Electronic Health Record (EHR). The resulting database contained a total of 750 records corresponding to 620 different patients (370 patients with ICA and 250 control), between may 2009 and december 2021.INTERVENTIONSNo.MAIN VARIABLES OF INTERESTAs predictors of ICA, a set of 28 variables including personal history, vital signs and laboratory data was employed.MODELSFor the early prediction of ICA, predictive models based on the following ML algorithms and using the mentioned variables, were developed and compared: K Nearest Neighbours, Support Vector Machine, Multilayer Perceptron, Random Forest, Gradient Boosting and Custom Ensemble of Gradient Boosting estimators (CEGB).EXPERIMENTSModel training and evaluation was carried out using cross validation. Among metrics of performance, accuracy, specificity, sensitivity and AUC were estimated.RESULTSThe best performance was provided by the CEGB model, which obtained an AUC = 0.90, a specificity = 0.84 and a sensitivity = 0.81. The main variables with influence to predict ICA were level of consciousness, haemoglobin, glucose, urea, blood pressure, heart rate, creatinine, age and hypertension, among others.CONCLUSIONSThe use of ML models could be of great support in the early detection of ICA, as the case of the CEGB model endorsed, which enabled good predictions of ICA.

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