更新于：2024-11-01

Sinus Arrest, Cardiac

心脏窦性停搏

更新于：2024-11-01

基本信息

别名

Cardiac Sinus Arrest、Cardiac Sinus Arrests、Cardiac Sinus Pause

+ [9]

简介

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).

关联

氯化钠/氯化钾/氯化镁/氯化钙/碳酸氢钠

靶点-

作用机制-

在研机构

Fuso Pharmaceutical Industries Ltd.

原研机构

Fuso Pharmaceutical Industries Ltd.

在研适应症

心脏窦性停搏

非在研适应症-

最高研发阶段批准上市

首次获批国家/地区

日本

首次获批日期1999-11-19

项与心脏窦性停搏相关的临床试验

NCT06166277 / RecruitingN/AIIT

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.

开始日期2024-08-01

申办/合作机构

Rush University Medical Center

100 项与心脏窦性停搏相关的临床结果

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100 项与心脏窦性停搏相关的转化医学

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0 项与心脏窦性停搏相关的专利（医药）

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298

项与心脏窦性停搏相关的文献（医药）

2024-08-01·Pacing and Clinical Electrophysiology

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

Article

作者: 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.

2024-08-01·Pediatric Emergency Care

Extracorporeal Cardiopulmonary Resuscitation

Review

作者: 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.

2024-08-01·Medicina Intensiva (English Edition)

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

Article

作者: 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.