scholarly journals Metoclopramide-induced cardiac arrest

2011 ◽  
Vol 1 (4) ◽  
pp. 83 ◽  
Author(s):  
Martha M. Rumore ◽  
Spencer Evan Lee ◽  
Steven Wang ◽  
Brenna Farmer
Keyword(s):  
Cardiac Arrest ◽  
Myocardial Injury ◽  
Normal Sinus Rhythm ◽  
Morbidly Obese ◽  
St Depression ◽  
Normal Sinus ◽  
Obese Female ◽  
Artery Disease ◽  
Electrocardiogram Ecg ◽  
Prescribing Information

The authors report a case of cardiac arrest in a patient receiving intravenous (IV) metoclopramide and review the pertinent literature. A 62-year-old morbidly obese female admitted for a gastric sleeve procedure, developed cardiac arrest within one minute of receiving metoclopramide 10 mg via slow intravenous (IV) injection. Bradycardia at 4 beats/min immediately appeared, progressing rapidly to asystole. Chest compressions restored vital function. Electrocardiogram (ECG) revealed ST depression indicative of myocardial injury. Following intubation, the patient was transferred to the intensive care unit. Various cardiac dysrrhythmias including supraventricular tachycardia (SVT) associated with hypertension and atrial fibrillation occurred. Following IV esmolol and metoprolol, the patient reverted to normal sinus rhythm. Repeat ECGs revealed ST depression resolution without pre-admission changes. Metoclopramide is a non-specific dopamine receptor antagonist. Seven cases of cardiac arrest and one of sinus arrest with metoclopramide were found in the literature. The metoclopramide prescribing information does not list precautions or adverse drug reactions (ADRs) related to cardiac arrest. The reaction is not dose related but may relate to the IV administration route. Coronary artery disease was the sole risk factor identified. According to Naranjo, the association was possible. Other reports of cardiac arrest, severe bradycardia, and SVT were reviewed. In one case, five separate IV doses of 10 mg metoclopramide were immediately followed by asystole repeatedly. The mechanism(s) underlying metoclopramide’s cardiac arrest-inducing effects is unknown. Structural similarities to procainamide may play a role. In view of eight previous cases of cardiac arrest from metoclopramide having been reported, further elucidation of this ADR and patient monitoring is needed. Our report should alert clinicians to monitor patients and remain diligent in surveillance and reporting of bradydysrrhythmias and cardiac arrest in patients receiving metoclopramide.

scholarly journals Detection of coronary artery disease by reduced features and extreme learning machine

2018 ◽  
Vol 91 (2) ◽  
pp. 166-175 ◽  
Author(s):  
Ram Sewak Singh ◽  
Barjinder Singh Saini ◽  
Ramesh Kumar Sunkaria
Keyword(s):  
Coronary Artery Disease ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Coronary Artery ◽  
Discriminant Analysis ◽  
Normal Sinus Rhythm ◽  
Normal Sinus ◽  
Hidden Node ◽  
Artery Disease ◽  
Learning Machine

Objective. Cardiovascular diseases generate the highest mortality in the globe population, mainly due to coronary artery disease (CAD) like arrhythmia, myocardial infarction and heart failure. Therefore, an early identification of CAD and diagnosis is essential. For this, we have proposed a new approach to detect the CAD patients using heart rate variability (HRV) signals. This approach is based on subspaces decomposition of HRV signals using multiscale wavelet packet (MSWP) transform and entropy features extracted from decomposed HRV signals. The detection performance was analyzed using Fisher ranking method, generalized discriminant analysis (GDA) and binary classifier as extreme learning machine (ELM). The ranking strategies designate rank to the available features extracted by entropy methods from decomposed heart rate variability (HRV) signals and organize them according to their clinical importance. The GDA diminishes the dimension of ranked features. In addition, it can enhance the classification accuracy by picking the best discerning of ranked features. The main advantage of ELM is that the hidden layer does not require tuning and it also has a fast rate of detection.Methodology. For the detection of CAD patients, the HRV data of healthy normal sinus rhythm (NSR) and CAD patients were obtained from  a standard database. Self recorded data as normal sinus rhythm (Self_NSR) of healthy subjects were also used in this work. Initially, the HRV time-series was decomposed to 4 levels using MSWP transform. Sixty two features were extracted from decomposed HRV signals by non-linear methods for HRV analysis, fuzzy entropy (FZE) and Kraskov nearest neighbour entropy (K-NNE). Out of sixty-two features, 31 entropy features were extracted by FZE and 31 entropy features were extracted by K-NNE method. These features were selected since every feature has a different physical premise and in this manner concentrates and uses HRV signals information in an assorted technique. Out of 62 features, top ten features were selected, ranked by a ranking method called as Fisher score. The top ten features were applied to the proposed model, GDA with Gaussian or RBF kernal + ELM having hidden node as sigmoid or multiquadric. The GDA method transforms top ten features to only one feature and ELM has been used for classification.Results. Numerical experimentations were performed on the combination of datasets as NSR-CAD and Self_NSR- CAD subjects. The proposed approach has shown better performance using top ten ranked entropy features. The GDA with RBF kernel + ELM having hidden node as multiquadric method and GDA with Gaussian kernel + ELM having hidden node as sigmoid or multiquadric method achieved an approximate detection accuracy of 100% compared to ELM and linear discriminant analysis (LDA)+ELM for both datasets. The subspaces level-4 and level-3 decomposition of HRV signals by MSWP transform can be used for detection and analysis of CAD patients.

scholarly journals Classification of cardiac arrhythmias using deep learning

2018 ◽  
Vol 7 (3.3) ◽  
pp. 401
Author(s):  
Jeong Hwan Kim ◽  
Jeong Whan Lee ◽  
Kyeong Seop Kim
Keyword(s):  
Deep Learning ◽  
Normal Sinus Rhythm ◽  
Premature Ventricular Contraction ◽  
Heart Rhythm ◽  
Future Research ◽  
Normal Sinus ◽  
Input Layer ◽  
Hidden Layer ◽  
Electrocardiogram Ecg

Background/Objectives: The main objective of this research is to design Deep Learning (DL) architecture to classify an electrocardiogram (ECG) signal into normal sinus rhythm (NSR), premature ventricular contraction (PVC), atrial premature contraction (APC) or right/left bundle branch block (RBBB/LBBB) arrhythmia by empirically optimizing the numbers of hidden layers, the number of neurons in each hidden layer and the number of neurons in input layer in DL model.Methods/Statistical analysis: For our experimental simulations, PhysioBank-MIT/BIH annotated ECG database was considered to classify heart beats into abnormal rhythms (PVC, APC, RBBB, LBBB) or normal sinus. The performance of classifying ECG beats by the proposed DL architecture was evaluated by computing the overall accuracy of classifying NSR or four different arrhythmias.Findings: Base on testing MIT/BIH arrhythmia database, the proposed DL model can classify the heart rhythm into one of NSR, PVC, APC, RBBB or LBBB beat with the mean accuracy of 95.5% by implementing DL architecture with 200 neurons in input layer, 100 neurons in the first and second hidden layer, respectively and 80 neurons in the 3rd hidden layer.Improvements/Applications: Our experimental results show that the proposed DL model might not be quite accurate for detecting APC beats due to its morphological resemblance of NSR. Therefore, we might need to design more sophisticated DL architecture by including more temporal characteristics of APC to increase the classification accuracy of APC arrhythmia in the future research efforts. 

Abstract 13743: A Tale of Syncope, Prolonged QT and ICD

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Isma N Javed ◽  
Nazir AHMAD ◽  
Deborah J Lockwood ◽  
Karen J J Beckman ◽  
Stavros Stavrakis
Keyword(s):  
Cardiac Arrest ◽  
Family History ◽  
Normal Sinus Rhythm ◽  
Beta Blockers ◽  
Strenuous Exercise ◽  
Caucasian Woman ◽  
Icd Implantation ◽  
Her Family ◽  
Normal Sinus ◽  
The 1960S

Introduction: Long QT syndrome (LQTS) was first described in the 1960s. It manifests clinically as syncope, cardiac arrest or sudden cardiac death. LQTS can be caused by 15 different genes. These mutations lead to action potential prolongation by causing impaired repolarizing currents. Case Discussion: A 29-year-old previously healthy Caucasian woman was admitted after recurrent episodes of syncope that happened within 1-month prior to the presentation. She was hemodynamically stable with normal vitals. Her ECG showed normal sinus rhythm with corrected QT (QTc) of 598ms. In the ED, she suffered an episode of sustained monomorphic ventricular tachycardia (VT) and underwent cardioversion. She was started on amiodarone infusion. Serial ECGs showed prolonged QTc. She had another episode of pulseless VT that terminated without defibrillation. She was transferred to our facility for further care. Her family history was significant for paternal aunt who had died unexpectedly at the age of 39. All her lab work including electrolytes, thyroid panel, cardiac enzymes, inflammatory markers and extended drug screen was unrevealing. Transthoracic echocardiogram showed normal biventricular size and function. Decision making: She was started on propranolol for possible LQTS. Cardiac MR did not show any evidence of structural abnormalities. Genetic panel was sent. Since myocarditis or familial LQTS could not be ruled out, we proceeded with implantable cardioverter defibrillator (ICD) implantation for secondary prevention. She was discharged home on nadolol. Conclusion: In the absence of genetic information, LQTS can be diagnosed in symptomatic patients with QTc >480msec on serial ECGs after excluding secondary causes. Schwartz score comprising of ECG findings, symptoms, clinical & family history is diagnostic when greater than 3.5. Beta-blockers are indicated in all patients with a clinical diagnosis. Patients must avoid any QT prolonging agents and strenuous exercise. An ICD is indicated in patients who suffered cardiac arrest. ICD may also be considered for primary prevention in high risk patients.

scholarly journals Initial Cardiac Rhythm Correlated to Emergency Department Survival

2009 ◽  
Vol 3 ◽  
pp. CMC.S695 ◽  
Author(s):  
Rade B. Vukmir
Keyword(s):  
Cardiac Arrest ◽  
Cardiac Rhythm ◽  
Normal Sinus Rhythm ◽  
Cardiopulmonary Arrest ◽  
Pulseless Electrical Activity ◽  
Clinical Intervention ◽  
Normal Sinus ◽  
Service Environments ◽  
Double Blinded ◽  
Prehospital Cardiac Arrest

Background This study attempted to correlate the initial cardiac rhythm and survival from prehospital cardiac arrest, as a secondary end-point. Methods Prospective, randomized, double-blinded clinical intervention trial where bicarbonate was administered to 874 prehospital cardiopulmonary arrest patients in prehospital urban, suburban, and rural emergency medical service environments. Results This group's manifested an overall survival rate of 13.9% (110 of 793) of prehospital cardiac arrest patients. The most common presenting arrhythmia was ventricular fibrillation (VF) (45.0%), asystole (ASY) (34.4%), and pulseless electrical activity (PEA) (15.7%). Less commonly found were normal sinus rhythm (NSR) (1.8%), other (1.8%), ventricular tachycardia (VT) (0.6%), and atrioventricular block (AVB) (0.5%) as prearrest rhythms. The best survival was noted in those with a presenting rhythm of AVB (57.1%), VT (33.3%), VF (15.7%), NSR (14.3%), PEA (11.2%), and ASY (11.1%) (p = 0.02). However, there was no correlation between the final cardiac rhythm and outcome, other than an obvious end-of-life rhythm. Conclusion The most common presenting arrhythmia was VF (45%), while survival is greatest in those presenting with AVB (57.1%).

DOES ST DEPRESSION HELP PREDICT CORONARY ARTERY DISEASE AFTER AN OUT-OF-HOSPITAL CARDIAC ARREST?

2017 ◽  
Vol 33 (10) ◽  
pp. S133
Author(s):  
C. Cheung ◽  
D. Wan ◽  
B. Grunau ◽  
C. Taylor ◽  
M. Deyell ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Cardiac Arrest ◽  
Coronary Artery ◽  
St Depression ◽  
Artery Disease ◽  
Hospital Cardiac Arrest

CHARACTERIZATION OF VENTRICULAR ARRHYTHMIAS USING A SEMANTIC MINING ALGORITHM

2012 ◽  
Vol 12 (03) ◽  
pp. 1250049 ◽  
Author(s):  
MOHD AFZAN OTHMAN ◽  
NORLAILI MAT SAFRI
Keyword(s):  
Ventricular Arrhythmia ◽  
Ventricular Arrhythmias ◽  
Normal Sinus Rhythm ◽  
High Sensitivity ◽  
Real Data ◽  
Biological Behavior ◽  
Ecg Signals ◽  
Normal Sinus ◽  
Semantic Mining ◽  
Electrocardiogram Ecg

Ventricular arrhythmia, especially ventricular fibrillation, is a type of arrhythmia that can cause sudden death. The aim of this paper is to characterize ventricular arrhythmias using semantic mining by extracting their significant characteristics (frequency, damping coefficient and input signal) from electrocardiogram (ECG) signals that represent the biological behavior of the cardiovascular system. Real data from an arrhythmia database are used after noise filtering and were statistically classified into two groups; normal sinus rhythm (N) and ventricular arrhythmia (V). The proposed method achieved high sensitivity and specificity (98.1% and 97.7%, respectively) and was capable of describing the differences between the N and V types in the ECG signal.

scholarly journals Towards the Development of Nonlinear Approaches to Discriminate AF from NSR Using a Single-Lead ECG

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 531
Author(s):  
Jieun Lee ◽  
Yugene Guo ◽  
Vasanth Ravikumar ◽  
Elena G. Tolkacheva
Keyword(s):  
Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Multiscale Entropy ◽  
Short Term ◽  
Embedding Method ◽  
Ecg Signals ◽  
Normal Sinus ◽  
The Right ◽  
Electrocardiogram Ecg ◽  
Insight Into

Paroxysmal atrial fibrillation (Paro. AF) is challenging to identify at the right moment. This disease is often undiagnosed using currently existing methods. Nonlinear analysis is gaining importance due to its capability to provide more insight into complex heart dynamics. The aim of this study is to use several recently developed nonlinear techniques to discriminate persistent AF (Pers. AF) from normal sinus rhythm (NSR), and more importantly, Paro. AF from NSR, using short-term single-lead electrocardiogram (ECG) signals. Specifically, we adapted and modified the time-delayed embedding method to minimize incorrect embedding parameter selection and further support to reconstruct proper phase plots of NSR and AF heart dynamics, from MIT-BIH databases. We also examine information-based methods, such as multiscale entropy (MSE) and kurtosis (Kt) for the same purposes. Our results demonstrate that embedding parameter time delay ( τ ), as well as MSE and Kt values can be successfully used to discriminate between Pers. AF and NSR. Moreover, we demonstrate that τ and Kt can successfully discriminate Paro. AF from NSR. Our results suggest that nonlinear time-delayed embedding method and information-based methods provide robust discriminating features to distinguish both Pers. AF and Paro. AF from NSR, thus offering effective treatment before suffering chaotic Pers. AF.

Case File: Cardiac amyloidosis using on routine hand-held ultrasound

POCUS Journal ◽  
2016 ◽  
Vol 1 (1) ◽  
pp. 3 ◽  
Author(s):  
Jeffrey Wilkinson, MD
Keyword(s):  
Cardiac Arrest ◽  
Ventricular Tachycardia ◽  
Sinus Rhythm ◽  
General Hospital ◽  
Cardiac Amyloidosis ◽  
Normal Sinus Rhythm ◽  
Case File ◽  
Normal Sinus ◽  
Kingston General Hospital

A 64 year-old man presented to the Kingston General Hospital with cardiac arrest. At the time of EMS arrival, the ECG showed ventricular tachycardia. The patient was intubated and ventilated. Multiple defibrillations were required to convert the patient back to normal sinus rhythm.

scholarly journals A Case report of hemodynamic instability, cardiac arrest, and acute severe dyspnea subsequent to inhalation of crystal methamphetamine

2019 ◽  
Author(s):  
Farhad Gholami ◽  
Seyed Hamzeh Hosseini ◽  
Amirhossein Ahmadi ◽  
Maryam Nabati
Keyword(s):  
Cardiac Arrest ◽  
Ventricular Fibrillation ◽  
Normal Sinus Rhythm ◽  
Hemodynamic Instability ◽  
Acute Dyspnea ◽  
Crystal Methamphetamine ◽  
Electrical Shock ◽  
Mechanically Ventilated ◽  
Normal Sinus ◽  
History Of

Misuse of stimulants similar to amphetamine is a universal problem. These stimulants cause many complications in their abusers. However, myocardial infarction is rarely reported as a complication of amphetamine abuse. Herein, we report a man aged 42 years presented at the Emergency Department with the chief complaint of acute dyspnea following ice inhalation without history of dyspnea. Within the first hour and a half of admission, the patient was treated by nasal oxygen and bronchodilator aminophylline. However, he did not respond to the initial treatment and lost his consciousness; showed ventricular fibrillation, cardiac arrest, and hemodynamic instability. So, cardiopulmonary resuscitation was immediately initiated for him. The patient was intubated, mechanically ventilated. Also, the synchronized electrical shock was delivered 5 times (200-360 J) along with amiodarone (300 mg intravenously [IV] stat, then 1 mg/min IV infusion for 6 hours and next 0.5 mg/min for 18 hours) to treat the ventricular fibrillation. The arrhythmia was subsequently controlled, and his normal sinus rhythm was resumed. Two hours later, condition of the patient improved, and he was extubated. After two days, when the patient got stable, the echocardiography was performed, which was completely normal.

scholarly journals Classification of Atrial Arrhythmias using Neural Networks

2018 ◽  
Vol 7 (2) ◽  
pp. 90 ◽  
Author(s):  
Jai Utkarsh ◽  
Raju Kumar Pandey ◽  
Shrey Kumar Dubey ◽  
Shubham Sinha ◽  
S. S. Sahu
Keyword(s):  
Normal Sinus Rhythm ◽  
High Accuracy ◽  
Ecg Signal ◽  
Atrial Arrhythmias ◽  
Autoregressive Modelling ◽  
Test Set ◽  
Normal Sinus ◽  
The Neural Network ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) is an important tool used by clinicians for successful diagnosis and detection of Arrhythmias, like Atrial Fibrillation (AF) and Atrial Flutter (AFL). In this manuscript, an efficient technique of classifying atrial arrhythmias from Normal Sinus Rhythm (NSR) has been presented. Autoregressive Modelling has been used to capture the features of the ECG signal, which are then fed as inputs to the neural network for classification. The standard database available at Physionet Bank repository has been used for training, validation and testing of the model. Exhaustive experimental study has been carried out by extracting ECG samples of duration of 5 seconds, 10 seconds and 20 seconds. It provides an accuracy of 99% and 94.3% on training and test set respectively for 5 sec recordings. In 10 sec and 20 sec samples it shows 100% accuracy. Thus, the proposed method can be used to detect the arrhythmias in a small duration recordings with a fairly high accuracy.

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