QT correction using Bazett’s formula remains preferable in long QT syndrome type 1 and 2

Dahlberg, Pia; Diamant, Ulla‐Britt; Gilljam, Thomas; Rydberg, Annika; Bergfeldt, Lennart

doi:10.1111/anec.12804

Cited by 44 publications

(24 citation statements)

References 27 publications

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“…(also blinded to follow-up). The following parameters were collected and analyzed on the first ECG at admission: PQ interval (ms), QRS interval (ms), and QTc interval (ms) with the Bazett formula in lead DII ( 21 ).…”

Section: Methodsmentioning

confidence: 99%

Incidence and Predictors of Cardiac Arrhythmias in Patients With COVID-19

Mouram

Pannone

Gauthey

et al. 2022

Front. Cardiovasc. Med.

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BackgroundCoronavirus disease 2019 (COVID-19) is a systemic disease caused by severe acute respiratory syndrome coronavirus 2. Arrhythmias are frequently associated with COVID-19 and could be the result of inflammation or hypoxia. This study aimed to define the incidence of arrhythmias in patients with COVID-19 and to correlate arrhythmias with pulmonary damage assessed by computed tomography (CT).MethodsAll consecutive patients with a COVID-19 diagnosis hospitalized at Universitair Ziekenhuis Brussel, Belgium, between March 2020 and May 2020, were screened. All included patients underwent a thorax CT scan and a CT severity score, a semiquantitative scoring system of pulmonary damage, was calculated. The primary endpoint was the arrhythmia occurrence during follow-up.ResultsIn this study, 100 patients were prospectively included. At a mean follow-up of 19.6 months, 25 patients with COVID-19 (25%) experienced 26 arrhythmic episodes, including atrial fibrillation in 17 patients, inappropriate sinus tachycardia in 7 patients, atrial flutter in 1 patient, and third-degree atrioventricular block in 1 patient. No ventricular arrhythmias were documented. Patients with COVID-19 with arrhythmias showed more often need for oxygen, higher oxygen maximum flow, longer QTc at admission, and worse damage at CT severity score. In univariate logistic regression analysis, significant predictors of the primary endpoint were: the need for oxygen therapy (odds ratio [OR] 4.59, 95% CI 1.44–14.67, p = 0.01) and CT severity score of pulmonary damage (OR per 1 point increase 1.25, 95% CI 1.11–1.4, p < 0.001).ConclusionsIn a consecutive cohort of patients with COVID-19 the incidence of cardiac arrhythmias was 25%. The need for oxygen therapy and CT severity score were predictors of arrhythmia occurrence during follow-up.

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Section: Methodsmentioning

confidence: 99%

Incidence and Predictors of Cardiac Arrhythmias in Patients With COVID-19

Mouram

Pannone

Gauthey

et al. 2022

Front. Cardiovasc. Med.

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“…Artificial neural networks can be single-layered or multilayered, depending on their intended use. Interlayers used in multi-layer networks can increase the capability of the network and negatively affect the uptime [16][17][18][19]. Multilayer networks are divided into layers as input layer, middle layer, and output layer.…”

Section: Arrhythmia Detection With Multilayer Neural Networkmentioning

confidence: 99%

Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks

Badr¹,

Al-Otaibi

Alturki

et al. 2022

Computational Intelligence and Neuroscience

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The electrocardiogram, also known as an electrocardiogram (ECG), is considered to be one of the most significant sources of data regarding the structure and function of the heart. In order to obtain an electrocardiogram, the contractions and relaxations of the heart are first captured in the proper recording medium. Due to the fact that irregularities in the functioning of the heart are reflected in the ECG indications, it is possible to use these indications to diagnose cardiac issues. Arrhythmia is the medical term for the abnormalities that might occur in the regular functioning of the heart (rhythm disorder). Environmental and genetic variables can both play a role in the development of arrhythmias. Arrhythmias are reflected on the ECG sign, which depicts the same region regardless of where in the heart they occur; thus, they may be seen in ECG signals. This is how arrhythmias can be detected. Due to the time limits of this study, the ECG signals of individuals who were healthy, as well as those who suffered from arrhythmias were divided into 10-minute segments. The arithmetic mean approach is one of the fundamental statistical factors. It is used to construct the feature vectors of each received wave and interval, and these vectors offer information regarding arrhythmias in accordance with the agreed-upon temporal restrictions. In order to identify the heart arrhythmias, the obtained feature vectors are fed into a classifier that is based on a multilayer perceptron neural network. In conclusion, ROC analysis and contrast matrix are utilised in order to evaluate the overall correct classification result produced by the ECG-based classifier. Because of this, it has been demonstrated that the method that was recommended has high classification accuracy when attempting to diagnose arrhythmia based on ECG indications. This research makes use of a variety of diagnostic terminologies, including ECG signal, multilayer perceptron neural network, signal processing, disease diagnosis, and arrhythmia diagnosis.

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“…We defined acceptable limits of mean difference for QTc as within ±15 ms. Both devices produced QTc readings using the Bazett formula [ 24 ]. Agreement between devices’ automatic ECG interpretation algorithms for diagnosing atrial fibrillation was also assessed.…”

Section: Methodsmentioning

confidence: 99%

User Perceptions and Experiences of a Handheld 12-Lead Electrocardiographic Device in a Clinical Setting: Usability Evaluation

Wong¹,

Thiagalingam²,

Kumar³

et al. 2021

JMIR Cardio

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Background Cardiac arrhythmias are a leading cause of death. The mainstay method for diagnosing arrhythmias (eg, atrial fibrillation) and cardiac conduction disorders (eg, prolonged corrected QT interval [QTc]) is by using 12-lead electrocardiography (ECG). Handheld 12-lead ECG devices are emerging in the market. In tandem with emerging technology options, evaluations of device usability should go beyond validation of the device in a controlled laboratory setting and assess user perceptions and experiences, which are crucial for successful implementation in clinical practice. Objective This study aimed to evaluate clinician and patient perceptions and experiences, regarding the usability of a handheld 12-lead ECG device compared to a conventional 12-lead ECG machine, and generalizability of this user-centered approach. Methods International Organization for Standardization Guidelines on Usability and the Technology Acceptance Model were integrated to form the framework for this study, which was conducted in outpatient clinics and cardiology wards at Westmead Hospital, New South Wales, Australia. Each patient underwent 2 ECGs (1 by each device) in 2 postures (supine and standing) acquired in random sequence. The times taken by clinicians to acquire the first ECG (efficiency) using the devices were analyzed using linear regression. Electrocardiographic parameters (QT interval, QTc interval, heart rate, PR interval, QRS interval) and participant satisfaction surveys were collected. Device reliability was assessed by evaluating the mean difference of QTc measurements within ±15 ms, intraclass correlation coefficient, and level of agreement of the devices in detecting atrial fibrillation and prolonged QTc. Clinicians’ perceptions and feedback were assessed with semistructured interviews based on the Technology Acceptance Model. Results A total of 100 patients (age: mean 57.9 years, SD 15.2; sex: male: n=64, female n=36) and 11 clinicians (experience acquiring ECGs daily or weekly 10/11, 91%) participated, and 783 ECGs were acquired. Mean differences in QTc measurements of both handheld and conventional devices were within ±15 ms with high intraclass correlation coefficients (range 0.90-0.96), and the devices had a good level of agreement in diagnosing atrial fibrillation and prolonged QTc (κ=0.68-0.93). Regardless of device, QTc measurements when patients were standing were longer duration than QTc measurements when patients were supine. Clinicians’ ECG acquisition times improved with usage ( P <.001). Clinicians reported that device characteristics (small size, light weight, portability, and wireless ECG transmission) were highly desired features. Most clinicians agreed that the handheld device could be used for clinician-led mass screening with enhancement in efficiency by increasing user training. Regardless of device, patients reported that they felt comfortable when they were connect...

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QT correction using Bazett’s formula remains preferable in long QT syndrome type 1 and 2

Cited by 44 publications

References 27 publications

Incidence and Predictors of Cardiac Arrhythmias in Patients With COVID-19

Incidence and Predictors of Cardiac Arrhythmias in Patients With COVID-19

Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks

User Perceptions and Experiences of a Handheld 12-Lead Electrocardiographic Device in a Clinical Setting: Usability Evaluation

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