BACKGROUND AND OBJECTIVE

COVID-19 has been associated with cardiac injury, often detectable through electrocardiographic (ECG) changes. This study seeks to characterize the cardiovascular and electrocardiographic profiles of adult patients diagnosed with COVID-19.

This study included adult patients with confirmed COVID-19 from June 2021 to June 2022. Clinical profiles and 12-lead ECG tracings were obtained from electronic medical records and reviewed independently by three cardiologists. Descriptive analysis was performed to summarize the cardiovascular and electrocardiographic findings in this population.

The study included 998 COVID-19 patients (mean age: 50 years; 53.7% male). The most common comorbidities were hypertension, diabetes, and dyslipidemia. A majority (31.36%) presented with severe COVID-19 infection. The most frequent significant ECG abnormalities observed at admission were sinus tachycardia (22.8%), and atrial fibrillation (11.02%). Additional ischemic findings included ST segment depression (2.91%), T-wave inversion (1.70%), and ST segment elevation (2.71%).

The baseline ECG findings among COVID-19 patients were predominantly normal; however, significant abnormalities were also identified. The most frequent abnormalities included sinus tachycardia, atrial fibrillation, and ischemic changes, all of which may have clinical implications.

In the field of medicine and cardiology, there is perhaps no other condition or situation that stimulates an adrenalin rush for the healthcare team than a patient presenting with wide QRS complex tachycardia. These cases may be potentially fatal and are usually associated with worse outcomes. While the real-world experience in the evaluation and management of these cases can be chaotic situations, a careful, systematic and organized scrutiny of the electrocardiographic tracing is key to obtaining a correct diagnosis and proceeding with the right therapeutic management. An understanding of the physiological mechanisms of arrhythmia, the appreciation of scientific basis for electrocardiographic features and recognition of different criteria for diagnosis provides endless opportunities and “teachable moments” in medicine. For both learners and teachers, the academic discussion of these points and features can be an exciting journey and electrifyingly educational experience. This article provides a simplified yet beautifully complicated approach to diagnosing wide complex tachycardia.

BACKGROUND AND OBJECTIVE

COVID-19 has been associated with cardiac injury, often detectable through electrocardiographic (ECG) changes. This study seeks to characterize the cardiovascular and electrocardiographic profiles of adult patients diagnosed with COVID-19.

This study included adult patients with confirmed COVID-19 from June 2021 to June 2022. Clinical profiles and 12-lead ECG tracings were obtained from electronic medical records and reviewed independently by three cardiologists. Descriptive analysis was performed to summarize the cardiovascular and electrocardiographic findings in this population.

The study included 998 COVID-19 patients (mean age: 50 years; 53.7% male). The most common comorbidities were hypertension, diabetes, and dyslipidemia. A majority (31.36%) presented with severe COVID-19 infection. The most frequent significant ECG abnormalities observed at admission were sinus tachycardia (22.8%), and atrial fibrillation (11.02%). Additional ischemic findings included ST segment depression (2.91%), T-wave inversion (1.70%), and ST segment elevation (2.71%).

The baseline ECG findings among COVID-19 patients were predominantly normal; however, significant abnormalities were also identified. The most frequent abnormalities included sinus tachycardia, atrial fibrillation, and ischemic changes, all of which may have clinical implications.

The electrocardiographic analysis of heart blocks provides great opportunities for the discussion of mechanisms of electrical cardiac conduction, serving as “teachable moments” in medicine. Recognition of heart blocks can sometimes be a challenge as they can present in many forms, different severities and levels of blocks that present as varied patterns on electrocardiographic tracing. The ultimate key to correct diagnosis rests on adequate understanding of normal electrophysiology of the electrical system of the heart. While it is vital to recognize the pattern, we should always know and understand the physiology behind the pattern. This article presents a detailed analysis of a case of heart block which can easily be misinterpreted on first look. The case is featured not for its rarity but for the interesting concepts in cardiac electrophysiology that are highlighted. Navigation of the different elements of tracing can be an adventure and a great learning experience enjoyed by both students and experts.
Heart Block ; Electrocardiography

OBJECTIVES

The study aimed to determine the association of electrocardiographic (ECG) abnormalities and in-hospital mortality of patients with coronavirus disease 2019 (COVID-19) infection admitted in a tertiary care hospital in the Philippines.

We conducted a retrospective study of confirmed COVID-19–infected patients. Demographic and clinical characteristics and clinical outcomes were extracted from the medical records. Electrocardiographic analysis was derived from the 12-lead electrocardiogram recorded upon admission. The frequencies and distributions of various clinical characteristics were described, and the ECG abnormalities associated with in-hospital mortality were investigated.

A total of 163 patients were included in the study; most were female (52.7%) with a median age of 55 years. Sinus rhythm with any ECG abnormality (65%), nonspecific ST and T-wave changes (35%), and sinus tachycardia (22%) were the frequently reported ECG findings. The presence of any ECG abnormality was detected in 78.5% of patients, and it was significantly associated with in-hospital mortality (P = 0.038). The analysis revealed a statistically significant association between in-hospital mortality and having atrial fibrillation or flutter (P = 0.002), supraventricular tachycardia (P = 0.011), ventricular tachycardia (P = 0.011), third-degree atrioventricular block (P = 0.011), T-wave inversion (P = 0.005), and right ventricular hypertrophy (P = 0.011).

The presence of any ECG abnormality in patients with COVID-19 infection was associated with in-hospital mortality. Electrocardiographic abnormalities that were associated with mortality were atrial fibrillation or flutter, supraventricular tachycardia, ventricular tachycardia, third-degree atrioventricular block, T-wave inversion, and right ventricular hypertrophy.

Humans ; Artificial Intelligence ; Heart Failure/therapy* ; Electrocardiography

Bradyarrhythmias are commonly encountered in clinical practice. While there are several electrocardiographic criteria and algorithms for tachyarrhythmias, there is no algorithm for bradyarrhythmias to the best of our knowledge. In this article, we propose a diagnostic algorithm that uses simple concepts: (1) the presence or absence of P waves, (2) the relationship between the number of P waves and QRS complexes, and (3) the regularity of time intervals (PP, PR and RR intervals). We believe this straightforward, stepwise method provides a structured and thorough approach to the wide differential diagnosis of bradyarrhythmias, and in doing so, reduces misdiagnosis and mismanagement.
Humans ; Bradycardia/therapy* ; Algorithms ; Diagnosis, Differential ; Electrocardiography

BACKGROUND: China bears the biggest atrial fibrillation (AF) burden in the world. However, little is known about the incidence and predictors of AF. This study aimed to investigate the current incidence of AF and its electrocardiographic (ECG) predictors in general community individuals aged over 60 years in China. METHODS: This was a prospective cohort study, recruiting subjects who were aged over 60 years and underwent annual health checkups from April to July 2015 in four community health centers in Songjiang District, Shanghai, China. The subjects were then followed up from 2015 to 2019 annually. Data on sociodemographic characteristics, medical history, and the resting 12-lead ECG were collected. Kaplan-Meier curve was used for showing the trends in AF incidence and calculating the predictors of AF. Associations of ECG abnormalities and AF incidence were examined using Cox proportional hazard models. RESULTS: This study recruited 18,738 subjects, and 351 (1.87%) developed AF. The overall incidence rate of AF was 5.2/1000 person-years during an observation period of 67,704 person-years. Multivariable Cox regression analysis indicated age (hazard ratio [HR], 1.07; 95% confidence interval [CI]: 1.06-1.09; P < 0.001), male (HR, 1.30; 95% CI: 1.05-1.62; P = 0.018), a history of hypertension (HR, 1.55; 95% CI: 1.23-1.95; P < 0.001), a history of cardiac diseases (HR, 3.23; 95% CI: 2.34-4.45; P < 0.001), atrial premature complex (APC) (HR, 2.82; 95% CI: 2.17-3.68; P < 0.001), atrial flutter (HR, 18.68; 95% CI: 7.37-47.31; P < 0.001), junctional premature complex (JPC) (HR, 3.57; 95% CI: 1.59-8.02; P = 0.002), junctional rhythm (HR, 18.24; 95% CI: 5.83-57.07; P < 0.001), ventricular premature complex (VPC) (HR, 1.76; 95% CI: 1.13-2.75, P = 0.012), short PR interval (HR, 5.49; 95% CI: 1.36-22.19; P = 0.017), right atrial enlargement (HR, 6.22; 95% CI: 1.54-25.14; P = 0.010), and pacing rhythm (HR, 3.99; 95% CI: 1.57-10.14; P = 0.004) were independently associated with the incidence of AF. CONCLUSIONS The present incidence of AF was 5.2/1000 person-years in the studied population aged over 60 years in China. Among various ECG abnormalities, only APC, atrial flutter, JPC, junctional rhythm, short PR interval, VPC, right atrial enlargement, and pacing rhythm were independently associated with AF incidence.
Humans ; Male ; Middle Aged ; Aged ; Atrial Fibrillation/epidemiology* ; Prospective Studies ; Incidence ; Atrial Flutter/complications* ; Risk Factors ; China/epidemiology* ; Electrocardiography

Fetal electrocardiogram (ECG) signals provide important clinical information for early diagnosis and intervention of fetal abnormalities. In this paper, we propose a new method for fetal ECG signal extraction and analysis. Firstly, an improved fast independent component analysis method and singular value decomposition algorithm are combined to extract high-quality fetal ECG signals and solve the waveform missing problem. Secondly, a novel convolutional neural network model is applied to identify the QRS complex waves of fetal ECG signals and effectively solve the waveform overlap problem. Finally, high quality extraction of fetal ECG signals and intelligent recognition of fetal QRS complex waves are achieved. The method proposed in this paper was validated with the data from the PhysioNet computing in cardiology challenge 2013 database of the Complex Physiological Signals Research Resource Network. The results show that the average sensitivity and positive prediction values of the extraction algorithm are 98.21% and 99.52%, respectively, and the average sensitivity and positive prediction values of the QRS complex waves recognition algorithm are 94.14% and 95.80%, respectively, which are better than those of other research results. In conclusion, the algorithm and model proposed in this paper have some practical significance and may provide a theoretical basis for clinical medical decision making in the future.
Algorithms ; Neural Networks, Computer ; Electrocardiography ; Databases, Factual ; Fetus

OBJECTIVE: To use the low-cost anesthesia monitor for realizing anesthesia depth monitoring, effectively assist anesthesiologists in diagnosis and reduce the cost of anesthesia operation. METHODS: Propose a monitoring method of anesthesia depth based on artificial intelligence. The monitoring method is designed based on convolutional neural network (CNN) and long and short-term memory (LSTM) network. The input data of the model include electrocardiogram (ECG) and pulse wave photoplethysmography (PPG) recorded in the anesthesia monitor, as well as heart rate variability (HRV) calculated from ECG, The output of the model is in three states of anesthesia induction, anesthesia maintenance and anesthesia awakening. RESULTS: The accuracy of anesthesia depth monitoring model under transfer learning is 94.1%, which is better than all comparison methods. CONCLUSIONS The accuracy of this study meets the needs of perioperative anesthesia depth monitoring and the study reduces the operation cost.
Artificial Intelligence ; Neural Networks, Computer ; Heart Rate ; Electrocardiography ; Photoplethysmography/methods* ; Anesthesia