Predictors of rhythm disorders following cardiac surgery

Funding Acknowledgements Type of funding sources: None. Background. Identification of high-risk patients and predictors for cardiac arrhythmias allows the development of preventive measures that will improve the postoperative period and rehabilitation of patients following cardiac surgery. Purpose. To determine probable predictors of arrhythmias in patients following cardiac surgery in the early postoperative period (up to 7 days). Methods. 56 patients were examined, including 19 (33.9%) men (p = 0.02). The average age of the patients was 60.86 ± 8.87 years. Cardiac surgery was performed for coronary heart disease in 37 (66.1%) and valvular heart defects in 19 (33.9%) patients (p = 0.02). The average duration of the operations was 371.94 ± 102.04 minutes. In 25 (44.6%) cases, the operations were performed in conditions of bypass, the average duration of which did not differ from operations without bypass (389.44 vs. 355.47, p = 0.34). Assessment of arrhythmias was performed during the first 7 days after cardiac surgery. Results. 27 (48.2%) patients have developed arrhythmias within first 7 days, among them in 12 (63.2%) women and 15 (40.5%) men (p = 0.24). Predictors of arrhythmias in early postoperative period are: operation with bypass r = 0,332 [1,06-3,03], p = 0,01; the size of left atrium (LA)> 40 mm r =0,296 [1,01-3,31], p = 0,03; presence of coronary artery (CA) stenosis r = 0,139 [1,11-2,69], p = 0,04; atrial fibrillation (AF) in the history r = 0,607 [1,99-5,58], p = 0,001. AF dominates in the structure of arrhythmias – in 17 (30.4%) patients, among them – in 11 (64.7%) was paroxysmal, 6 (35.3%) patients - persistent form. The mean score of CHA2DS2VASc scale - 2.56 ± 0.89. The predictors of AF are: operation with bypass r = 0.451 [1.63-9.76], p= 0.0001; the size of the LA> 40 mm r = 0.303 [1.04-7.58], p = 0.02; left ventricle (LV) ejection fraction (EF) <40% r = -0.207 [1.23-1.82], p = 0.05; mitral regurgitation r = 0.314 [1.05-10.04], p = 0.02. Also registered ventricular ventricular prematute beats (VPB) – in 12 (21.4%) patients, among them VPB 1st Laun class - in 8 (66.7%), 2nd Laun class – in 2 (16.7%), 3rd Laun class – in 1 (8.3%), 4A Laun class – in 1 (8.3%) patient, respectively. Predictors of VPB are: chronic heart failure (CHF) 4 functional class (FC) r = 0.258 [2.94-8.48,], p = 0.05; CA stenosis r = 0,282 [1,04-40,54], p = 0,04; LV EF <50% r = -0.344 [1,3-9,42], p = 0,009, stroke in the anamnesis r = 0,262 [1,33-9,37], p = 0,05. Conclusions The prevalence of arrhythmias in the early postoperative period (up to 7 days) following cardiac surgery is 48.2%. The predictors of arrhythmias are: operation with bypass, the size of LA> 40 mm, presence of CA stenosis and any form of AF in the history. AF dominates in the structure of heart arrhythmias (30.4%). The predictors of AF are: operation with bypass, the size of the LA> 40 mm, LV EF <40%, mitral regurgitation according to echocardiography; predictors of VPB are: CHF 4 FC, CA stenosis, LV EF <50%, stroke in the history.

COVID-19: The Awareness of Health-Related Complications of COVID-19 among College Students

Background: COVID-19 affects the health and quality of life of the entire world. Despite the toll COVID-19 places on the diverse body systems involving the health of those who suffer from this illness and the communication capabilities of the current era, there are still gaps in information related to the repercussions of this virus. Purpose: To identify the common proficiency of the complications of COVID-19 in a diverse population of college students. Methods: The present study employed a survey created in Google forms and shared online with students from Texas Woman’s University Dallas and Denton Campus. Results: The complications of COVID-19, which are thoroughly recognized and repeatedly selected, were a sense of smell, changes in taste, loss of appetite, and muscle pain. However, we identified a shortage of awareness regarding the more severe problems of the virus, such as heart failure, heart arrhythmias, liver damage, long-term musculoskeletal issues, and kidney failure. Conclusion: Despite the health-related complications of COVID-19, the current study determined a disquieting disparity in education attributed to the long-and short-term impairments of this virus. We encourage anyone exposed to COVID-19 or with the possibility of being exposed to delve into all the various health issues created by this virus, such as those alluded to in this report. Future research should focus on strategies to assemble and disseminate the complications associated with COVID-19 more effectively.

P968 Subcostal M-mode echocardiography of tricuspid annular motion helps recogntion of heart arrhythmias

Recognition of supraventricular arrhythmias and differention between supraventricular and ventricular rhythm abnormalities in ECG can sometimes be difficult due to indiscernible P wave. 1D echocardiography of tricuspid annular motion can quickly and reliably detect atrial contraction (Picture 1, figure 1a.) which has a characteristic appearance for majority of supraventricular arrhythmias. Since subcostal approach is almost always reliable, it allows simple and rapid recognition and differentiation between supraventricular arrhythmias, e.g. atrial fibrillation (Picture 1, figure 1b.) from atrial flutter and nodal rhythm. Detection of atrial contractions enables recognition of AV dissociation in ECG, which helps in differentiation between supraventricular and ventricular arrhythmias. This technique is also useful in checking the function and mechanical efficiency of atrial pacing. Figure 1a. Atrial contraction / sinus rhythm RA = right atrium A = atrial contraction Figure 1b. Atrial fibrillation RA = right atrium a = fibrillary contractions Abstract P968 Figure. Picture 1

Detection of Atrial Fibrillation Based on Optimized Electrocardiogram (Ecg) Recordings

Atrial fibrillation (AF) is one among the foremost common heart arrhythmias. It is terribly tough to discover unless a precise arrhythmia episode happens throughout the exploration. If the diagnosis and the treatment is delayed the Atrial fibrillation can lead to heart strokes and causes death, therefore automatic detection of AF is an urgent need. The analysis of ECG recordings is considered as one of the typical process of detecting AF. The ECG signals analysed by considering normal rhythm (N), other arrhythmias (O) and Atrial Fibrillation(A) and noises. In this paper the proposed technique is validated by considering open accessible public dataset. In the proposed method initially pre-processing of ECG signal is performed, next extraction of features, optimizing the features using genetic algorithm (GA) and finally classifying using support vector machine (SVM) classifier. The proposed algorithm achieves overall accuracy of 95.8% and by considering top 10 features the rate of accuracy is 96.8% which is better compared to the existing algorithm with an SNR of dB. The experimental results are performed using MATLAB and uggest that by availing the short ECG recording also the detection of AF is obtained accurately.

EKG Sensor Pad Innovation and Strategy

Innovation is the key to having a competitive edge in the market. Success comes from not just new ideas, but improvements to old ideas, and attracting new customers with new innovations. Innovations should make the product stand out to the consumer. There are challenges and barriers to innovations in the market. Innovations may be more expensive; therefore, it may be less challenging to simply edit. Wearable sensors have a strong potential for success in the market. Because this is the first sign of heart attack, this is an easy and predictable method as to identifying heart arrhythmias. The purpose of this review is to determine if wearable sensors are effective at notifying the patient and providers of cardiac problems prior to an event. Many patients are not very confident in the applications of new technologies such as wearable sensors. This is a main reason that this study was done.