Paroxysmal Supraventricular Tachycardia
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Cureus ◽  
2021 ◽  
Farrukh Ahmad ◽  
Majdi Abu Sneineh ◽  
Ravi S Patel ◽  
Sai Rohit Reddy ◽  
Adiona Llukmani ◽  

Marco Bergonti ◽  
Antonio Dello Russo ◽  
Rita Sicuso ◽  
Valentina Ribatti ◽  
Paolo Compagnucci ◽  

2021 ◽  
pp. 1-7
Muluneh A. Yimer ◽  
Svjetlana Tisma-Dupanovic ◽  
Lindsey Malloy-Walton ◽  
Diana Connelly ◽  
Janelle Noel-Macdonnell ◽  

Abstract Background: Arrhythmias are common in the post-operative course of patients with hypoplastic left heart syndrome. We sought to determine the types, incidence, risk factors, and impact of arrhythmias in patients with HLHS and anatomic variants. Methods: We performed a retrospective chart review of 120 consecutive patients with HLHS and anatomical variants, who had single-ventricle palliation at our institution from January, 2006 to December, 2016. Results: A total of thirty-one patients (26%) had 37 episodes of arrhythmias over a median follow-up period of 3.5 years. Of the 37 episodes, 12 (32.4%) were ectopic atrial tachycardia, 9 (24.3%) were paroxysmal supraventricular tachycardia, 4 (10.8%) were junctional ectopic tachycardia, 5 (13.6%) were sinus node dysfunction, 3 (8.1%) were heart block, 2 (5.4%) were atrial flutter, and 2 (5.4%) were ventricular tachycardia. Twenty-four (65%) of the arrhythmias occurred at post-stage 1 surgery. Most (64.8%) of the arrhythmias were resolved. Arrhythmias that occurred at post-stage 1 surgery were more likely to resolve compared to post-stages 2 or 3 (p = 0.006). No anatomical, surgical, or clinical variables were associated with arrhythmia except for age (OR per unit decrease in age at stage 1 palliation: 1.12 (95% CI 1.003, 1.250); p = 0.0439). Arrhythmias were not associated with length of hospital stay or mortality. Conclusion: Arrhythmias are common in patients with HLHS and anatomic variants, with EAT and PSVT being the most common types. Arrhythmias were associated with younger age at surgery, but did not affect mortality or length of hospital stay.

Yong-Yeon Jo ◽  
Joon-myoung Kwon ◽  
Ki-Hyun Jeon ◽  
Yong-Hyeon Cho ◽  
Jae-Hyun Shin ◽  

Abstract Aims Paroxysmal supraventricular tachycardia (PSVT) is not detected owing to its paroxysmal nature, but it is associated with the risk of cardiovascular disease and worsens the patient quality of life. A deep learning model (DLM) was developed and validated to identify patients with PSVT during normal sinus rhythm in this multicenter retrospective study. Methods and Results This study included 12,955 patients with normal sinus rhythm, confirmed by a cardiologist. A DLM was developed using 31,147 ECGs of 9,069 patients from one hospital. We conducted an accuracy test with 13,753 ECGs of 3,886 patients from another hospital. The DLM was developed based on residual neural network. Digitally stored ECG were used as predictor variables and the outcome of the study was ability of the DLM to identify patients with PSVT using an ECG during sinus rhythm. We employed a sensitivity map method to identify an ECG region that had a significant effect on developing PSVT. During accuracy test, the area under the receiver operating characteristic curve of an DLM using a 12-lead ECG for identifying PSVT patients during sinus rhythm was 0.966 (0.948–0.984). The, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of DLM were 0.970, 0.868, 0.972, 0.255, and 0.998, respectively. The DLM showed delta wave and QT interval were important to identify the PSVT. Conclusion The proposed DLM demonstrated a high performance in identifying PSVT during normal sinus rhythm. Thus, it can be used as a rapid, inexpensive, point-of-care means of identifying PSVT in patients.

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