Pub Date : 2024-06-02DOI: 10.5812/intjcardiovascpract-143916
Shahin Keshtkar Rajabi, Farshad Divsalar, Mohsen Arabi, Mohammad Amin Abbasi
Background: Previous studies have shown that cardiac arrhythmias may occur in up to 44% of patients with severe COVID-19. Objectives: This study aims to evaluate the incidence of cardiac arrhythmias in patients with COVID-19 and their risk factors. Methods: In this retrospective observational study, we included 288 consecutive COVID-19 patients who were admitted to the emergency department. Patients with a history of old arrhythmia, including atrial fibrillation, flutter, and atrial tachycardia, were excluded. Electrocardiographic data were collected in the first 24 hours of hospitalization, and hematological biomarkers were measured. Results: Arrhythmia occurred in 23.6% of patients and 61.8% of ICU patients. Its prevalence was significantly higher in ICU patients compared to ward patients. Arrhythmias were categorized as atrial fibrillation (11.8%), ventricular tachycardia (4.2%), premature ventricular contraction (2.7%), and paroxysmal supraventricular tachycardia (2.4%). Gender, age, and lab tests were not associated with the incidence of arrhythmia in COVID-19 patients. Conclusions: Arrhythmia was observed in 23.6% of patients with COVID-19 and in 61.8% of ICU patients with COVID-19. No risk factor was found for cardiac arrhythmia in COVID-19 patients.
{"title":"Prevalence and Risk Factors of Cardiac Arrhythmia in COVID-19 Patients","authors":"Shahin Keshtkar Rajabi, Farshad Divsalar, Mohsen Arabi, Mohammad Amin Abbasi","doi":"10.5812/intjcardiovascpract-143916","DOIUrl":"https://doi.org/10.5812/intjcardiovascpract-143916","url":null,"abstract":"Background: Previous studies have shown that cardiac arrhythmias may occur in up to 44% of patients with severe COVID-19. Objectives: This study aims to evaluate the incidence of cardiac arrhythmias in patients with COVID-19 and their risk factors. Methods: In this retrospective observational study, we included 288 consecutive COVID-19 patients who were admitted to the emergency department. Patients with a history of old arrhythmia, including atrial fibrillation, flutter, and atrial tachycardia, were excluded. Electrocardiographic data were collected in the first 24 hours of hospitalization, and hematological biomarkers were measured. Results: Arrhythmia occurred in 23.6% of patients and 61.8% of ICU patients. Its prevalence was significantly higher in ICU patients compared to ward patients. Arrhythmias were categorized as atrial fibrillation (11.8%), ventricular tachycardia (4.2%), premature ventricular contraction (2.7%), and paroxysmal supraventricular tachycardia (2.4%). Gender, age, and lab tests were not associated with the incidence of arrhythmia in COVID-19 patients. Conclusions: Arrhythmia was observed in 23.6% of patients with COVID-19 and in 61.8% of ICU patients with COVID-19. No risk factor was found for cardiac arrhythmia in COVID-19 patients.","PeriodicalId":502770,"journal":{"name":"International Journal of Cardiovascular Practice","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141273742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.5812/intjcardiovascpract-143902
Samaneh Hoseinzadeh, Mehrdad Jafari Fesharaki, S. Samavat, Hossein Amini, Vahid Eslami, Masoumeh Hakiminejad, Asghar Rahmani, N. Dalili
Background: Cardiovascular diseases are among the leading causes of morbidity and mortality in chronic kidney disease (CKD) patients. Therefore, predicting cardiac events in CKD patients is essential. Objectives: The present study aims to evaluate the predictive value of single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) in patients with different stages of CKD. Methods: Consecutive CKD patients with an estimated glomerular filtration rate (eGFR
{"title":"Evaluating the Predictive Value of a Cardiac Perfusion Scan for Cardiac Events in Chronic Kidney Disease","authors":"Samaneh Hoseinzadeh, Mehrdad Jafari Fesharaki, S. Samavat, Hossein Amini, Vahid Eslami, Masoumeh Hakiminejad, Asghar Rahmani, N. Dalili","doi":"10.5812/intjcardiovascpract-143902","DOIUrl":"https://doi.org/10.5812/intjcardiovascpract-143902","url":null,"abstract":"Background: Cardiovascular diseases are among the leading causes of morbidity and mortality in chronic kidney disease (CKD) patients. Therefore, predicting cardiac events in CKD patients is essential. Objectives: The present study aims to evaluate the predictive value of single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) in patients with different stages of CKD. Methods: Consecutive CKD patients with an estimated glomerular filtration rate (eGFR","PeriodicalId":502770,"journal":{"name":"International Journal of Cardiovascular Practice","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141280879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Context: With the widespread availability of portable electrocardiogram (ECG) devices, there is an increasing interest in utilizing artificial intelligence (AI) methods for ECG signal analysis and arrhythmia detection. The potential benefits of AI-assisted arrhythmia prognosis, early screening, and improved accuracy in arrhythmia classification are discussed. Evidence Acquisition: Artificial intelligence methods are a new way to classify different types of arrhythmias. For example, deep learning (DL) algorithms, including long short-term memory (LSTM) networks, convolutional neural networks (CNN), CNN-based autoencoders (AE), and convolutional recurrent neural networks (CRNN), have been extensively utilized for ECG signal analysis and arrhythmia detection. Results: This study explores different DL techniques for classifying arrhythmias. The two-dimensional (2D) CNN model achieved an accuracy of 97.42% in classifying five different arrhythmias. After classifying five types of ECG signals, an accuracy of 99.53% was achieved by the CNN-based AE and transfer learning (TL) models. The CNN-Bi-LSTM model achieved an accuracy of 98.0% in categorizing five categories of ECG signals. The CNN+LSTM model achieved an accuracy of 98.24% in classifying five classes of arrhythmias. The CNN-support vector machine (SVM) classifier model demonstrated an accuracy of 98.64% in detecting seventeen classes of heartbeats. The results indicated that the CNN-based AE and TL models perform exceptionally well with high accuracy in detecting ECG signals. Conclusions: The present study demonstrates the growing interest in utilizing DL for ECG signal detection in medical and healthcare applications over the past decade. Deep learning models have been shown to outperform experienced cardiologists, delivering state-of-the-art and more accurate results.
{"title":"Advancements in Artificial Intelligence for ECG Signal Analysis and Arrhythmia Detection: A Review","authors":"Fatemeh Kazemi Lichaee, A. Salari, Jalil Jalili, Sedigheh Beikmohammad Dalivand, Mahdis Roshanfekr Rad, Mohadeseh Mojarad","doi":"10.5812/intjcardiovascpract-143437","DOIUrl":"https://doi.org/10.5812/intjcardiovascpract-143437","url":null,"abstract":"Context: With the widespread availability of portable electrocardiogram (ECG) devices, there is an increasing interest in utilizing artificial intelligence (AI) methods for ECG signal analysis and arrhythmia detection. The potential benefits of AI-assisted arrhythmia prognosis, early screening, and improved accuracy in arrhythmia classification are discussed. Evidence Acquisition: Artificial intelligence methods are a new way to classify different types of arrhythmias. For example, deep learning (DL) algorithms, including long short-term memory (LSTM) networks, convolutional neural networks (CNN), CNN-based autoencoders (AE), and convolutional recurrent neural networks (CRNN), have been extensively utilized for ECG signal analysis and arrhythmia detection. Results: This study explores different DL techniques for classifying arrhythmias. The two-dimensional (2D) CNN model achieved an accuracy of 97.42% in classifying five different arrhythmias. After classifying five types of ECG signals, an accuracy of 99.53% was achieved by the CNN-based AE and transfer learning (TL) models. The CNN-Bi-LSTM model achieved an accuracy of 98.0% in categorizing five categories of ECG signals. The CNN+LSTM model achieved an accuracy of 98.24% in classifying five classes of arrhythmias. The CNN-support vector machine (SVM) classifier model demonstrated an accuracy of 98.64% in detecting seventeen classes of heartbeats. The results indicated that the CNN-based AE and TL models perform exceptionally well with high accuracy in detecting ECG signals. Conclusions: The present study demonstrates the growing interest in utilizing DL for ECG signal detection in medical and healthcare applications over the past decade. Deep learning models have been shown to outperform experienced cardiologists, delivering state-of-the-art and more accurate results.","PeriodicalId":502770,"journal":{"name":"International Journal of Cardiovascular Practice","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140490314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-25DOI: 10.5812/intjcardiovascpract-142570
M. Namazi, I. Khaheshi, Maryam Alaei, Yasaman Tavakoli, Amir Moradi, Omid Amali, M. Safi, Saeed Alipour Parsa, Vahid Eslami, Zohre Zamiri
Background: The Global Registry of Acute Coronary Events (GRACE) is used in patients with acute coronary syndrome (ACS) to stratify the risk of mortality. The Synergy Between Percutaneous Coronary Intervention (SYNTAX) score explains the extent of coronary artery disease (CAD) and guides to an appropriate treatment strategy. Objectives: This study aimed to determine the correlation between GRACE and SYNTAX scores. Methods: A total of 101 ACS patients were recruited in this case-control study. Coronary angiography (CA) was performed for all of the participants. Correlation analysis was performed to investigate the relationship between GRACE risk and SYNTAX angiographic scores. Results: A total of 83 men and 18 women who had ACS with an average age of 57.2 ± 11.6 years (minimum of 33 and maximum of 89 years) were investigated. The SYNTAX angiographic score and the GRACE risk score for participants of this study were 15.09 ± 5.87 and 114.47 ± 26.2, respectively. A strong positive correlation, which was statistically significant, was demonstrated between the GRACE risk score and the SYNTAX angiographic score (r = 0.867, P < 0.001) Conclusions: Our findings point out a significant strong positive correlation exists between GRACE risk score and SYNTAX angiographic score in patients with unstable angina (UA), ST-elevation myocardial infarction (STEMI), or non-ST elevation myocardial infarction (NSTEMI).
{"title":"The Correlation Between GRACE Risk Score and SYNTAX Angiographic Score in Acute Coronary Syndrome: A Cross-Sectional Study","authors":"M. Namazi, I. Khaheshi, Maryam Alaei, Yasaman Tavakoli, Amir Moradi, Omid Amali, M. Safi, Saeed Alipour Parsa, Vahid Eslami, Zohre Zamiri","doi":"10.5812/intjcardiovascpract-142570","DOIUrl":"https://doi.org/10.5812/intjcardiovascpract-142570","url":null,"abstract":"Background: The Global Registry of Acute Coronary Events (GRACE) is used in patients with acute coronary syndrome (ACS) to stratify the risk of mortality. The Synergy Between Percutaneous Coronary Intervention (SYNTAX) score explains the extent of coronary artery disease (CAD) and guides to an appropriate treatment strategy. Objectives: This study aimed to determine the correlation between GRACE and SYNTAX scores. Methods: A total of 101 ACS patients were recruited in this case-control study. Coronary angiography (CA) was performed for all of the participants. Correlation analysis was performed to investigate the relationship between GRACE risk and SYNTAX angiographic scores. Results: A total of 83 men and 18 women who had ACS with an average age of 57.2 ± 11.6 years (minimum of 33 and maximum of 89 years) were investigated. The SYNTAX angiographic score and the GRACE risk score for participants of this study were 15.09 ± 5.87 and 114.47 ± 26.2, respectively. A strong positive correlation, which was statistically significant, was demonstrated between the GRACE risk score and the SYNTAX angiographic score (r = 0.867, P < 0.001) Conclusions: Our findings point out a significant strong positive correlation exists between GRACE risk score and SYNTAX angiographic score in patients with unstable angina (UA), ST-elevation myocardial infarction (STEMI), or non-ST elevation myocardial infarction (NSTEMI).","PeriodicalId":502770,"journal":{"name":"International Journal of Cardiovascular Practice","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140495786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.5812/intjcardiovascpract-142571
M. Safi, Farshid Heidarpour Kiaee, Mohammad Khani, N. Deravi, Seyedeh Zahra Banihashemian, M. Namazi, Saeed Alipour Parsa, Saeed Nourian, A. Salehi, Hossein Jafari
Background: Resolution of ST-segment (STR) and thrombolysis in myocardial infarction (TIMI) and frame count (TFC) are useful parameters to evaluate the reperfusion status following primary percutaneous coronary intervention (PPCI) in ST-elevation myocardial infarction (STEMI) patients. Objectives: Here, the association of ejection fraction (EF), as a parameter of systolic function, with TFC and STR was assessed in patients with STEMI who underwent PPCI. Methods: Ejection fraction was evaluated by transthoracic echocardiography using Simpson’s biplane method before PPCI in the first 24 hours after the admission of STEMI patients. Also, STR and TFC were assessed in all patients after PPCI. Then, the association of EF with STR and TFC was examined before and after the operation. Results: STEMI patients with STR less or greater than 50% were comparable in terms of clinical and demographic characteristics and laboratory indices. Our results showed a weak inverse correlation between EF before PPCI and TFC (r = -0.2336, P = 0.0002). However, there was a strong inverse correlation between EF after PPCI and TFC (P < 0.0001, r = -0.3137). The results of correlation analysis showed that the mean EF (pre- and post-PPCI) was significantly higher in patients with STR of ≥50% compared to those with STR < 50%. Conclusions: The results of this study showed that EF after PPCI, as an echocardiographic indicator, could reflect the status of cardiac and microvascular perfusion. We also found that cardiac status on ECG could better reflect EF.
{"title":"Association of Ejection Fraction with Reperfusion Parameters Before and After Primary Percutaneous Coronary Intervention","authors":"M. Safi, Farshid Heidarpour Kiaee, Mohammad Khani, N. Deravi, Seyedeh Zahra Banihashemian, M. Namazi, Saeed Alipour Parsa, Saeed Nourian, A. Salehi, Hossein Jafari","doi":"10.5812/intjcardiovascpract-142571","DOIUrl":"https://doi.org/10.5812/intjcardiovascpract-142571","url":null,"abstract":"Background: Resolution of ST-segment (STR) and thrombolysis in myocardial infarction (TIMI) and frame count (TFC) are useful parameters to evaluate the reperfusion status following primary percutaneous coronary intervention (PPCI) in ST-elevation myocardial infarction (STEMI) patients. Objectives: Here, the association of ejection fraction (EF), as a parameter of systolic function, with TFC and STR was assessed in patients with STEMI who underwent PPCI. Methods: Ejection fraction was evaluated by transthoracic echocardiography using Simpson’s biplane method before PPCI in the first 24 hours after the admission of STEMI patients. Also, STR and TFC were assessed in all patients after PPCI. Then, the association of EF with STR and TFC was examined before and after the operation. Results: STEMI patients with STR less or greater than 50% were comparable in terms of clinical and demographic characteristics and laboratory indices. Our results showed a weak inverse correlation between EF before PPCI and TFC (r = -0.2336, P = 0.0002). However, there was a strong inverse correlation between EF after PPCI and TFC (P < 0.0001, r = -0.3137). The results of correlation analysis showed that the mean EF (pre- and post-PPCI) was significantly higher in patients with STR of ≥50% compared to those with STR < 50%. Conclusions: The results of this study showed that EF after PPCI, as an echocardiographic indicator, could reflect the status of cardiac and microvascular perfusion. We also found that cardiac status on ECG could better reflect EF.","PeriodicalId":502770,"journal":{"name":"International Journal of Cardiovascular Practice","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139128756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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