Tehran University Medical Journal

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Background: Atrial and ventricular arrhythmias are among the most common complications after coronary artery bypass graft (CABG) surgery. Previous studies demonstrated that cardiopulmonary bypass itself results in reduced serum magnesium levels. In this study, we evaluated the effect of total blood magnesium level (TMG) on the prevention of perioperative arrhythmias with routine regimens of 2-4 grams supplemental magnesium (SMG). Methods: TMG was measured in patients who were scheduled for CABG on three occasions: just before anesthesia, just after entering the intensive care unit (ICU) after completion of the sugery, and on the first morning after the operation. Patients were evaluated for primary cardiac rhythm and other variables that could have an influence on the magnesium level, including serum creatinine, urine output in the operating room and diuretic therapy. The SMG dosage was also recorded in the operating room and ICU. Patients were then evaluated for the rate and type of arrhythmia for the next three days. Results: The mean TMG levels in 174 cases were 2.2 (0.5), 2.6 (0.6) and 2.4 (0.6) mg/dl for the three occasions, respectively. The mean SMG was 2.5 (1.2) grams. Of 164 patients, 51 (31%) developed the following post-operative arrhythmias: AF (7.3%), non-AF SVA (15.2%) and ventricular (16.5%). The mean serum creatinine level and urine output were 1.2 mg/dl and 1800 ml, respectively. Although there was a significant difference between the TMG levels on the three different occasions (P<0.001), all values were within normal range. When we stratified the TMG levels of the patients based on administered SMG, the Mentel-Haenszel test revealed no significant difference between the first and third TMG (P=0.6). Although the TMG levels were higher in arrhythmic patients compared to those without arrhythmia (2.25 vs. 2.14 mg/dl), both values were within the normal range and there was no significant difference between the two groups. Serum creatinine levels and urine volume were not related to TMG levels. Conclusion: This study indicates that routine magnesium administration has no significant effect on SMG levels. Also, serum creatinine and urine output are not determinant factors for SMG administration. There was no correlation between TMG levels and perioperative arrhythmia. We conclude that the routine regimen of magnesium administration has no effect on the incidence of perioperative arrhythmia, though it is necessary for maintaining normal magnesium levels.

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Background: It is generally accepted that the selective adenosine triphosphate-dependent potassium channel openers (KATP openers) have a dramatic role in the treatment of some cardiovascular disorders. The aim of this study was to investigate the effects of diazoxide, a potent ATP-related potassium channel opener, on spontaneously beating isolated rat atria to achieve more accurate approaches to treat cardiovascular diseases, such as atrial related disorders including atrial arrhythmias.
Methods: After induction of anesthesia, we exsected the heart and isolated the atria of 48 male Wistar rats. Later, we recorded the beating and contractile force of the atria by a physiograph. Subsequently, we studied the effects of diazoxide (2 to 100 µg/mL) on beating and contractile force of the isolated atria 5, 10, 15 and 20 minutes after applying the drug onto the atria.
Results: Diazoxide administration (2 to 100 µg/mL) showed a significant decrease (7% to 49% depending on concentration) in atrial beatings (P≤0.001) and in contractile force (1.5% to 67% depending on concentration), (P≤0.001). The effects began several minutes after applying the drug onto the tissues.
Conclusion: This study revealed that diazoxide has a direct concentration-dependent effect on cardiac performance and leads to reduction in beating rates and contractile force of the heart. This effect seems to be related to the activation of mitochondrial or sarcolemmal KATP channels. Since the inhibitory action of diazoxide on the heart was very remarkable and prompt, this agent may also exhibit antiarrhythmic properties.

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Background: Systemic Sclerosis (SSc) is an autoimmune disease with multi-organ involvement mostly due to fibrosis and ectopic or excessive collagen fibers production in organs. Myocardial fibrosis is the main finding of cardiac involvement in patients with systemic sclerosis. In recent studies, the presence of Fragmented QRS complexes (FQRS) has been shown in the surface electrocardiogram in relation to fibrosis.
Methods: The present study is a case-control study during March 2019 to February 2020 that was conducted in 148 patients with scleroderma referred to the Rheumatology clinic in Shariati Hospital and 101 non-ischemic individuals in the control group matched by age and sex with the patient group. All the medical records were reviewed and those who were low risk according to 10-year atherosclerotic cardiovascular disease (ASCVD) risk assessment were selected as case groups. Data of ECG were evaluated for availability of FQRS or conductive abnormalities and calculating PR, QRS, QT, QTc and Tp-e intervals.

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Background: An Electrocardiogram is a non-invasive method for receiving heart signals. Despite advances in imaging methods, the electrocardiogram still plays an important role remains a vital tool in the diagnosis of heart diseases. Analysis of electrocardiogram signals plays an important role in the early detection of heart diseases such as arrhythmias and heart attacks. Today, with the advancement of science and technology, computer methods have received more and more attention from doctors. In this study, machine learning methods were used to classify normal and abnormal heartbeats.
Methods: The data under study were extracted from a dataset called Heartbeat published on the Kaggle website. This dataset includes samples of audio ECG signals that are divided into healthy and unhealthy categories. First, the data were preprocessed and normalized to prepare them for input into the model. Then, temporal and frequency features were extracted from the signals. Next, a hybrid model consisting of one-dimensional convolutional layers was designed and trained. Also, by using the early stopping method, overfitting was prevented and the stability of the model was improved.
Results: In this study, it was shown that by using deep learning, especially using CNN and 1D Conv, an accuracy of 0.99% and a loss of 0.0350 for test data in detecting normal and abnormal heartbeats can be achieved. This model has the ability to analyze complex structures and temporal dynamics of ECG signals and is able to detect patterns related to cardiac disorders.
Conclusion: Today, the electrocardiogram has received more attention than ever before. Appropriate selection of the model, data standardization, and a qualitative range of data are among the factors of high accuracy in this study. This study can be an effective step in the development of intelligent systems for diagnosing cardiac disorders and can be used in medical applications, especially in the field of continuous patient monitoring.