The evaluation of bariatric surgery effect on cardiac structure and function using transthoracic echocardiography: a cohort study.

BACKGROUND: Obesity is a pathology and a leading cause of death worldwide. Obesity can harm multiple organs, including the heart. In this study, we aim to investigate the effect of bariatric surgery and following weight loss on cardiac structure and functions using echocardiography parameters in patients with morbid obesity. METHODS: In this cohort study, 30 patients older than 18 with BMI > 40 or BMI > 35 and comorbidity between March 2020 to March 2021 were studied. The patients underwent transthoracic echocardiography before and after six months of the bariatric surgery. RESULTS: In total, 30 patients (28 women, 93.3%) with a mean age of 38.70 ± 9.19 were studied. Nine (30%) were diabetic, and 9 (30%) had hypertension. After six months of bariatric surgery, all physical measurements, including weight, Body mass index, and Body surface area, decreased significantly (p < 0.001). After bariatric surgery, all parameters regarding left ventricular morphology, including left ventricular mass, interventricular septal thickness, left ventricular posterior wall thickness, left ventricular end-systolic diameter, and left ventricular end-diastolic diameter, improved significantly (p < 0.001). Also, LVEF rose post-bariatric surgery (p < 0.001). TAPSE parameter indicating right ventricular function also improved (p < 0.001). Right ventricular diameter, left atrium volume, and mitral inflow E/e' decreased significantly (p < 0.001). CONCLUSION: Systolic and diastolic parameters refine significantly after bariatric surgery in patients with obesity. Bariatric surgery lead to significant cardiac structure and function improvement.

Differentiating electrocardiographic indications of massive and submassive pulmonary embolism: A cross-sectional study in Southern Iran from 2015 to 2020.

BACKGROUND: Although using electrocardiogram (ECG) for pulmonary embolism (PE) risk stratification has shown mixed results, it is currently used as supplementary evidence in risk stratification. This cross-sectional study aimed to assess and compare ECG findings of massive and submassive PE versus segmental PE. METHODS: This cross-sectional study included 250 hospitalized patients with a confirmed diagnosis of acute PE from 2015 to 2020 in Southern Iran. Demographic variables, clinical data, troponin levels, on-admission ECG findings, echocardiography findings, and ECG findings 24 h after receiving anticoagulants or thrombolytics were extracted. RESULTS: Patients diagnosed with submassive or massive PE exhibited significantly higher rates of right axis deviation (p = .010), abnormal ST segment (p < .0001), S1Q3T3 pattern (p < .0001), inverted T wave in leads V1-V3 (p < .0001), inverted T wave in leads V4-V6 (p < .0001), and inverted T wave in leads V1-V6 (p < .0001). In a multivariable model, inverted T wave in leads V1-V3, inverted T wave in leads V4-V6, pulse rate, and positive troponin test were the statistically independent variables for predicting submassive or massive PE. Furthermore, inverted T wave in leads V1-V3 (sensitivity: 85%, specificity: 95%, accuracy: 93%, AUC: 0.902) and troponin levels (sensitivity: 72%, specificity: 86%, accuracy: 83%, AUC: 0.792) demonstrated the best diagnostic test performance for discriminating submassive or massive PE from segmental PE. CONCLUSION: In addition to clinical rules, ECG can serve as an ancillary tool for assessing more invasive testing and earlier aggressive treatments among patients with PE, as it can provide valuable information for the diagnosis and risk stratification of submassive or massive PE.

Bariatric surgery mitigated electrocardiographic abnormalities in patients with morbid obesity.

Obesity can lead to cardiovascular dysfunctions and cause electrocardiographic disruptions. Bariatric surgery plays a significant role in weight loss. To assess its benefits, this study investigated electrocardiographic changes before and after bariatric surgery. The present article describes a retrospective cohort study with a 6-month follow-up period. Electrocardiograms were interpreted and compared before and six months after surgery. The relationships between weight loss, type of surgery, and electrocardiographic alterations were analyzed. A total of 200 patients participated in the study, with 34 (17%) men and 166 (83%) women. The mean age of the participants was 44.6 ± 8.6, and their mean body mass index was 43.8 ± 5.5 kg/m2. The mean of QTc decreased after the surgery, while the Sokolow-Lyon scores increased. The statistical analysis showed that QTc dispersion (> 40) (P < 0.001), right ventricular hypertrophy (P < 0.001), abnormal R wave progression (P < 0.001), QTc (P < 0.001) and Sokolow-Lyon criteria (P < 0.001) significantly changed postoperatively. In conclusion, bariatric surgery can reduce QTc, correct poor R wave progression, and resolve right ventricular hypertrophy (RVH) in patients with morbid obesity.

The association between GJB2 gene (producing Cx26 protein) and the ventricular storm: A case report.

BACKGROUND: A structural heart disease or functional electrical abnormalities can cause an electrical storm. CASE PRESENTATION: We present a young boy with an electrical storm who had no cardiac risk factors and a positive family history of sudden cardiac death. The stepwise diagnostic approach was ineffective in determining previously known causes as the origin of the electrical storm. However, whole-exome sequencing (with Next Generation Illumina Sequencing) revealed a mutation in the GJB2 (NM_004004:exon2:c.G71A:p.W24X) gene. CONCLUSION: A mutation in the GJB2 gene, which forms the connexin 26 protein, a crucial component of the myocytes' intercalated disc of gap junction complex between the myocytes, results in an abnormal electrical cell-by-cell conductance, and, eventually, ventricular storm. General anesthesia was used to control the storm, and intracardiac pacing was fruitful in ceasing the subsequent VT storms.

Polypharmacy and medication usage patterns in hypertensive patients: Findings from the Pars Cohort Study.

Polypharmacy, defined here as the concomitant use of five or more medications, is a significant health issue, particularly affecting individuals with chronic diseases like hypertension (HTN). To compare individuals with and without HTN in term of polypharmacy, and to investigate correlates of polypharmacy and medication use patterns in individuals with HTN in southwest Iran. This cross-sectional study used the baseline data of 9270 participants of the Pars Cohort Study (PCS) with a mean age of 52.6 ± 9.7 years. Poisson multivariable modeling was applied to identify correlates of polypharmacy, and Lexicomp® was used to assess drug-drug interactions. Anatomical Therapeutic Chemical classification was used to describe the pattern of medication use. The prevalence of polypharmacy in individuals without hypertension was 4.7 % (4.2%-5.2 %) vs. 23.7 % (22.1%-25.3 %) in individuals with hypertension (P < 0.001). Individuals with hypertension from middle-high socioeconomic status (SES) had a 1.51-fold higher prevalence of polypharmacy than vs. low SES. Those with more than three comorbidities had a 5.18 times higher prevalence of polypharmacy than those with isolated hypertension. Calcium channel blockers were the most common antihypertensives (20.9 %). In terms of drug-drug interactions, type C interactions were most prevalent among participants with hypertension and polypharmacy (76.0 %). Our findings imply a fairly high prevalence of polypharmacy and drug-drug interactions among individuals with hypertension; to tackle this issue, we recommend a national pharmacovigilance system, training programs for primary care physicians, public education and awareness campaigns, drug-checking campaigns, targeted screenings to alter modifiable risk factors, and the use of safe combination pills.

A cohort study on the predictive capability of body composition for diabetes mellitus using machine learning.

Purpose: We applied machine learning to study associations between regional body fat distribution and diabetes mellitus in a population of community adults in order to investigate the predictive capability. We retrospectively analyzed a subset of data from the published Fasa cohort study using individual standard classifiers as well as ensemble learning algorithms. Methods: We measured segmental body composition using the Tanita Analyzer BC-418 MA (Tanita Corp, Japan). The following features were input to our machine learning model: fat-free mass, fat percentage, basal metabolic rate, total body water, right arm fat-free mass, right leg fat-free mass, trunk fat-free mass, trunk fat percentage, sex, age, right leg fat percentage, and right arm fat percentage. We performed classification into diabetes vs. no diabetes classes using linear support vector machine, decision tree, stochastic gradient descent, logistic regression, Gaussian naïve Bayes, k-nearest neighbors (k = 3 and k = 4), and multi-layer perceptron, as well as ensemble learning using random forest, gradient boosting, adaptive boosting, XGBoost, and ensemble voting classifiers with Top3 and Top4 algorithms. 4661 subjects (mean age 47.64 ± 9.37 years, range 35 to 70 years; 2155 male, 2506 female) were analyzed and stratified into 571 and 4090 subjects with and without a self-declared history of diabetes, respectively. Results: Age, fat mass, and fat percentages in the legs, arms, and trunk were positively associated with diabetes; fat-free mass in the legs, arms, and trunk, were negatively associated. Using XGBoost, our model attained the best excellent accuracy, precision, recall, and F1-score of 89.96%, 90.20%, 89.65%, and 89.91%, respectively. Conclusions: Our machine learning model showed that regional body fat compositions were predictive of diabetes status.

Risk stratification of diabetic patients with unusual cardiac symptoms using a myocardial perfusion scan.

BACKGROUND: Autonomic nervous system dysfunction in diabetic patients can result in an atypical presentation of cardiovascular disease that can be missed. We aimed to use single-photon emission computed tomography (SPECT) to assess cardiovascular disease (CAD) in diabetic patients with atypical pain to determine whether the pain above reflects the CAD. METHOD: Diabetic patients with atypical cardiac symptoms were referred to the SPECT department. Demographic data such as age, gender, diabetes status, and other underlying diseases were gathered. A myocardial perfusion scan was then performed. The results were recorded to evaluate the risk of myocardial ischemia and the degree of coronary artery involvement in a non-invasive manner. RESULTS: The study included 222 (177 female) subjects with mean ages of 63.01±11.62 and 59.41±9.19 in positive and negative SPECT, respectively. The most common symptoms were atypical chest pain (51.8%), followed by shortness of breath (50.5%), nausea, and syncope (0.9%). Cardiac parameters, such as the summed stress score (SSS), summed rest score (SRS), total perfusion deficit in stress (TPD-s), total perfusion deficit in rest (TPD-r), were significantly higher in the group with coronary artery involvement (P<0.001). However, ejection fraction (EF), end-diastolic volume (EDV), and end-systolic volumes (ESV) parameters were not (P=.0.328, 0.351, and 0.443, respectively). CONCLUSIONS: The mere presence of diabetes does not necessitate any additional diagnostic tests beyond those required for the general population, and it is possible to follow a diagnostic course similar to that of the general population.