Left Ventricular Mass Index Predicts Pacemaker-Induced Cardiomyopathy in Patients with Dual Chamber Permanent Pacemakers Implanted Due to Complete Atrioventricular Block.

Background: Pacing-induced cardiomyopathy (PICM) occurs as a result of high-burden right ventricular (RV) pacing, which usually develops in patients with complete atrioventricular (AV) block. There is a paucity of data on the association between PICM and pre-implantation left ventricular mass index (LVMI). Thus, the purpose of this study was to analyze the influence of LVMI on PICM in patients who had dual chamber permanent pacemakers (PPMs) implanted secondary to complete AV block. Methods: Overall, 577 patients with dual chamber permanent pacemakers (PPMs) were classified into three tertiles according to their pre- implantation LVMI. The average follow-up period was 57 ± 38 months. The baseline characteristics, laboratory and echocardiographic variables were compared between the tertiles. PICM was defined as a ≥ 10% drop in left ventricular ejection fraction (LVEF) from pre-implantation with a resultant LVEF < 50%. PICM occurred in 42 (7.2%) patients. The independent predictors of PICM development, as well as the impact of LVMI on PICM, were investigated. Results: After controlling for confounding baseline variables, the tertile with the greatest LVMI had a 1.8 times higher risk for the development of long-term PICM compared with the tertile with the lowest LVMI, which was accepted as the reference group. A receiver operating characteristic curve analysis revealed that the best LVMI cut- off value for predicting long-term PICM was 109.8 g/m2 with 71% sensitivity and 62% specificity (area under curve: 0.68; 95% confidence interval: 0.60-0.76; p < 0.001). Conclusions: This investigation revealed that pre-implantation LVMI had a prognostic role in predicting PICM in patients with an implanted dual chamber PPM due to complete AV block.

Machine Learning Approach on High Risk Treadmill Exercise Test to Predict Obstructive Coronary Artery Disease by using P, QRS, and T waves' Features.

Treadmill Exercise Test (TET) results and patients' clinical symptoms influence cardiologists' decision to perform Coronary Angiography (CAG) which is an invasive procedure. Since TET has high false positive rates, it can cause an unnecessary invasive CAG. Our primary objective was to develop a machine learning model capable of optimizing TET performance based on electrocardiography (ECG) waves characteristics and signals. TET reports from 294 patients who underwent CAG following high risk TET were collected and categorized into those with critical CAD and others. The signal was converted to time series format. A dataset containing the P, QRS, and T wave times and amplitudes was created. Using this dataset, 5 machine learning algorithms were trained with 5-fold cross validation. All these models were then compared to the performance of cardiologists on V5 signal. The results from 5 machine learning models were clearly superior to the cardiologists' V5 signal performance (P < 0.0001). In addition, the XGBoost model, with an accuracy of 80.92±6.42% and an area under the curve (AUC) of 0.78±0.06, was the most successful model. Machine learning models can produce high-performance diagnoses using the V5 signal markers only as it does not require any clinical markers obtained from TET reports. This can lead to significant contributions to improving clinical prediction in non-invasive methods.

Cardiac variables associated with atrial fibrillation occurrence and mortality in octogenarians implanted with dual chamber permanent pacemakers.

BACKGROUND: There is a dearth of data on the predictors of atrial fibrillation (AF) and the association between AF and long-term mortality in octogenarians with dual-chamber permanent pacemakers (PPM). We investigate the occurrence of AF and whether it is associated with overall mortality among octogenarians with dual-chamber PPM implants. METHODS: Three hundred and fifty-four patients with PPM implants were divided into two groups based on their long-term survival status. Baseline characteristics, laboratory variables, and echocardiographic variables were then compared between the groups, and independent predictors of the long-term incidence of AF and mortality were determined. RESULTS: Multivariable Cox regression analysis performed after adjusting for the parameters in univariable analysis revealed that diabetes, urea levels, albumin levels, paced QRS duration, and the frequency of atrial high-rate episodes (AHREs) were independently associated with a long-term risk of AF in octogenarians after having dual chamber PPMs implanted. The left ventricular (LV) ejection fraction, left atrial (LA) anteroposterior diameter, and AHRE + AF (HR 1.498, 95%CI 1.003-2.237, p = 0.048) were independent risk factors for the long-term mortality in octogenarians receiving dual-chamber PPMs implants. CONCLUSION: The occurrence of AF following dual-chamber PPM implantation is a significant prognostic factor in octogenarian patients.

Prognostic value of serum albumin for long-term mortality in patients with dual-chamber permanent pacemakers.

Background: This investigation aims to examine the prognostic utility of albumin concentrations for long-term all-cause mortality in patients undergoing permanent pacemaker implantation. Methods: A total of 1798 patients who received permanent pacemaker implantation were divided into quartiles according to serum albumin concentrations. The significance of albumin in predicting long-term mortality was compared in these quartiles. Results: There was a higher rate of long-term mortality in the Q4 group compared with the Q1-3 groups (49.9 vs 15.8%). The risk of long-term mortality in the Q4 group was 3.6-times higher compared with the Q1-3 groups after adjustment for confounders. Conclusion: Serum albumin level at the time of device implantation has great value when assessing long-term mortality in patients with permanent pacemakers.