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1.
Acta Cardiol Sin ; 40(3): 267-274, 2024 May.
Article in English | MEDLINE | ID: mdl-38779161

ABSTRACT

Background: The treadmill exercise test is widely used to determine cardiovascular risk and mortality. Premature ventricular complexes (PVCs) are frequently observed during exercise stress testing. The literature on the role of PVCs observed during treadmill exercise testing in predicting prognosis is controversial. Hence, we aimed to evaluate the clinical results of PVCs seen during exercise testing in patients without obstructive coronary artery disease confirmed by coronary angiography (CAG). Methods: The study population consisted of 1624 consecutive patients who were considered high risk according to the Duke treadmill risk score and had no significant stenosis on CAG from January 2016 to April 2021. The primary endpoints of the study were long-term all-cause mortality of patients who had PVCs during the exercise test or during the resting phase. Results: Long-term mortality was observed in 53 of the 1624 patients after a mean follow-up of 47 months. PVCs were observed in 293 (18.7%) patients without long-term mortality, and in 24 (45.3%) patients with long-term mortality (p < 0.001). The model adjusted for all covariates showed that the presence of PVCs in the recovery phase [p < 0.007, hazard ratio (HR) (95% confidence interval (CI)) 2.244 (1.244-4.047)] and advanced age [p < 0.001, HR (95% CI) 1.194 (1.143-1.247)] were associated with long-term all-cause mortality. Conclusions: PVCs observed during treadmill exercise testing and the recovery phase were related to long-term mortality in patients without obstructive coronary artery disease.

2.
Acta Cardiol Sin ; 39(3): 416-423, 2023 May.
Article in English | MEDLINE | ID: mdl-37229328

ABSTRACT

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.

3.
Aging Clin Exp Res ; 34(10): 2533-2539, 2022 Oct.
Article in English | MEDLINE | ID: mdl-35834163

ABSTRACT

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.


Subject(s)
Atrial Fibrillation , Pacemaker, Artificial , Aged, 80 and over , Humans , Octogenarians , Pacemaker, Artificial/adverse effects , Heart Atria , Risk Factors
4.
Curr Probl Cardiol ; 48(2): 101482, 2023 Feb.
Article in English | MEDLINE | ID: mdl-36336117

ABSTRACT

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.


Subject(s)
Coronary Artery Disease , Humans , Coronary Artery Disease/diagnosis , Exercise Test/methods , Coronary Angiography , Electrocardiography , Machine Learning
5.
Biomark Med ; 16(5): 341-348, 2022 04.
Article in English | MEDLINE | ID: mdl-35234522

ABSTRACT

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.


Subject(s)
Pacemaker, Artificial , Serum Albumin , Humans , Pacemaker, Artificial/adverse effects , Prognosis
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