Chronology of cardiac dysfunction after permanent pacemaker implantation: an observational 2 year prospective study in North India

Types of Complications and Associated Factors in Patients Undergoing Permanent Cardiac Pacemaker Implantation: A Systematic Review

Background

Permanent Pacemaker (PPM) implantation is essential in treating cardiac arrhythmias and conduction disorders, especially in patients with heart failure. Although PPM has been proven to improve quality of life and prolong life expectancy in patients with cardiac conduction disorders, post implantation complications still often occur.

Purpose

This study aimed to identify types of complications and associated predictors in patients undergoing PPM implantation.

Methods

This review used a systematic review design and follows the guidelines of the Cochrane Handbook for Systematic Reviews and the Preferred Reporting Item for Systematic Reviews and Meta-analysis (PRISMA). The database used was PubMed, CINAHL: Medline Ultimate, ScienceDirect, Scopus, and search engines: Google Scholar. Articles with observational designs and RCTs in English were included without limitation of publication year.

Results

This review analyzed 15 articles. The analysis showed that there are five categories of factors which influences the incidence of complications in patients after PPM implantation: demographic, pre-existing clinical, comorbid disease, procedural, operator experience, and activity factors. The complications that are most frequently reported are generator erosion, pacemaker infection, pneumothorax, atrial lead displacement, battery depletion, and even death after PPM implantation.

Conclusion

There are many incidents of complications and factors that influence complications in patients after PPM placement. The reported complications underscore the importance of careful patient selection and procedure execution to minimize risks. Healthcare providers should emphasize patients with risk factors to provide targeted monitoring and management. Integrating a multidisciplinary approach involving cardiologists, nephrologists, surgeons, and the nursing team is essential to optimizing patient care and improving clinical outcomes.

Impact of single chamber and dual chamber permanent pacemaker implantation on left ventricular function: An observational study

BACKGROUND

Permanent pacemaker implantation has the potential to impact left ventricular (LV) function and hence quality of life (QoL) in the long term.

AIM

To assess the effect of single- and dual-chamber pacing on LV function and QoL.

METHODS

This study included 56 patients who underwent permanent pacing: Dual pacing, dual sensing, dual responsive and rate responsive (DDDR) for the initial 3 months and ventricular pacing, ventricular sensing, inhibited response and rate responsive (VVIR) for the next 3 months, and DDDR mode for the last 3 months. Throughout the study period, various echocardiographic parameters, functional status, and QoL were measured to assess the impact of pacing on LV function compared with baseline and at every 3 months interval.

RESULTS

A significant change appeared in cardiac function after VVIR pacing which included diastolic properties of LV as shown by increase in isovolumic relaxation time from (85.28 ± 9.54 ms) to (89.53 ± 9.65 ms). At the 3-, 6-, and 9-month follow-up, reduction in LV ejection fraction was observed to be 62.71 ± 4.66%, 61.07 ± 4.41%, and 58.48 ± 3.89%, respectively. An increase in the QoL scores was noted at every follow-up visit.

CONCLUSION

An apparent depressant effect on LV function due to right ventricular pacing, with a higher incidence of adverse outcomes in the VVIR mode. In addition, an upsurge in QoL scores for the study population was noted, which indicates improvement in the QoL of patients post-pacing, irrespective of the mode. Generally, the DDDR mode is a highly preferable pacing mode.

Novel Radiomics Features for Automated Detection of Cardiac Abnormality in Patients with Pacemaker

Cardiac pacemakers are used in the treatment of patients with symptomatic bradycardia. The pacemaker paces the heart at the predetermined rate to maintain uninterrupted cardiac activity. Usually, pacemaker lead will be connected to the right atrium (RA) and right ventricle (RV) in dual-chamber pacemaker implantation and RV alone in single-chamber pacemaker implantation. This alters the route of proper conduction across the myocardial cells. The cell-to-cell conduction transmission in pacing delays the activation of selected intraventricular myocardial activation. Pacing-induced cardiomyopathy (PICM) is most commonly defined as a drop in left ventricle ejection fraction (LVEF) in the setting of chronic, high-burden right ventricle (RV) pacing. Currently, very few effective treatments are standard for PICM which rely on the detection of the RV pacing. Such treatments have primarily focused on upgrading to cardiac resynchronization therapy (CRT) when LVEF has dropped. However, the early and accurate detection of these stress factors is challenging. Cardiac desynchrony and interventricular desynchrony can be determined by various echocardiographic techniques, including M-mode, Doppler method, tissue Doppler method, and speckle tracking echocardiography which is subjective measures and shows a significant difference between RV and LV preejection period where the activation of LV is delayed considerably. Computer-aided diagnosis (CAD) is a noninvasive technique that can classify the ultrasound images of the heart in pacemaker-implanted patients and healthy patients with normal left ventricular systolic function and further detect the variations in pacemaker functions in its early stage using heart ultrasound images. Developing such a system requires a vast and diverse database to reach optimum performance. This paper proposes a novel CAD tool for the accurate detection of pacemaker variations using machine learning models of decision tree, SVM, random forest, and AdaBoost. The models have been used to extract radiomics features in terms of textures and then screened by their Relief-F scores for selection and ranking to be classified into nine groups consisting of up to 250 radiomics features. Ten best features were fed to the machine learning models. The R-wave dataset achieved a maximum test performance accuracy of 97.73% with four features in the random forest model. The T-wave dataset achieved a maximum test performance accuracy of 96.59% with three features in the SVM model. Our experimental results demonstrate the system's robustness, which can be developed as an early and accurate detection system for pacing-induced cardiomyopathy.