Identification and Application of Early Prognostic Indicators of Left Bundle Branch Block Non-ischemic Cardiopathy
Approved Research ID: 95325
Approval date: January 31st 2023
The heart has conduction pathways to the left and right sides of the heart. When one of the conduction pathways becomes blocked then the left and right sides of the heart will no longer pump in synchrony and will cause the heart to become larger, known as dilated cardiomyopathy. As a result, the amount of blood flow out of the heart (cardiac output) is decreased and over time this will result in heart failure. In addition, individuals with heart failure from a blocked conduction pathway have been shown to have poor response to traditional standard of care medicines used to treat heart failure. However, studies have demonstrated a positive response when treating this subset of patient with cardiac resynchronization therapy (CRT) which is a device that stimulates both sides of the heart in synchrony to regain normal function of the heart. Early implementation of CRT has been shown to have a greater treatment effect while a delayed diagnosis could miss the critical period to reverse the damage to the heart. Therefore, early detection methods or prognostic indicators of identifying heart failure caused by conduction block is of considerable benefit to improve patient clinical outcomes.
The proposed study aims to identify early prognostic indicators of decreased cardiac function (cardiomyopathy) caused by blocked conduction pathway to the left side of the heart, known as left bundle branch block (LBBB). The project hypothesizes that increased surveillance of at-risk individuals for the development of LBBB induced cardiomyopathy will allow for earlier detection and intervention resulting in improved patient outcomes. The specific project aims are to 1) identify early prognostic indicators associated with LBBB induced cardiomyopathy, 2) incorporate prognostic indicators and cardiac imaging studies to create a model to predict the development, severity, and potential future clinical implications of LBBB induced cardiomyopathy, and 3) develop new surveillance guidelines to allow for early detection and treatment of LBBB induced cardiomyopathy.
The duration for the proposed study is 36 months. This duration will allow sufficient time to obtain necessary data, identify significant prognostic indicators, and develop a machine learning model. This comprehensive research will define new approaches to LBBB induced cardiomyopathy prevention, early detection, diagnosis, and treatment. Results from the proposed study and subsequent studies will be published in peer-reviewed journals.