A Simple Predictive Marker in Cardiac Resynchronization Therapy Recipients: Prominent S-Wave in Right Precordial Leads

Advancing Sports Cardiology: Integrating Artificial Intelligence with Wearable Devices for Cardiovascular Health Management

Sports cardiology focuses on athletes' cardiovascular health, yet sudden cardiac death remains a significant concern despite preventative measures. Prolonged physical activity leads to notable cardiovascular adaptations, known as the athlete's heart, which can resemble certain pathological conditions, complicating accurate diagnoses and potentially leading to serious consequences such as unnecessary exclusion from sports or missed treatment opportunities. Wearable devices, including smartwatches and smart glasses, have become prevalent for monitoring health metrics, offering potential clinical applications for sports cardiologists. These gadgets are capable of spotting exercise-induced arrhythmias, uncovering hidden heart problems, and offering crucial information for training and recovery, to minimize exercise-related cardiac incidents and enhance heart health care. However, concerns about data accuracy and the actionable value of the obtained information persist. A major challenge lies in the integration of artificial intelligence with wearables, research gaps remain regarding their ability to provide real-time, reliable, and clinically relevant insights. Combining artificial intelligence with wearable devices can improve how data is managed and used in sports cardiology. Artificial intelligence, particularly machine learning, can classify, predict, and draw inferences from the data collected by wearables, revolutionizing patient data usage. Despite artificial intelligence's proven effectiveness in managing chronic conditions, the limited research on its application in sports cardiology, particularly regarding wearables, creates a critical gap that needs to be addressed. This review examines commercially available wearables and their applications in sports cardiology, exploring how artificial intelligence can be integrated into wearable technology to advance the field.

Impact of S-Wave Amplitude in Right Precordial Leads on Improvement in Mitral Regurgitation following Cardiac Resynchronization Therapy

Background: The therapeutic strategy for mitral regurgitation (MR) in patients with advanced heart failure and wide QRS complex who are indicated for both intervention to MR and cardiac resynchronization therapy (CRT), remains unclear. Objective: We aimed to determine electrocardiogram parameters that associate with MR reduction following CRT implantation. Methods: Among the patients with advanced heart failure and functional MR who intended to receive CRT implantation, baseline QRS morphology, electrical axis, PR interval, QRS duration, and averaged S-wave in right precordial leads (V1 to V3) in surface electrocardiogram were measured. The impact of these parameters on MR reduction following CRT implantation, which was defined as a reduction in MR ≥1 grade six months later, was investigated. Results: In 35 patients (median 71 years old, 18 men), 17 (49%) achieved an MR reduction following CRT implantation. Among baseline characteristics, only the higher S-wave amplitude in right precordial leads was an independent predictor of MR reduction (odds ratio 14.00, 95% confidence interval 1.65–119.00, p = 0.016) with a cutoff of 1.3 mV calculated through the area under the curve. The cutoff significantly stratified the cumulative incidences of heart failure re-admission and percutaneous mitral valve repair following CRT implantation (p = 0.032 and p = 0.011, respectively). Conclusions: In patients with advanced heart failure and functional MR, the baseline higher amplitude of S-wave in the right precordial leads might be a good indicator of MR improvement following CRT.