Market for Pacing Systems Growing Alongside Heart Disease Uptick

Sanjiv Narayan, MD, is a PhD holder cardiologist and a professor of medicine at Stanford University. One of Dr. Sanjiv Narayan’s areas of expertise is atrial fibrillation, for which he advocates more effective therapies to handle the current rising trend of the condition in the United States.

Atrial fibrillation is a condition that causes an abnormal heart rate, typically one that is unusually fast and erratic. Pacemakers are a common solution, which means that the market for such devices correlates with the uptick in the diagnosis of the condition. The device can regulate the heart by sending it electrical signals that cause it to slow down and become more regular. The market for leadless pacing systems is growing due to the upwards trend of heart conditions in the US, which has been steadily increasing in individuals with lower incomes over the past few decades.

The growth of this market is also related to investment in more efficient ambulatory surgical centers, which allow patients to receive treatment without hospital admission. The rising rates of heart disease mean that more and more people will need such services daily, be it to have a new pacemaker fitted or full heart surgery.

The Purpose of Cardiac Ablation

A professor of medicine at Stanford University, cardiologist Sanjiv Narayan, MD, PhD, focuses on developing innovative bioengineering solutions for improving treatments and addressing major health issues. Dr. Sanjiv Narayan was a professor of medicine at the University of California, San Diego, where he treated patients with heart rhythm abnormalities, trained residents and fellows, and researched to improve the area of heart rhythm medicine. He also takes an interest in digital health and cardiac ablation topics.

Catheter ablation is a procedure that creates tiny scars to block electric signals in targeted sites inside the heart to prevent abnormal heart rhythm (a heart problem called arrhythmia). This treatment creates those scars by causing slight injuries to tissues via cold energy or heat. The procedure seldom requires open-heart surgery.

Catheter ablation can be done in a minimally invasive style by using a group of special medical tubes called catheters. Before the procedure, an electrocardiogram study of the heart will help health practitioners detect a disruption in electric signals within the organ that may result in an abnormal rhythm. Next, catheters will be inserted into the patient’s veins or arteries and directed to the points where the errors are coming from. Cauterizing or freezing tiny tissues in those regions hinders the abnormal electric signals, correcting the problem.

Practitioners seldom recommend cardiac ablation as a first treatment option. It is often suggested when other less invasive approaches, like medications, fail to control the arrhythmia. For some patients who are more likely to develop serious side effects from medications, cardiac ablation may be the most natural option. If a patient also has a high risk of suffering lethal or debilitating complications from arrhythmia, a rigorous treatment like cardiac ablation may be the doctor’s first recommendation.

Symptoms of Atrial Fibrillation

Sanjiv Narayan, MD, PhD, is a professor of medicine at Stanford University, where he conducts research on unique bioengineering advancements in arrhythmia medicine and trains the next generation of physician-scientists. He has more than two decades of experience as a cardiologist. Before joining Stanford University, Dr. Sanjiv Narayan formerly served as a professor of medicine at the University of California, San Diego. One of his research focuses is on the heart rhythm condition of atrial fibrillation.

Atrial fibrillation is an abnormal heart rhythm that occurs when the heart’s upper chambers beat in an unregulated manner. When this happens, the regular beat synchronization between the heart chambers is lost. Consequently, the movement of blood within the heart may be delayed, causing the blood to pool and form clots within the heart.

For some people, atrial fibrillation does not present any symptoms at all. However, symptoms like shortness of breath, chest pain, fatigue and unexplained weakness, confusion, and heart palpitations may occur. These symptoms can occur occasionally and last for a few minutes to hours or occur persistently, necessitating medical intervention to restore the heart rhythm.

For people with asymptomatic atrial fibrillation, the condition can progress unnoticed for months and result in heart failure or stroke, among other heart-related complications. Atrial fibrillation is a potentially debilitating condition, and treatment is often crucial. If you notice any of the symptoms as mentioned, endeavor to make an appointment with your physician for diagnosis and potential treatment recommendations as soon as possible.

NLP and Machine Learning in Healthcare

An experienced cardiologist with more than three decades of experience in the medical field, Sanjiv Narayan, MD, PhD, serves as a professor of medicine at Stanford University. Before this, he spent more than a decade at the University of California San Diego and the Veterans Affairs Medical Center in San Diego, where he led the cardiac heart rhythm service. Dr. Sanjiv Narayan’s scientific and clinical interests include digital health, computer methods in medicine, and AI and machine learning.

Machine learning is a subset of artificial intelligence that involves embedding computers with programs that teach the systems to interact with data to carry out sophisticated tasks without human direction or human supervision. Machine learning involves feeding an algorithm based on computational statistics with data so that the algorithm can learn from the data and improve its computational accuracy and efficiency.

There are multiple machine learning applications in the health sector – including diagnostic aid, patient medical report summarization, and personalized and automated prediction of the potential risk level of specific procedures for patients. There is, however, a bottleneck in the use of machine learning.

Traditional algorithms are trained to read and comprehend data in mathematical/statistical format. However, more than 70% of the data in electronic health records is stored in text, which humans alone can understand. This limits the ability of ML-embedded systems to work off data on their own. Researchers are embedding ML-based systems with programs that can read natural languages to address this situation. This action is styled Natural Language Processing (NLP).

NLP helps build algorithms that can comprehend and respond to data presented in natural written language or spoken words. With this feature, ML-based computer systems will no longer need humans to harness all the information from raw text or spoken data.