Publications by authors named "Tina Baykaner"

Background: Current recommendations for a prophylactic (primary prevention) implantable cardioverter defibrillator (ICD) in patients with both ischemic and non-ischemic heart failure with reduced ejection fraction (HFrEF) originate from clinical trials conducted in selected patients over 20 years ago that showed an overall statistically significant survival benefit associated with a primary prevention ICD in the range of 23%-34%. The recent introduction of angiotensin receptor-neprilysin inhibitors [ARNI] and sodium glucose co-transporter 2 inhibitors [SGLT2i]) was shown to further reduce the risk of sudden cardiac death (SCD) in patients with HFrEF. Thus, there is an unmet need appropriately designed comparative effectiveness clinical trials aimed to reassess the survival benefit of a primary prevention ICD in contemporary patients with HFrEF.

View Article and Find Full Text PDF

Background: Implantable cardioverter-defibrillator (ICD) battery longevity impacts the need for generator replacement, with the accompanying risk of complications and cost.

Objective: We sought to identify factors associated with ICD battery longevity and compare manufacturers.

Methods: We used a nationwide, multicenter remote monitoring dataset (PaceMate) to evaluate ICDs implanted between 2003 and 2023, assessing time from implant to replacement interval (RI).

View Article and Find Full Text PDF

Background: The incidence of stroke is increasing in young to middle-aged adults. Assessing risk factors is important in this large population whose comorbidities may differ from older adults.

Methods: In this retrospective cohort analysis of adults aged between 20 and 50 presenting to the Stanford Healthcare system from 1 January 2000 through 31 December 2021, with no prior history of stroke or transient ischemic attack, we studied the effects of 30 risk factors on the primary endpoint of incident ischemic stroke, defined by the presence of the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes for stroke and confirmed by brain imaging.

View Article and Find Full Text PDF

There is conflicting literature on sex differences and clinical outcomes in patients who develop atrial fibrillation (AF) post-cardiac surgery. Our aim was to compare clinical outcomes between females and males with post-cardiac surgery AF. A systematic search was conducted for studies published until 27 September 2024 in MEDLINE, Embase, and CENTRAL.

View Article and Find Full Text PDF

Background: It is difficult to identify patients with atrial fibrillation (AF) most likely to respond to ablation. While any arrhythmia patient may recur after acutely successful ablation, AF is unusual in that patients may have long-term arrhythmia freedom despite a lack of acute success. We hypothesized that acute and chronic AF ablation outcomes may reflect distinct physiology and used machine learning of multimodal data to identify their phenotypes.

View Article and Find Full Text PDF
Article Synopsis
  • Large language models (LLMs) like ChatGPT struggle with private data interpretation, specifically electronic health records (EHRs), but prompt engineering could improve their accuracy.
  • Through systematic testing of prompt techniques on 490 EHR notes, the study found that structured prompts significantly enhanced LLM accuracy from 64.3% to 91.4%, outperforming traditional natural language processing methods.
  • The results indicate that LLMs, with proper prompt strategies, can effectively identify clinical insights from EHRs without requiring expert knowledge, suggesting potential applications in other fields for automated data analysis.
View Article and Find Full Text PDF

In the last three decades, ablation of atrial fibrillation (AF) has become an evidence-based safe and efficacious treatment for managing the most common cardiac arrhythmia. In 2007, the first joint expert consensus document was issued, guiding healthcare professionals involved in catheter or surgical AF ablation. Mounting research evidence and technological advances have resulted in a rapidly changing landscape in the field of catheter and surgical AF ablation, thus stressing the need for regularly updated versions of this partnership which were issued in 2012 and 2017.

View Article and Find Full Text PDF

Background: Despite many atrial fibrillation (AF) patients being at risk of bleeding, very limited data are available on bleeding rates of different direct oral anticoagulants based on the spectrum of bleeding risk.

Objective: We aimed to compare the risk of major bleeding and thromboembolic events with apixaban vs rivaroxaban for AF patients stratified by bleeding risk.

Methods: We conducted a population-based, retrospective cohort study of all adult patients (66 years or older) with AF in Ontario, Canada, who were treated with apixaban or rivaroxaban between April 1, 2011, and March 31, 2020.

View Article and Find Full Text PDF
Article Synopsis
  • Segmenting CT is essential for clinical practices like personalized cardiac ablation, but traditional machine learning methods often require large labeled datasets which are difficult to gather.
  • The article introduces the DOKEN algorithm, which uses domain knowledge to automatically label a small training set, enabling high-performance ML segmentation without the need for extensive data.
  • In tests, the DOKEN-enhanced nnU-Net model showed impressive segmentation results, achieving a high Dice score of 96.7% and demonstrating performance comparable to expert manual segmentation, thus validating its efficacy in real-world applications.
View Article and Find Full Text PDF
Article Synopsis
  • Ablation of atrial fibrillation (AF) has become a widely accepted and effective treatment for managing this common heart rhythm disorder over the last 30 years.
  • Since the initial consensus document in 2007, new research and technologies have significantly changed AF ablation practices, necessitating updates in 2012 and 2017.
  • A new consensus document was recently created by various cardiac societies to provide a current framework for selecting and managing patients for catheter or surgical AF ablation, reflecting the evolving nature of the field.
View Article and Find Full Text PDF
Article Synopsis
  • * The first expert guidelines for AF ablation were published in 2007, and updates were necessary in 2012 and 2017 due to advancements in research and technology.
  • * A new consensus document is now being released to provide updated guidelines for healthcare professionals on selecting and managing patients for AF ablation, created by various international cardiac societies.
View Article and Find Full Text PDF
Article Synopsis
  • Atrial fibrillation (AF) ablation has become a well-established treatment method in the last 30 years, supported by evidence showing it is safe and effective.
  • In response to advancements in research and technology, new guidelines have been released over the years, the latest being necessary to provide updated recommendations for patient care.
  • This revised consensus involves collaboration among major cardiac electrophysiology societies from Europe, Asia-Pacific, and Latin America to ensure comprehensive guidelines for AF treatment.
View Article and Find Full Text PDF
Article Synopsis
  • * It focuses on the CNA-FWRD Registry, a multicenter prospective study that compares outcomes between patients receiving standard therapy and those undergoing cardioneuroablation over a follow-up period of three years.
  • * The findings aim to provide valuable data on long-term effects, recurrence of symptoms, and overall safety of cardioneuroablation, addressing a gap in existing research which is largely based on retrospective studies.
View Article and Find Full Text PDF
Article Synopsis
  • Atrial fibrillation is a common heart condition that increases the risk of stroke, and its electrical activity often shows disorganized patterns instead of consistent rhythms, with spiral wave activity being notable in simulations but less so in clinical recordings.
  • Researchers conducted computer simulations and recorded data from patients to examine how spiral waves behave during atrial fibrillation, focusing on their persistence and phase changes.
  • The results showed that while computer simulations frequently resulted in out-of-phase spiral waves, most patients exhibited spiral waves that returned in sync, suggesting these patterns reflect ongoing, stabilized activity rather than newly generated waves.
View Article and Find Full Text PDF
Article Synopsis
  • Segmentation of cardiac CT is crucial for procedures like cardiac ablation, but traditional machine learning methods require extensive labeled data, which is hard to gather.
  • The researchers developed a "virtual dissection" model that uses simple geometric shapes to represent atrial anatomy, enabling effective segmentation with minimal training data in a sample of just 6 digital hearts.
  • Their results showed high accuracy in segmenting atrial structures across multiple datasets, significantly reducing segmentation time in live patients while maintaining accuracy comparable to expert assessments.
View Article and Find Full Text PDF
Article Synopsis
  • Tyrosine kinase inhibitors (TKIs), especially ibrutinib, are commonly used to treat blood cancers, but there are concerns about arrhythmias associated with these medications.
  • A study analyzed 193 patients on long-term cardiac monitoring, revealing that those on ibrutinib had a significantly higher occurrence of atrial fibrillation and non-sustained ventricular tachycardia compared to those on other TKIs or non-TKI treatments.
  • The findings suggest that 25% of ibrutinib patients had to pause their treatment due to arrhythmias, highlighting the need for careful heart monitoring in patients receiving this type of therapy.
View Article and Find Full Text PDF
Article Synopsis
  • Structural changes in the left atrium modestly predict outcomes for patients undergoing catheter ablation for atrial fibrillation (AF), and machine learning (ML) can enhance predictive models using CT scans and patient data.
  • A study analyzed 321 patients who had pre-ablation CT scans, combining morphological features and clinical data to train ML models to classify responders to AF ablation.
  • Results showed that the ML model that integrated various data types significantly outperformed those relying on single data sources, indicating potential for personalized patient management strategies in AF treatment.
View Article and Find Full Text PDF