The over 400,000 cardiac surgeries performed in the United States each year hold a risk for the postoperative complication of arrhythmias. Currently, bedside monitoring of surface electrocardiogram leads is used to interpret arrhythmias despite the evidence that atrial electrograms (AEGs) offer superior rhythm discrimination. This hesitancy to use the AEG may be due to a lack of training for practitioners in interpreting AEGs; therefore, our goal was to create an algorithm for the diagnosis of tachyarrhythmia using an AEG that can be utilized by any health care practitioner. Our algorithm classifies the most prevalent type of tachyarrhythmias following cardiac surgery. To allow rhythm identification, we categorized them based on their atrial to ventricular signal ratio, which is uniquely apparent on AEGs. Other considerations were given to rhythm regularity, consistency, P-wave axis, and rate. The algorithm includes the most common postoperative arrhythmias differentiated based on a unique branch-point approach, which walks through the steps in arrhythmia discrimination. Both rendered and collected AEGs are included as references for further understanding and interpretation of tachyarrhythmias. The utility of AEGs for rhythm discrimination post-cardiac surgery is established and recent technology can provide real-time and continuous monitoring; however, practitioner training may be inadequate. To bridge this divide, we created an algorithm so that existing atrial wires can be better used for an enhanced rhythm interpretation via AEGs.
Background: Asthma and atrial fibrillation (AF) share an underlying inflammatory pathophysiology. We hypothesized that persistent asthmatics are at higher risk for developing AF and that this association would be attenuated by adjustment for baseline markers of systemic inflammation. Methods: The MESA (Multi-Ethnic Study of Atherosclerosis) is a prospective longitudinal study of adults free of cardiovascular disease at baseline. Presence of asthma was determined at exam 1. Persistent asthma was defined as asthma requiring use of controller medications. Intermittent asthma was defined as asthma without use of controller medications. Participants were followed for a median of 12.9 (interquartile range, 10–13.6) years for incident AF. Multivariable Cox regression models were used to assess associations of asthma subtype and AF. Results: The 6615 participants were a mean (SD) 62.0 (10.2) years old (47% male, 27% black, 12% Chinese, and 22% Hispanic). AF incidence rates were 0.11 (95% CI, 0.01–0.12) events/10 person-years for nonasthmatics, 0.11 (95% CI, 0.08–0.14) events/10 person-years for intermittent asthmatics, and 0.19 (95% CI, 0.120.49) events/10 person-years for persistent asthmatics (log-rank P =0.008). In risk-factor adjusted models, persistent asthmatics had a greater risk of incident AF (hazard ratio, 1.49 [95% CI, 1.03–2.14], P =0.03). IL (Interleukin)-6 (hazard ratio, 1.26 [95% CI, 1.13–1.42]), TNF (tumor necrosis factor)-α receptor 1 (hazard ratio, 1.09 [95% CI, 1.08–1.11]) and D-dimer (hazard ratio, 1.10 [95% CI, 1.02–1.20]) predicted incident AF, but the relationship between asthma and incident AF was not attenuated by adjustment for any inflammation marker (IL-6, CRP [C-reactive protein], TNF-α R1, D-dimer, and fibrinogen). Conclusions: In a large multiethnic cohort with nearly 13 years follow-up, persistent asthma was associated with increased risk for incident AF. This association was not attenuated by adjustment for baseline inflammatory biomarkers.
Backgrounds The purpose of the study was to assess the clinical outcome of patients with situational syncope (SS) compared to patients with vasovagal syncope (VVS). Methods We assessed the prevalence, patients’ characteristics, and outcome of consecutive patients with SS and VVS who presented to the Faint and Fall Clinic (University of Wisconsin) between January 2013 and December 2015. Results SS was found in 55/1,401 (4.0%) syncope patients with follow‐up data available in 47 patients: defecation (n = 16), micturition (n = 15), cough (n = 10), swallow (n = 3), laughter (n = 1), sneeze (n = 1), and cough plus laughter (n = 1). Over the same time period, 252/1,401 patients (18%) were diagnosed with VVS with follow‐up data available in 171 patients. Compared with VVS patients, SS patients were older, more likely to be male, had a higher prevalence of hypertension, had an absence of prodromes, and experienced more injuries at the time of syncope (P = 0.01 for all). During a mean follow‐up duration of 15.4 ± 9.1 months, syncope recurred in 5/47 (10.6%) patients with SS and 16/171 (9.4%) patients with VVS. The recurrence rates at 1 year and 2 years were 20% (95% SE ± 13) and 40% (95% SE ± 20) for the SS group, and 23% (95% SE ± 13) and 43% (95% SE ± 20) for the VVS group (P = 0.6). No patient died. Conclusions We have shown in a large cohort of consecutive patients with syncope that SS is a relatively infrequent form of reflex syncope with different clinical characteristics but similar recurrence rate to VVS.
As Large Language Models (LLMs) are integrated into electronic health record (EHR) workflows, validated instruments are essential to evaluate their performance before implementation. Existing instruments for provider documentation quality are often unsuitable for the complexities of LLM-generated text and lack validation on real-world data. The Provider Documentation Summarization Quality Instrument (PDSQI-9) was developed to evaluate LLM-generated clinical summaries. Multi-document summaries were generated from real-world EHR data across multiple specialties using several LLMs (GPT-4o, Mixtral 8x7b, and Llama 3-8b). Validation included Pearson correlation for substantive validity, factor analysis and Cronbach's alpha for structural validity, inter-rater reliability (ICC and Krippendorff's alpha) for generalizability, a semi-Delphi process for content validity, and comparisons of high- versus low-quality summaries for discriminant validity. Seven physician raters evaluated 779 summaries and answered 8,329 questions, achieving over 80% power for inter-rater reliability. The PDSQI-9 demonstrated strong internal consistency (Cronbach's alpha = 0.879; 95% CI: 0.867-0.891) and high inter-rater reliability (ICC = 0.867; 95% CI: 0.867-0.868), supporting structural validity and generalizability. Factor analysis identified a 4-factor model explaining 58% of the variance, representing organization, clarity, accuracy, and utility. Substantive validity was supported by correlations between note length and scores for Succinct (rho = -0.200, p = 0.029) and Organized (rho = -0.190, p = 0.037). Discriminant validity distinguished high- from low-quality summaries (p < 0.001). The PDSQI-9 demonstrates robust construct validity, supporting its use in clinical practice to evaluate LLM-generated summaries and facilitate safer integration of LLMs into healthcare workflows.
Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.
Shared decision making (SDM) has been advocated to improve patient care, patient decision acceptance, patient-provider communication, patient motivation, adherence, and patient reported outcomes. Documentation of SDM is endorsed in several society guidelines and is a condition of reimbursement for selected cardiovascular and cardiac arrhythmia procedures. However, many clinicians argue that SDM already occurs with clinical encounter discussions or the process of obtaining informed consent and note the additional imposed workload of using and documenting decision aids without validated tools or evidence that they improve clinical outcomes. In reality, SDM is a process and can be done without decision tools, although the process may be variable. Also, SDM advocates counter that the low-risk process of SDM need not be held to the high bar of demonstrating clinical benefit and that increasing the quality of decision making should be sufficient. Our review leverages a multidisciplinary group of experts in cardiology, cardiac electrophysiology, epidemiology, and SDM, as well as a patient advocate. Our goal is to examine and assess SDM methodology, tools, and available evidence on outcomes in patients with heart rhythm disorders to help determine the value of SDM, assess its possible impact on electrophysiological procedures and cardiac arrhythmia management, better inform regulatory requirements, and identify gaps in knowledge and future needs.
Background Ventricular tachycardia (VT) ablation significantly improves our ability to control VT, yet little is known about whether disparities exist in delivery of this technology. Methods and Results Using a national 100% Medicare inpatient data set of beneficiaries admitted with VT from January 1, 2014, through November 30, 2014, multivariable logistic regression techniques were used to examine the sociodemographic and clinical characteristics associated with receiving ablation. Census block group-level neighborhood socioeconomic disadvantage was measured for each patient by the Area Deprivation Index, a composite measure of socioeconomic disadvantage consisting of education, income, housing, and employment factors. Among 131 645 patients admitted with VT, 2190 (1.66%) received ablation. After adjustment for comorbidities, hospital characteristics, and sociodemographics, female sex (odds ratio [OR], 0.75 [95% CI, 0.67-0.84]), identifying as Black race (OR, 0.75 [95% CI, 0.62-0.90] compared with identifying as White race), and living in a highly socioeconomically disadvantaged neighborhood (national Area Deprivation Index percentile of >85%) (OR, 0.81 [95% CI, 0.69-0.95] versus Area Deprivation Index ≤85%) were associated with significantly lower odds of receiving ablation. Conclusions Female patients, patients identifying as Black race, and patients living in the most disadvantaged neighborhoods are 19% to 25% less likely to receive ablation during hospitalization with VT. The cause of and solutions for these disparities require further investigation.
