Development and validation of the Atri-Risk Conduction Index risk score to predict risk of atrial fibrillation after typical atrial flutter ablation.

BACKGROUND: Identification of patients at risk for atrial fibrillation (AF) after typical atrial flutter (tAFL) ablation is important to guide monitoring and treatment. OBJECTIVE: The purpose of this study was to create and validate a risk score to predict AF after tAFL ablation METHODS: We identified patients who underwent tAFL ablation with no AF history between 2017 and 2022 and randomly allocated to derivation and validation cohorts. We collected clinical variables and measured conduction parameters in sinus rhythm on an electrophysiology recording system (CardioLab, GE Healthcare). Univariate and multivariate logistic regressions (LogR) were used to evaluate association with AF development. RESULTS: A total of 242 consecutive patients (81% male; mean age 66 ± 11 years) were divided into derivation (n =142) and validation (n = 100) cohorts. Forty-two percent developed AF over median follow-up of 330 days. In multivariate LogR (derivation cohort), proximal to distal coronary sinus time (pCS-dCS) ≥70 ms (odds ratio [OR] 16.7; 95% confidence interval [CI] 5.6-49), pCS time ≥36 ms (OR 4.5; 95% CI 1.5-13), and CHADS2-VASc score ≥3 (OR 4.3; 95% CI 1.6-11.8) were independently associated with new AF during follow-up. The Atri-Risk Conduction Index (ARCI) score was created with 0 as minimal and 4 as high-risk using pCS-dCS ≥70 ms = 2 points; pCS ≥36 ms = 1 point; and CHADS2-VASc score ≥3 = 1 point. In the validation cohort, 0% of patients with ARCI score = 0 developed AF, whereas 89% of patients with ARCI score = 4 developed AF. CONCLUSION: We developed and validated a risk score using atrial conduction parameters and clinical risk factors to predict AF after tAFL ablation. It stratifies low-, moderate-, and high-risk patients and may be helpful in individualizing approaches to AF monitoring and anticoagulation.

A Discussion of the Contemporary Prediction Models for Atrial Fibrillation.

Atrial Fibrillation is a complex disease state with many environmental and genetic risk factors. While there are environmental factors that have been shown to increase an individual's risk of atrial fibrillation, it has become clear that atrial fibrillation has a genetic component that influences why some patients are at a higher risk of developing atrial fibrillation compared to others. This review will first discuss the clinical diagnosis of atrial fibrillation and the corresponding rhythm atrial flutter. We will then discuss how a patients' risk of stroke can be assessed by using other clinical co-morbidities. We will then review the clinical risk factors that can be used to help predict an individual patient's risk of atrial fibrillation. Many of the clinical risk factors have been used to create several different risk scoring methods that will be reviewed. We will then discuss how genetics can be used to identify individuals who are at higher risk for developing atrial fibrillation. We will discuss genome-wide association studies and other sequencing high-throughput sequencing studies. Finally, we will touch on how genetic variants derived from a genome-wide association studies can be used to calculate an individual's polygenic risk score for atrial fibrillation. An atrial fibrillation polygenic risk score can be used to identify patients at higher risk of developing atrial fibrillation and may allow for a reduction in some of the complications associated with atrial fibrillation such as cerebrovascular accidents and the development of heart failure. Finally, there is a brief discussion of how artificial intelligence models can be used to predict which patients will develop atrial fibrillation.

Impact of Median Sternotomy on Safety and Efficacy of the Subcutaneous Implantable Cardioverter Defibrillator.

BACKGROUND: Subcutaneous implantable cardioverter defibrillators (S-ICDs) are an attractive alternative to transvenous ICDs among those not requiring pacing. However, the risks of damage to the S-ICD electrode during sternotomy and adverse interactions with sternal wires remain unclear. We sought to determine the rates of damage to the S-ICD lead during sternotomy, inappropriate shocks from electrical noise due to interaction with sternal wires, and failure to terminate spontaneous or induced ventricular arrhythmias. METHODS: Retrospective, multicenter study of patients undergoing sternotomy before or after S-ICD implantation. Clinical, procedural, and device-related data were collected by each center and analyzed by the coordinating center. These data were compared with a historical control cohort of nonsternotomy patients. RESULTS: Of 196 identified patients (52±16 years, 47 women), 166 underwent S-ICD implantation after sternotomy and 30 sternotomy after S-ICD. There was no damage to any lead among those who underwent sternotomy after S-ICD. Defibrillation threshold testing was performed in 63% at implant, with 91% first shock success. During a median follow-up of 29 months (range, 1-188), S-ICD first shocks successfully terminated spontaneous ventricular arrhythmias in 31 of 32 patients (97%). Inappropriate shocks occurred in 22 patients, most commonly related to T wave oversensing (n=14). Compared with the nonsternotomy controls, there were no differences in rates of first shock success for induced or spontaneous arrhythmias or rate of inappropriate shocks. CONCLUSIONS: Sternotomy before or after S-ICD does not confer additional risk relative to a historical control group without sternotomy.

Clinical Characteristics and Outcomes of Non-ICU Hospitalization for COVID-19 in a Nonepicenter, Centrally Monitored Healthcare System.

BACKGROUND: The clinical characteristics and outcomes associated with non-intensive care unit (non-ICU) hospitalizations for coronavirus disease 2019 (COVID-19) outside disease epicenters remain poorly characterized. METHODS: Systematic analysis of all non-ICU patient hospitalizations for COVID-19 completing discharge between March 13 and May 1, 2020, in a large US health care system utilizing off-site central monitoring. Variables of interest were examined in relation to a composite event rate of death, ICU transfer, or increased oxygen requirement to high-flow nasal cannula, noninvasive ventilation, or mechanical ventilation. RESULTS: Among 350 patients (age, 64 ± 16 years; 55% male), most (73%) required 3 L/min or less of supplemental oxygen during admission. Telemetry was widely utilized (79%) yet arrhythmias were uncommon (14%) and were predominantly (90%) among patients with abnormal troponin levels or known cardiovascular disease. Ventricular tachycardia was rare (5%), nonsustained, and not associated with hydroxychloroquine/azithromycin treatment. Adverse events occurred in 62 patients (18%), including 22 deaths (6%), 48 ICU transfers (14%), and 49 patients with increased oxygen requirement (14%) and were independently associated with elevated C-reactive protein (odds ratio, 1.09 per 1 mg/dL; 95% CI, 1.01-1.18; P = .04) and lactate dehydrogenase (OR, 1.006 per 1U/L; 95% CI, 1.001-1.012; P = .03) in multivariable analysis. CONCLUSION: Among non-critically ill patients hospitalized within a nonepicenter health care system, overall survival was 94% with the development of more severe illness or death independently associated with higher levels of C-reactive protein and lactate dehydrogenase on admission. Clinical decompensation was largely respiratory-related, while serious cardiac arrhythmias were rare, which suggests that telemetry can be prioritized for high-risk patients.

Atrial Tachyarrhythmias Among Patients With Left Ventricular Assist Devices: Prevalence, Clinical Outcomes, and Impact of Rhythm Control Strategies.

OBJECTIVES: This study sought to describe the burden of atrial fibrillation (AF)/atrial flutter (AFL) in patients with left ventricular assist devices (LVAD) and to evaluate the impact of rhythm control strategies. BACKGROUND: AF and AFL among patients with LVADs are poorly characterized. METHODS: Retrospective multivariable survival analysis of all LVAD recipients at the Cleveland Clinic from January 1, 2004 to June 30, 2016 examining the association of death, thromboembolism, and major bleeding with AF/AFL and exposure to rhythm control measures. RESULTS: Among 418 patients (median age: 58 [interquartile range: 50 to 67] years, 80% male) with median follow-up of 445 (interquartile range: 165 to 936) days, AF (n = 287 of 418, 69%) and AFL (n = 61 of 418, 15%) were highly prevalent. Patients with AF/AFL (n = 302 of 418, 72%) and without AF/AFL (n = 116 of 418, 28%) had similar mortality (39% vs. 38%; p = 0.88) and major bleeding (46% vs. 49%; p = 0.53); AF/AFL patients had fewer thromboembolic events (13% vs. 23%; p < 0.01). Paroxysmal or persistent AF/AFL was present in 238 patients (57%), and rhythm control exposure (n = 166, 70%) was not associated with decreased mortality (39% vs. 43%; p = 0.57), thromboembolism (13% vs. 17%; p = 0.41), or bleeding (49% vs. 39%; p = 0.16). In the multivariable survival analysis only prior valve surgery (hazard ratio: 2.0; 95% confidence interval: 1.3 to 3.0; p = 0.002) was associated with increased hazard; AF/AFL had no association with risk of death, thromboembolism, or bleeding. CONCLUSIONS: Though highly prevalent among LVAD patients, AF/AFL was not associated with increased mortality, thromboembolism, or bleeding, and among paroxysmal/persistent AF patients, rhythm control measures were not associated with improved outcomes.

The Transcription Factor Prdm16 Marks a Single Retinal Ganglion Cell Subtype in the Mouse Retina.

Purpose: Retinal ganglion cells (RGC) can be categorized into roughly 30 distinct subtypes. How these subtypes develop is poorly understood, in part because few unique subtype markers have been characterized. We tested whether the Prdm16 transcription factor is expressed by RGCs as a class or within particular ganglion cell subtypes. Methods: Embryonic and mature retinal sections and flatmount preparations were examined by immunohistochemistry for Prdm16 and several other cell type-specific markers. To visualize the morphology of Prdm16+ cells, we utilized Thy1-YFP-H transgenic mice, where a small random population of RGCs expresses yellow fluorescent protein (YFP) throughout the cytoplasm. Results: Prdm16 was expressed in the retina starting late in embryogenesis. Prdm16+ cells coexpressed the RGC marker Brn3a. These cells were arranged in an evenly spaced pattern and accounted for 2% of all ganglion cells. Prdm16+ cells coexpressed parvalbumin, but not calretinin, melanopsin, Smi32, or CART. This combination of marker expression and morphology data from Thy1-YFP-H mice suggested that the Prdm16+ cells represented a single ganglion cell subtype. Prdm16 also marked vascular endothelial cells and mural cells of retinal arterioles. Conclusions: A single subtype of ganglion cell appears to be uniquely marked by Prdm16 expression. While the precise identity of these ganglion cells is unclear, they most resemble the G9 subtype described by Völgyi and colleagues in 2009. Future studies are needed to determine the function of these ganglion cells and whether Prdm16 regulates their development.