Publications by Year: 2024

2024

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Ferro, Enrico G, Matthew R Reynolds, Jiaman Xu, Yang Song, David J Cohen, Rishi K Wadhera, Andre D’Avila, Peter J Zimetbaum, Robert W Yeh, and Daniel B Kramer. (2024) 2024. “Outcomes of Atrial Fibrillation Ablation Among Older Adults in the United States: A Nationwide Study.”. JACC. Clinical Electrophysiology 10 (7 Pt 1): 1341-50. https://doi.org/10.1016/j.jacep.2024.03.032.

BACKGROUND: Pulmonary vein isolation (PVI) is increasingly recommended as first-line therapy for atrial fibrillation. Recent data suggest growing PVI volumes but rising complication rates, although comprehensive real-world outcomes including both inpatient and outpatient encounters remain unclear.

OBJECTIVES: The purpose of this study was to evaluate patient characteristics, population rates, and 30-day outcomes of PVI in a nationwide sample of U.S. adults aged >65 years.

METHODS: First-time PVIs were identified among U.S. Medicare fee-for-service beneficiaries using Current Procedural Terminology procedural codes. Comorbidities were ascertained using International Classification of Diseases-10th Revision diagnosis codes associated with each procedural claim. Outcomes included periprocedural complications, all-cause hospitalizations, and mortality at 30 days.

RESULTS: From January 2017 through December 2021, a total of 227,133 patients underwent PVI (mean age 72.5 years, 42% women, 92.7% White) with an increasing comorbidity burden over time. PVI volume increased from 83.8 (2017) to 111.6 per 100,000 patient-years (2021), which was driven by outpatient procedures (87.8% of all PVIs). Concurrently, there was a significant decrease in complication rates (3.9% in 2017 vs 3.1% in 2021; P < 0.001) and hospitalizations (8.8% vs 7.0%; P < 0.001), with no significant change in mortality (0.4%; P = 0.08). The most common periprocedural complications were bleeding (1.8%), pericardial effusion (1.4%), and vascular access damage (0.8%).

CONCLUSIONS: The use of PVI has steadily increased among older patients in contemporary U.S. clinical practice; yet, cumulative complication and hospitalization rates at 30 days have decreased over time, with stably low rates of short-term mortality despite rising comorbidity burden among treated patients. These data may reassure patients and providers on the safety of PVI as an increasingly common first-line procedure for atrial fibrillation.

Sau, Arunashis, Antônio H Ribeiro, Kathryn A McGurk, Libor Pastika, Nikesh Bajaj, Mehak Gurnani, Ewa Sieliwonczyk, et al. (2024) 2024. “Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.”. Circulation. Cardiovascular Quality and Outcomes 17 (12): e010602. https://doi.org/10.1161/CIRCOUTCOMES.123.010602.

BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. We aimed to investigate whether neural network-derived ECG features could be used to predict future cardiovascular disease and mortality and have phenotypic and genotypic associations.

METHODS: We extracted 5120 neural network-derived ECG features from an artificial intelligence-enabled ECG model trained for 6 simple diagnoses and applied unsupervised machine learning to identify 3 phenogroups. Using the identified phenogroups, we externally validated our findings in 5 diverse cohorts from the United States, Brazil, and the United Kingdom. Data were collected between 2000 and 2023.

RESULTS: In total, 1 808 584 patients were included in this study. In the derivation cohort, the 3 phenogroups had significantly different mortality profiles. After adjusting for known covariates, phenogroup B had a 20% increase in long-term mortality compared with phenogroup A (hazard ratio, 1.20 [95% CI, 1.17-1.23]; P<0.0001; phenogroup A mortality, 2.2%; phenogroup B mortality, 6.1%). In univariate analyses, we found phenogroup B had a significantly greater risk of mortality in all cohorts (log-rank P<0.01 in all 5 cohorts). Phenome-wide association study showed phenogroup B had a higher rate of future atrial fibrillation (odds ratio, 2.89; P<0.00001), ventricular tachycardia (odds ratio, 2.00; P<0.00001), ischemic heart disease (odds ratio, 1.44; P<0.00001), and cardiomyopathy (odds ratio, 2.04; P<0.00001). A single-trait genome-wide association study yielded 4 loci. SCN10A, SCN5A, and CAV1 have roles in cardiac conduction and arrhythmia. ARHGAP24 does not have a clear cardiac role and may be a novel target.

CONCLUSIONS: Neural network-derived ECG features can be used to predict all-cause mortality and future cardiovascular diseases. We have identified biologically plausible and novel phenotypic and genotypic associations that describe mechanisms for the increased risk identified.