Artificial intelligence-enhanced 12-lead electrocardiography for identifying atrial fibrillation during sinus rhythm (AIAFib) trial: protocol for a multicenter retrospective study.

Introduction: Atrial fibrillation (AF) is the most common arrhythmia, contributing significantly to morbidity and mortality. In a previous study, we developed a deep neural network for predicting paroxysmal atrial fibrillation (PAF) during sinus rhythm (SR) using digital data from standard 12-lead electrocardiography (ECG). The primary aim of this study is to validate an existing artificial intelligence (AI)-enhanced ECG algorithm for predicting PAF in a multicenter tertiary hospital. The secondary objective is to investigate whether the AI-enhanced ECG is associated with AF-related clinical outcomes. Methods and analysis: We will conduct a retrospective cohort study of more than 50,000 12-lead ECGs from November 1, 2012, to December 31, 2021, at 10 Korean University Hospitals. Data will be collected from patient records, including baseline demographics, comorbidities, laboratory findings, echocardiographic findings, hospitalizations, and related procedural outcomes, such as AF ablation and mortality. De-identification of ECG data through data encryption and anonymization will be conducted and the data will be analyzed using the AI algorithm previously developed for AF prediction. An area under the receiver operating characteristic curve will be created to test and validate the datasets and assess the AI-enabled ECGs acquired during the sinus rhythm to determine whether AF is present. Kaplan-Meier survival functions will be used to estimate the time to hospitalization, AF-related procedure outcomes, and mortality, with log-rank tests to compare patients with low and high risk of AF by AI. Multivariate Cox proportional hazards regression will estimate the effect of AI-enhanced ECG multimorbidity on clinical outcomes after stratifying patients by AF probability by AI. Discussion: This study will advance PAF prediction based on AI-enhanced ECGs. This approach is a novel method for risk stratification and emphasizes shared decision-making for early detection and management of patients with newly diagnosed AF. The results may revolutionize PAF management and unveil the wider potential of AI in predicting and managing cardiovascular diseases. Ethics and dissemination: The study findings will be published in peer-reviewed publications and disseminated at national and international conferences and through social media. This study was approved by the institutional review boards of all participating university hospitals. Data extraction, storage, and management were approved by the data review committees of all institutions. Clinical Trial Registration: [cris.nih.go.kr], identifier (KCT0007881).

Increased atrial fibrillation risk in Parkinson's disease: A nationwide population-based study.

OBJECTIVE: Parkinson's disease (PD) is the second most common neurodegenerative disorder associated with various morbidities. Although the relationship between cardiovascular disease and PD has been studied, a paucity of information on PD and atrial fibrillation (AF) association exists. Thus, we aimed to investigate whether patients with PD have an increased risk of AF. METHODS: This study included 57,585 patients with newly diagnosed PD (≥40-year-old, mean age 69.7 years, men 40.2%) and without a history of AF from the Korean National Health Insurance Service (NHIS) database between 2010 and 2015. Furthermore, an equal number of age- and sex-matched subjects without PD were selected for comparison. The primary outcome was new-onset AF. RESULTS: During the mean follow-up period of 3.4 ± 1.8 years, AF was newly diagnosed in 3,665 patients. A significantly higher incidence rate of AF was noted among patients with PD than among patients without PD (10.75 and 7.86 per 1000 person-year, respectively). Multivariate Cox-regression analysis revealed that PD was an independent risk factor for AF (hazard ratio [HR]: 1.27, 95% confidence interval [CI]: 1.18-1.36). Furthermore, subgroup analyses revealed that AF risk was higher in the younger age subgroups, and compared with the non-PD group, the youngest PD group (age: 40-49 years) had a threefold increased risk of AF (HR: 3.06, 95% CI: 1.20-7.77). INTERPRETATION: Patients with PD, especially the younger age subgroups, have an increased risk of AF. Active surveillance and management of AF should be considered to prevent further complications.

Radiosensitization of tumor cells by modulation of ATM kinase.

PURPOSE: To elucidate the relationship between the radiation-induced activation of ataxia telangiectasia mutated (ATM) kinase, G2 arrest and the caffeine-induced radiosensitization. METHOD: RKO cells (human colorectal cancer cells) and ATM kinase over-expressing RKO/ATM cells were used. The cellular radiosensitivity was determined with clonogenic survival assay and the cell cycle progression, including G2 arrest, was studied with flow cytometry. The activity of ATM kinase, check point 2 (Chk2) kinase and cycline B1/cell division cycle 2 (Cdc2) kinase was investigated. The radiosensitivity of RKO xenografts grown in nude mice was studied. RESULTS: RKO/ATM cells were radioresistant as compared with RKO cells. There was a greater increase in ATM kinase activity and G2 arrest in RKO/ATM cells than in RKO cells. Caffeine also sensitized both RKO cells and RKO/ATM cells to radiation. The caffeine treatment suppressed the radiation-induced activation of ATM kinase, suppressed the activation of Chk2 kinase and inhibited the accumulation of cells in G2 phase. The activity of cycline B1/Cdc2 kinase increased earlier but decayed rapidly in the presence of caffeine. Caffeine enhanced radiation-induced growth delay of RKO xenografts. CONCLUSIONS: Caffeine inhibited the radiation-induced activation of ATM kinase, thereby preventing the accumulation of cells in G2 phase. Consequently, radiosensitivity of cells increased in the presence of caffeine both in vitro and in vivo.