Safety and feasibility of a 1 frame-per-second X-ray framerate in cardiac electrophysiology.

BACKGROUND: We investigated the feasibility and safety of a framerate of 1 frame per second ("fps") for fluoroscopy and cine-angiography, to lower radiation exposure for patients and personnel in cardiac electrophysiology ("EP"). METHOD: Analysis of 2521 EP procedures, 899 (36%) with the lowest available conventional framerate (3.75 fps) and 1622 (64%) procedures performed with a framerate lowered further to 1.0 fps (by looping a 1 Hz square pulse to the ECG trigger) performed between 01/2016 and 01/2020. RESULTS: Procedures performed with 1.0 fps had the same acute procedural success rates (p = 0.20) and adverse event rates (p = 0.34) as the 3.75 fps group. There was no difference in total X-ray operation time (p = 0.40). The dose-area-product (DAP) was significantly reduced from 638 to 316 cGy*cm2 (p < < 0.0001) for all procedure types together, and for each subgroup. In a multivariable linear regression model, total X-ray operation time (estimate 38 cGy*cm2 /min) and body mass index (estimate 32 cGy*cm2 / index point) and a framerate of 1.0 fps (-314 cGy*cm2 against 3.75 fps) were independent predictors of a lower DAP (p-value of t-statistic for all << 0.0001). CONCLUSIONS: A framerate of 1.0 fps is safe and feasible in cardiac electrophysiology procedures. It was associated with a significant reduction of radiation exposure for patient and personnel.

Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions.

Atrial fibrillation (AF) is the most common arrhythmia and has a major impact on morbidity and mortality; however, detection of asymptomatic AF is challenging. This study sims to evaluate the sensitivity and specificity of non-invasive AF detection by a medical wearable. In this observational trial, patients with AF admitted to a hospital carried the wearable and an ECG Holter (control) in parallel over a period of 24 h, while not in a physically restricted condition. The wearable with a tight-fit upper armband employs a photoplethysmography technology to determine pulse rates and inter-beat intervals. Different algorithms (including a deep neural network) were applied to five-minute periods photoplethysmography datasets for the detection of AF. A total of 2306 h of parallel recording time could be obtained in 102 patients; 1781 h (77.2%) were automatically interpretable by an algorithm. Sensitivity to detect AF was 95.2% and specificity 92.5% (area under the receiver operating characteristics curve (AUC) 0.97). Usage of deep neural network improved the sensitivity of AF detection by 0.8% (96.0%) and specificity by 6.5% (99.0%) (AUC 0.98). Detection of AF by means of a wearable is feasible in hospitalized but physically active patients. Employing a deep neural network enables reliable and continuous monitoring of AF.