An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk.

Background: Several large trials have employed age or clinical features to select patients for atrial fibrillation (AF) screening to reduce strokes. We hypothesized that a machine learning (ML) model trained to predict AF risk from 12‑lead electrocardiogram (ECG) would be more efficient than criteria based on clinical variables in indicating a population for AF screening to potentially prevent AF-related stroke.

Methods: We retrospectively included all patients with clinical encounters in Geisinger without a prior history of AF.

Comparison of treatment modalities for single-sided deafness in paediatric patients.

Objective: Children with single-sided deafness often receive inconsistent clinical recommendations because there is currently no clear best practice in paediatric single-sided deafness. This systematic review of the literature aimed to compare commonly used treatments and attempted to support the use of a particular treatment modality.

Method: This was a comprehensive literature review from 1 January 2000 to 22 February 2022; the study compared the outcomes of bone conduction devices and cochlear implantation in paediatric patients with single-sided deafness.