Incident atrial fibrillation and its risk prediction in patients developing COVID-19: A machine learning based algorithm approach.

ABSTRACT:

Background

The elderly multi-morbid patient is at high risk of adverse outcomes with COVID-19 complications, and in the general population, the development of incident AF is associated with worse outcomes in such patients. There is therefore the need to identify those patients with COVID-19 who are at highest risk of developing incident AF. We therefore investigated incident AF risks in a large prospective population of elderly patients with/without incident COVID-19 cases and baseline cardiovascular/non-cardiovascular multi-morbidities. We used two approaches: main effect modeling and secondly, a machine-learning (ML) approach, accounting for the complex dynamic relationships among comorbidity variables.

Methods

We studied a prospective elderly US cohort of 280,592 patients

SUBMITTER: Lip GYH 

PROVIDER: S-EPMC8118660 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature