INTRODUCTION
Multi-morbidity is associated with adverse health outcomes and healthcare costs, especially among the elderly1,2. Preliminary evidence suggests that multi-morbidity is associated with confirmed COVID-19 infections notably among the elderly with several co-morbidities3-5. Furthermore, it has been hypothesized that COVID-19 is associated with incident atrial fibrillation (AF) but large studies are lacking to test this hypothesis6,7.
In light of the above, the elderly population is usually closely scrutinized due to (a) the staggering healthcare costs reaching in many countries above 70% of the national healthcare expenditures, and (b) the need to improve the quality of integrated care because of the presence of multi-morbid conditions. The elderly multi-morbid patient is at high risk of adverse outcomes with COVID-19 complications3-5, and in the general population, the development of incident AF is associated with worse outcomes in such patients8. 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 characteristics consisting of diverse cardiovascular/non-cardiovascular multi-morbidities. We used two approaches: main effect modeling as well as a machine-learning (ML) approach, accounting for the complex dynamic relationships among co-morbidity variables.