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.