DISCUSSION
In this large analysis of elderly patients free of AF and COVID-19 at baseline, but followed up for new COVID-19 cases, we developed a ML based logistic regression algorithm for predicting incident AF accounting for dynamic changes in risk including newly acquired risk factors. Second, DCA showed the ML based logistic regression algorithm had better clinical utility in terms of net benefit than the two treatment strategies (i.e., treat all or none).
The ML analyses demonstrated that COVID-19 status had the strongest independent association with incident AF relative to the traditional cardiovascular co-morbidities including congestive heart failure and coronary artery disease. This was also evident in the main effect analyses. In the absence of COVID-19, the presence of congestive heart failure and coronary disease are independent cardiovascular risk factors leading to incident AF conditions; however, the presence of incident COVID-19 infection changed the importance of classic cardiovascular risk factors feeding into the development of new onset AF. There were also significant and dynamic interactions between the presence of incident COVID-19 infections and co-morbid history including anemia, chronic obstructive pulmonary disease and vascular disease.
In the main effect model, cardiovascular and non-cardiovascular multi-morbidities had significant roles in the spectrum of AF disease complexity in addition to the emergent COVID-19 as a risk factor. As expected, multi-morbidity played an important role in increasing the risk of COVID-19 infection3-5. Demographic variables continued to demonstrate their importance as risk factors associated with the incidence of AF. Age implicated its effects in non-linear terms using both (a) quadratic effects when modelling age as a continuous variables, and (b) interactive terms (with coronary artery disease and chronic kidney disease) upon the use of age as a categorical variable. Gender showed its influence in interactive terms with the co-morbid history (chronic obstructive pulmonary disease, major bleeding).
Our findings are important given the worse prognosis amongst COVID-19 patients with AF, with a higher risk of mortality when compared to AF patients without COVID-19 patients 9. Our ML prediction could be incorporated into telehealth approaches to monitor patients following their COVID-19 diagnosis, for the onset of incident AF10. Given the increasing focus on integrated care management of patients with AF11, novel ML approaches could facilitate structured management and follow-up, especially since risk profiles change in a dynamic manner over time12-14. Such a structured approach to holistic AF care, including proactive risk evaluation, has been shown to be associated with improved clinical outcomes, especially with a reduction in hospitalisations and bleeding events15-17.