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.