Statistical analysis
Statistical analyses were conducted using main effects with COVID-19 or
AF as an outcome, with logistic regression modeling using the SAS
Enterprise software. Prediction modeling was pursued using the
Enterprise SAS Miner software for complex relationships between AF as a
binary outcome and comorbid history / COVID-19 status / demographic
variables. All ML based modeling accounted for dynamic changes in
risk including newly acquired risk factors, hence consisting of complex
interactions among the comorbid condition history as well as incident
conditions such as COVID-19 conditions. The ML based logistic regression
algorithm included main effects, interaction terms and polynomial
effects, with the model selection based on the stepwise method. Several
polynomial terms were included in the ML formulation.
Model validation was based on calibration, discrimination, and clinical
utility. Each model was trained on 67% of the data, with the remaining
33% data used for external validation. In this respect, the development
and validation samples were extracted at random. Discriminant validity
was assessed using C-indexes (area under the curve) for both the
development and validation samples, separately. In addition, clinical
utility was assessed using decision curve analysis (DCA).