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).