Incidence and Complications of Atrial Fibrillation in a Low Socioeconomic and High Disability United States (US) Population: A Combined Statistical and Machine Learning Approach.

Background: Poor socioeconomic status coupled with individual disability is significantly associated with incident atrial fibrillation (AF) and AF-related adverse outcomes, with the information currently lacking for US cohorts. We examined AF incidence/complications and the dynamic nature of associated risk factors in a large socially disadvantaged US population.

Methods: A large population representing a combined poor socioeconomic status/disability (Medicaid program) was examined from diverse geographical regions across the US continent. The target population was extracted from administrative databases with patients possessing medical/pharmacy benefits. This retrospective cohort study was conducted from Jan 1, 2016, to Sep 30, 2021, and was limited to 18- to 80-year age group drawn from the Medicaid program. Descriptive and inferential statistics (parametric: logistic regression and neural network) were applied to all computations using a combined statistical and machine learning (ML) approach.

Results: A total of 617413 individuals participated in the study, with mean age of 41.7 years (standard deviation "SD" 15.2) and 65.6% female patients. Seven distinct groups were identified with different combinations of low socioeconomic status and disability constraints. The overall crude AF incidence rate was 0.49 cases/100 person-years (95% confidence limit "CI" 0.40-0.58), with the lowest rate for the younger group (temporary assistance for needy family "TANF") (0.20, 95%CI 0.18-0.21), the highest rates for the older groups (age, blindness, or disability "ABD" duals-1.51, 95% CI 1.31-1.58; long-term services and support "LTSS" duals-1.45, 95% CI 1.31-1.58), and the remaining four other groups in between the lower and upper rates. Based on independent effects after accounting for confounders in main effect modeling, the point estimates of odds ratios for AF status with various clinical outcomes were as follows: stroke (2.69, 95% CI 2.53-2.85); heart failure (6.18, 95% CI 5.86-6.52); myocardial infarction (3.71, 95% CI 3.49-3.94); major bleeding (2.26, 95% CI 2.14-2.38); and cognitive impairment (1.74, 95% CI 1.59-1.91). A logistic regression-based ML model produced excellent discriminant validity for high-risk AF outcomes (c "concordance" index based on training data 0.91, 95%CI 0.891-0.929), together with similar measures for external validity, calibration, and clinical utility. The performance measures for the ML models predicting associated complications with high-risk AF cases were good to excellent.

Conclusions: A combination of low socioeconomic status and disability contributes to AF incidence and complications, elevating risks to higher levels relative to the general population. ML algorithms can be used to identify AF patients at high risk of clinical events. While further research is definitely in need on this socially important issue, the reported investigation is unique in which it integrates the general case about the subject due to the different ethnic groups around the world under a unified culture stemming from residing in the US.