Determining optimal strategies for personalized atrial fibrillation treatment in intensive care unit patients using a deep learning-based causal inference approach: rhythm and/or rate control

OBJECTIVES: Atrial fibrillation (AF) is common among intensive care unit (ICU) patients. Effective management of AF in this setting remains a subject of debate, with current guidelines often derived from outpatient studies. This study aims to evaluate the effectiveness of different AF management strategies-both, rhythm, rate, or no control-in reducing mortality in ICU patients using a deep learning-based causal inference model. MATERIALS AND METHODS: Data from the Medical Information Mart for Intensive Care (MIMIC)-III and MIMIC-IV were utilized, encompassing ICU admissions with documented AF. Exposures included both rhythm and rate, only rhythm, and only rate, or no control. A deep learning-based causal inference model analyzed treatment effects. Additionally, the characteristics of patients who benefited more from rhythm control compared to rate control were identified using treatment effect sizes and multivariable logistic regression. RESULTS: The study population comprised 13 583 patients. Both rhythm and rate control, rhythm control-only, and rate control-only strategies significantly reduced in-hospital mortality compared to no control, with average treatment effects of -1.23% (-1.43% to -1.03%), -2.32% (-2.48% to -2.15%), and -9.11% (-9.29% to -8.93%), respectively. Rhythm control proved more effective than rate control in specific subgroups: older age, higher maximum heart rate, presence of new-onset AF, absence of hypertension, absence of diabetes, chronic liver disease, not having undergone heart surgery, and the use of vasopressor agents. CONCLUSION: Using a deep learning-based causal inference model, we quantified mortality reduction for each treatment strategy and identified the patient characteristics associated with the most favorable outcomes for each strategy.