Abstract 18281: Identifying Predictors of ICU Mortality Outcomes Among Critically Ill Patients With Atrial Fibrillation Using Machine Learning Models

Abstract

Introduction: New onset atrial fibrillation (Afib) is a frequent complication of intensive care admission and is linked to higher mortality in the ICU. We aimed to develop machine learning (ML) models to predict the risk of death in the ICU among these patients and assess their long-term prognosis. Methods: Eligible patients with Afib from the Medical Information Mart for Intensive Care IV database were included. The cases were randomly divided into training (75%) and test (25%) groups. Machine learning algorithms such as light GBM, random forest, gradient boosted classifier, and XGBoost were used. In addition, we utilized a tabular deep learning model architecture comprised of an embedding layer for categorical variables. The robustness of the results was ensured through K-fold cross-validation. AUROC and accuracy were used to compare model performance. Models accounted for variables from demographics, labs, charting events, and comorbidity domains. A web-based calculator was also developed to encourage the model's practical application. Results: The sample (n = 8455) consisted of 3408 females (40.3%), 6004 whites (71.0%), and 4876 (57.7%) patients with Medicare insurance. The ICU mortality was 9.5% (n = 802). The median age (79 vs. 75 years; p < 0.001) and ICU LOS (4 vs. 2 days; p < 0.001) were higher among the patients who expired. Congestive heart failure was reported in 15% of cases. The lightGBM model (AUROC: 84%) performed best and showed that mean arterial BP, WBC, PO2, platelet count, age, hemoglobin, BUN, pH, ICU LOS and anion gap were the top predictors of ICU mortality among this population. Conclusion: Death in the ICU among patients with Afib was associated with several clinical features, especially BP, WBC count, PO2, platelet count, age, and hemoglobin. Predictors of ICU mortality were identified with high accuracy and this may aid in the creation of a real time risk score for selective intervention to reduce the risk of death in this vulnerable patient population.