From Valves to Vessels: A Machine Learning Approach to Explore Heart Failure Risk in ICU Patients with Aortic Valve and Aortic Vascular Disorders

Background and objectives: Aortic valve and aortic vascular disorders represent a subset of cardiovascular conditions that can lead to heart failure. Aortic valve diseases, such as aortic stenosis or regurgitation, and aortic vascular diseases, including aortic aneurysms and aortic dissection, can contribute to impaired cardiac function and increase the risk of heart failure. This study aims to investigate the risk of heart failure in patients with aortic valve and aortic vascular disorders within 30-days of admission in the Intensive Care Unit (ICU). Methods: Patients from a US-based critical care database (MIMIC-III) who developed heart failure in the ICU within 30 days of follow-up were included. Two predictive models, XGBoost and logistic regression, were developed and evaluated using ROC, sensitivity, specificity, and F1 measure. The dataset was split into training and testing samples in an 8:2 ratio. Results: Out of 2,871 patients analyzed, 1,062 (37%) developed heart failure in the ICU during the 30-day follow-up. Key predictors of heart failure included creatinine, phosphate, age, COPD, INR, diabetes, CAD, magnesium, atrial fibrillation, and hyperlipidemia. The logistic regression model outperformed XGBoost with (AU-ROC, 0.78 vs. 0.77, respectively). Conclusions: This study demonstrates the potential of machine learning techniques to enhance predictive modeling in critical care research. It provides valuable insights into heart failure risk in patients with aortic valve and aortic vascular disorders admitted to the ICU.