A Machine Learning-Derived Score to Effectively Identify Heart Failure With Preserved Ejection Fraction

作者：Kevin Bermea, Jana Lovell, Allison G. Hays, Erin Goerlich, Soumya Vungarala, Vivek Jani, Sanjiv J. Shah, Kavita Sharma, Luigi Adamo · 发表于：JACC Advances · 年份：2024 · DOI：10.1016/j.jacadv.2024.101040 · 被引用次数：13 · 研究领域：Cardiovascular Function and Risk Factors、Heart Failure Treatment and Management、Cardiovascular Disease and Adiposity

Background: The diagnosis of heart failure with preserved ejection fraction (HFpEF) in the clinical setting remains challenging, especially in patients with obesity. Objectives: This study aimed to identify novel predictors of HFpEF well suited for patients with obesity. Methods: ) and controls (n = 67). We used the machine learning algorithm Gradient Boosting Machine to analyze the association of various parameters with the diagnosis of HFpEF and subsequently created a multivariate logistic model for the diagnosis. Results: < 0.001). Conclusions: In a HFpEF cohort with obesity, BMI, estimated glomerular filtration rate, left ventricular mass index, and left atrial to left ventricular volume ratio most correlated with the identification of HFpEF, and a score based on these variables (HFpEF-JH score) outperformed the currently used H2PEF score. Further validation of this novel score is warranted, as it may facilitate improved diagnostic accuracy of HFpEF, particularly in patients with obesity.