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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
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Approaching Higher Dimension Imaging Data Using Cluster-Based Hierarchical Modeling in Patients with Heart Failure
Yukari Kobayashi1,2, Maxime Tremblay-Gravel3,4, Kalyani A Boralkar3,4
1Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, United States. yukariko@stanford.edu.
Scientific Reports
|July 20, 2019
Summary
N-terminal pro-B-type natriuretic peptide (NT-proBNP) and right ventricular systolic pressure (RVSP) predict mortality in heart failure with preserved ejection fraction (HFpEF). These biomarkers improve existing risk scores, aiding in better patient management.
Area of Science:
- Cardiology
- Biomarkers
- Predictive Modeling
Background:
- Heart failure with preserved ejection fraction (HFpEF) is a leading cause of morbidity and mortality, driving a majority of heart failure hospitalizations.
- Identifying key predictors of mortality in HFpEF is crucial for improving patient outcomes.
Purpose of the Study:
- To identify complementary predictors of mortality in HFpEF by analyzing clinical, laboratory, and echocardiographic data.
- To evaluate the added value of NT-proBNP and RVSP to existing risk scores for HFpEF mortality prediction.
Main Methods:
- A cohort of 186 patients hospitalized with HFpEF was identified from the Stanford Translational Research Database (2005-2016).
- Comprehensive echocardiographic assessment, including left ventricular longitudinal strain (LVLS) and RVSP, along with NT-proBNP levels, were analyzed.
- Unsupervised cluster analyses and stepwise hierarchical modeling were employed to identify independent predictors of all-cause mortality.
Main Results:
- N-terminal pro-B-type natriuretic peptide (NT-proBNP) (HR 1.56 [1.17-2.08]) and right ventricular systolic pressure (RVSP) (HR 1.37 [1.09-1.78]) were identified as independent predictors of mortality.
- The addition of NT-proBNP and RVSP to the validated Get with the Guideline Heart Failure risk score significantly improved its predictive accuracy (p=0.01).
Conclusions:
- NT-proBNP and RVSP are independently predictive of mortality in HFpEF patients.
- Cluster-based hierarchical modeling is a valuable approach for identifying complementary predictive parameters in complex clinical datasets.
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