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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
A prediction model for major adverse cardiovascular events in patients with heart failure based on high-throughput
Qinliang Sun1, Shuangquan Jiang1, Xudong Wang1
1Department of Ultrasound Imaging, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Insights
This study developed a nomogram model to predict major cardiovascular events (MACE) in heart failure patients. The model integrates clinical data with advanced echocardiography, improving risk stratification for tailored treatment.
Area of Science:
- Cardiology
- Medical Imaging
- Data Science
Background:
- Heart failure (HF) is a growing global health concern.
- Limited studies combine clinical and high-throughput echocardiographic data for HF risk assessment.
- Accurate prognostication is crucial for managing heart failure patients.
Purpose of the Study:
- To develop a practical and accurate prognostic nomogram for heart failure.
- To identify high-risk patient groups for major cardiovascular events (MACE).
- To provide a tool for tailored treatment strategies in heart failure.
Main Methods:
- A prospective study of 468 heart failure patients.
- Integration of 320 features: clinical data, echocardiography (2D, Doppler, STE), and vector flow mapping (VFM).
- Development and validation of a predictive model using machine learning (XGBoost).
Main Results:
- 156 patients (33.3%) experienced MACE within 6 months post-discharge.
- The prediction model demonstrated strong discrimination (C-statistic = 0.876).
- Models incorporating STE and VFM parameters showed superior predictive accuracy (AUC).
Conclusions:
- A novel nomogram model accurately predicts 6-month MACE risk in heart failure patients.
- The model integrates clinical, structural, functional, and hemodynamic data.
- This tool aids clinicians in early risk detection and personalized management of heart failure.
Background:
Heart failure (HF) is a serious end-stage condition of various heart diseases with increasing frequency. Few studies have combined clinical features with high-throughput echocardiographic data to assess the risk of major cardiovascular events (MACE) in patients with heart failure. In this study, we assessed the relationship between these factors and heart failure to develop a practical and accurate prognostic dynamic nomogram model to identify high-risk groups of heart failure and ultimately provide tailored treatment options.
Materials And Methods:
We conducted a prospective study of 468 patients with heart failure and established a clinical predictive model. Modeling to predict risk of MACE in heart failure patients within 6 months after discharge obtained 320 features including general clinical data, laboratory examination, 2-dimensional and Doppler measurements, left ventricular (LV) and left atrial (LA) speckle tracking echocardiography (STE), and left ventricular vector flow mapping (VFM) data, were obtained by building a model to predict the risk of MACE within 6 months of discharge for patients with heart failure. In addition, the addition of machine learning models also confirmed the necessity of increasing the STE and VFM parameters.
Results:
Through regular follow-up 6 months after discharge, MACE occurred in 156 patients (33.3%). The prediction model showed good discrimination C-statistic value, 0.876 (p < 0.05), which indicated good identical calibration and clinical efficacy. In multiple datasets, through machine learning multi-model comparison, we found that the area under curve (AUC) of the model with VFM and STE parameters was higher, which was more significant with the XGboost model.
Conclusion:
In this study, we developed a prediction model and nomogram to estimate the risk of MACE within 6 months of discharge among patients with heart failure. The results of this study can provide a reference for clinical physicians for detection of the risk of MACE in terms of clinical characteristics, cardiac structure and function, hemodynamics, and enable its prompt management, which is a convenient, practical and effective clinical decision-making tool for providing accurate prognosis.
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