A prediction model for major adverse cardiovascular events in patients with heart failure based on high-throughput echocardiographic data

Qinliang Sun; Shuangquan Jiang; Xudong Wang; Jingchun Zhang; Yi Li; Jiawei Tian; Hairu Li

doi:10.3389/fcvm.2022.1022658

A prediction model for major adverse cardiovascular events in patients with heart failure based on high-throughput echocardiographic data

Front Cardiovasc Med. 2022 Oct 28:9:1022658. doi: 10.3389/fcvm.2022.1022658. eCollection 2022.

Authors

Qinliang Sun¹, Shuangquan Jiang¹, Xudong Wang¹, Jingchun Zhang², Yi Li¹, Jiawei Tian¹, Hairu Li¹

Affiliations

¹ Department of Ultrasound Imaging, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
² Department of Gastroenterology, Digestive Disease Hospital, Heilongjiang Provincial Hospital Affiliated to Harbin Institute of Technology, Harbin, China.

Abstract

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.

Keywords: heart failure; nomogram; prediction model; speckle tracking; vector flow mapping.