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

Abstract

BackgroundHeart 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 methodsWe 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.ResultsThrough 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.ConclusionIn 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.

Machine learning in heart failure diagnosis, prediction and prognosis: Review

“…Sun et al . 40 developed a scoring system to predict the risk of major adverse cardiovascular events in patients with congestive heart failure (CHF). Sun et al .…”

“…Sun et al . 40 study aimed to develop a predictive model suitable for different HF populations, focusing on LVEF analysis variables without categorizing LVEF subtypes. The study introduced the use of velocity flow mapping (VFM) parameters in the predictive model.…”

“…ML algorithms, particularly the XGBoost classifier, were employed to analyze the predictive ability of the model. The inclusion of general parameters, speckle-tracking echocardiography (STE)-related parameters, and VFM-related parameters in the XGBoost classifier resulted in improved classification accuracy compared to other algorithms 40 .…”

A Common Variant of ARRB2 Promoter Region Associated with the Prognosis of Heart Failure

“…Heart failure (HF) is the serious end-stage clinical syndrome caused by various factors including coronary heart disease, hypertension, valvular heart disease, and idiopathic dilated cardiomyopathy [1]. Although significant advances have been made in the treatment of chronic HF, the mortality and rehospitalization rates remain high [2].…”

Serum apolipoprotein B to apolipoprotein A‐I ratio predicts mortality in patients with heart failure

“…Heart failure (HF) is a common and lethal disease caused by various factors including coronary heart disease, hypertension, valvular heart disease, and idiopathic dilated cardiomyopathy. 1 Despite the fact that significant advances have been made in the diagnosis and treatment of chronic HF, the mortality and rehospitalization rate remain high. 2 As the ageing of population and HF risk factors increase, HF has become a serious challenge for the public health.…”