# Prediction of Cardiac Remodeling and/or Myocardial Fibrosis Based on Hemodynamic Parameters of Vena Cava in Athletes

## TL;DR

This study uses machine learning to predict heart changes in athletes based on blood flow in the vena cava.

## Contribution

A novel Gradient Boosting Machine model was developed to predict cardiac remodeling and myocardial fibrosis in athletes using vena cava hemodynamics.

## Key findings

- Gradient Boosting Machine achieved 89.1% AUC in predicting cardiac remodeling and/or fibrosis.
- Key predictors included net positive volume, forward volume, and body metrics like waist circumference.
- Athletes showed increased vena cava flow parameters compared to sedentary controls.

## Abstract

This study aimed to assess the hemodynamic changes in the vena cava and predict the likelihood of Cardiac Remodeling (CR) and Myocardial Fibrosis (MF) in athletes utilizing four-dimensional (4D) parameters.

A total of 108 athletes and 29 healthy sedentary controls were prospectively recruited and underwent Cardiac Magnetic Resonance (CMR) scanning. The 4D flow parameters, including both general and advanced parameters of four planes for the Superior Vena Cava (SVC) and Inferior Vena Cava (IVC) (sheets 1-4), were measured and compared between the different groups. Four machine learning models were employed to predict the occurrence of CR and/or MF.

Most general 4D flow parameters related to VC were increased in athletes and positive athletes compared to controls (p < 0.05). Gradient Boosting Machine (GBM) was the most effective model in sheet 2 of SVC, with the area under the curve values of 0.891, accuracy of 85.2%, sensitivity of 84.6%, and specificity of 85.4%. The top five predictors in descending order were as follows: net positive volume, forward volume, waist circumference, body weight, and body surface area.

Physical activity can induce a high flow state in the vena cava. CR and/or MF may elevate the peak velocity and maximum pressure gradient of the IVC. This study successfully constructed a GBM model with high efficacy for predicting CR and/or MF. This model may provide guidance on the frequency of follow-up and the development of appropriate exercise plans for athletes.