# The Clinical Significance and Potential of Complex Diagnosis for a Large Scar Area Following Myocardial Infarction

## TL;DR

This study identifies clinical markers to predict large heart scars after heart attacks using MRI and common blood tests.

## Contribution

A new multifactorial model using HscTn I, NT-proBNP, and LV RWT to predict large scar areas after myocardial infarction is proposed.

## Key findings

- 39% of patients had large scars (Mscar/LVMM > 20%) while 61% had smaller scars.
- A model using HscTn I, NT-proBNP, and LV RWT predicted large scars with 63.6% sensitivity and 85.7% specificity.

## Abstract

Background/Objectives: The aim of this study is to identify markers and develop a multifactorial model for characterizing extensive scar tissue after revascularization in patients with myocardial infarction (MI). Methods: A total of 123 patients with MI were examined. The patients underwent contrast-enhanced cardiac magnetic resonance imaging (MRI) with a 1.5 Tesla GE SIGNA Voyager (GE HealthCare, Chicago, IL, USA) on the 7th–10th days from the onset of the disease. At the first stage, we performed a comparative analysis and built a multifactorial model based on the examination results of 92 (75%) patients enrolled from April 2021 to October 2023. These patients formed the group used for model development, or the “modeling group”. The mass of the scar was calculated, including relative to the left ventricular (LV) myocardium mass (Mscar/LVMM, in %). Results: The first subgroup consisted of 36 (39%) patients with a large scar, denoted as “LS” (Mscar/LVMM > 20%). The second subgroup included 56 (61%) patients with a smaller scar, referred to as “SS” (Mscar/LVMM ≤ 20%). Logistic regression was used to identify independent factors affecting scar tissue size. A multifactorial model was created. This model predicts Mscar/LVMM > 20% on MRI. It uses readily available clinical parameters: high-sensitivity troponin I (HscTn I) and N-terminal pro B-type natriuretic peptide (NT-proBNP) levels, and LV relative wall thickness (RWT). We tested the multifactorial model on the “modeling group” (n = 31). The sensitivity was 63.6% and the specificity was 85.7%. Conclusions: These indicates the feasibility of its application in clinical practice.