Study on Risk Early Warning of Clinical Prediction Model Based on Multi-Parameter Stress Perfusion Cardiac Magnetic Resonance in Adverse Prognosis of Dilated Cardiomyopathy

The study focuses on a common but serious heart condition called dilated cardiomyopathy (DCM), which affects millions of people worldwide. Dilated cardiomyopathy is a disease where the heart's main pumping chamber (the left ventricle) becomes enlarged and weakened, making pumping function harder for the heart to pump blood efficiently to the rest of the body. For patients with DCM, the biggest concern is the risk of "adverse prognosis"-a category includes serious outcomes like heart failure, abnormal heart rhythms, hospitalizations related to heart problems, or even death. Currently, doctors face challenges in accurately predicting which DCM patients are more likely to experience these severe outcomes, a limitation restricting the ability to provide timely, targeted care to those at highest risk.

To address the gap, the investigators aim to develop and test a "clinical prediction model"-a tool that combines medical data to predict the likelihood of adverse outcomes in DCM patients. The key innovation of the model is the use of a powerful medical imaging technique called multi-parameter stress perfusion cardiac magnetic resonance (MP stress perfusion CMR), combined with standard clinical information about patients.

First, the terms are explained in simple language. Cardiac magnetic resonance (CMR) is a safe, non-invasive imaging test that creates detailed pictures of the heart's structure and function. "Stress perfusion" means that during the CMR scan, the participant's heart is placed under a mild form of stress (usually by administration of a medication that increases heart rate, similar to light exercise) to observe blood flow through the heart muscle during increased workload. "Multi-parameter" refers to the collection of multiple types of information from the CMR scan, such as myocardial contractility, myocardial perfusion, and any damage or scarring in cardiac tissue.

The goal of the study is to use detailed CMR data, along with other basic clinical information (e.g., patient age, gender, blood pressure, and results from standard heart tests), to construct a prediction model. The model will be trained using data from hundreds of DCM patients with existing follow-up records. The investigators will analyze the records to determine which patients experienced adverse outcomes and to identify patterns in CMR and clinical data associated with those outcomes.

Once the model is constructed and tested for accuracy, the tool can be utilized by doctors in clinical practice to assist in the assessment of DCM patients. For example, when a patient is diagnosed with DCM, doctors can perform the CMR scan, input the data into the model, and obtain a personalized risk score indicating the probability of future severe cardiac events in that patient. The resulting information will support clinical decision-making: the scores will allow doctors to prioritize care for high-risk patients-such as early treatment initiation, more frequent monitoring, or medication adjustment-to prevent adverse outcomes. For lower-risk patients, the model can help avoid unnecessary, costly tests or over-treatment.