The team of Professor Seung-pyo Lee of the Department of Cardiology at Seoul National University Hospital (first author, Specialist Kwak Soon-koo) revealed that myocardial fibrosis measured by T1-mapping, a new cardiac MRI technology, is an important risk factor for the long-term prognosis of patients with severe aortic stenosis. It was announced on the 23rd that the threshold value of death was identified for the first time.
Aortic stenosis is a disease in which the aortic valve on the way from the heart to the aorta does not open properly due to aging. The narrowed valves cause pressure overload on the heart, which causes the heart to thicken and progress to heart failure. Shortness of breath, chest pain, and fainting appear during exercise. In severe cases, it is a terrifying disease that can cause sudden death without notice. The prevalence is reported to be about 5% in people over 65 years of age and up to about 12% in people over 75 years of age.
The only treatment is to replace the diseased aortic valve with a new artificial valve through open thoracic surgery or percutaneous catheter replacement. Current medical guidelines recommend valve replacement if heart failure is present or if heart function is impaired even if asymptomatic.
However, in the case of patients with asymptomatic severe aortic stenosis, whether and when surgery is performed is still controversial. What are the objective indicators and biomarkers that can quantify the structural and functional damage of the heart?
The research team built a random survival forest machine learning model that predicts death using a cardiac MRI database of a total of 799 multinational patients undergoing surgery or procedures for severe aortic stenosis at 13 research centers in the UK, Europe, the US, and Canada. The database consisted of 29 clinical, echocardiographic, and cardiac MRI parameters.
As a result, important indicators in predicting the prognosis of patients with aortic valve stenosis are diffuse fibrosis (ECV%) and replacement fibrosis (LGE%), which are indicators of myocardial fibrosis, and left end-ventricular volume (LVEDVi), and right ventricular ejection fraction (RVEF), which are early indicators of heart failure. was confirmed as Left ventricular ejection fraction, which was previously known to be important, was found to be insignificant.
In the machine learning model, when the ECV% exceeded 27%, the risk of death rose sharply, increasing by 2.8 times. In addition, when the LGE% exceeded 2%, the risk of death increased steadily and increased by about 2.5 times.
These threshold values (ECV%: 27%, LGE%: 2%) were significantly predictive of death even in patients with asymptomatic aortic stenosis according to independent test data. In particular, when these cardiac MRI indicators were added to the existing risk factors in the final mortality prediction model, the predictive power of postoperative death was significantly improved.
The research team said that a clinical trial is currently underway to determine whether early valve replacement based on these indicators can improve the prognosis of patients with asymptomatic but severe aortic stenosis. It is evaluated that it has suggested the possibility of recommending surgery based on which indicators in the future when the disease is severe even if it is asymptomatic.
Professor Lee Seung-pyo said, “This study is the largest multi-national and multicenter study in which T1-mapping, a new cardiac MRI technology, is applied among aortic stenosis studies conducted worldwide. The significance of the study is significant in that it is the first to identify a risk factor and the threshold value that predicts the patient’s death from those indicators.”
Specialist Kwak Soon-koo emphasized, “It shows that these threshold values are meaningful even in patients with asymptomatic severe aortic stenosis, which is controversial about whether and when to perform surgery.”
This study was published in the latest issue of the ‘Journal of the American College of Cardiology’, a renowned journal in the field of cardiovascular medicine.
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