Prognotic Role of CMR in Takotsubo Syndrome

Machine Learning, Magnetic Resonance Imaging, Takotsubo Cardiomyopathy

The primary objective of this observational registry is to develop a comprehensive clinical and imaging score (incorporating echocardiography and cardiac magnetic resonance data) that enhances risk stratification for patients with Takotsubo syndrome. The secondary objectives of this registry are as follows: Investigate the diagnostic value of cardiac magnetic resonance parameters in predicting in-hospital and long-term outcomes in patients with Takotsubo syndrome. Compare the proposed risk stratification score for patients with Takotsubo syndrome with previously existing scores. Investigate the contribution of machine learning models in predicting in-hospital and long-term outcomes compared to standard clinical scores. The design and rationale of this registry are available at 10.1097/RTI.0000000000000709

The prognosis of Takotsubo syndrome patients remains contentious, necessitating improved risk stratification for better management. While various clinical characteristics and parameters from transthoracic echocardiography have been associated with outcomes, none of the existing predictive scores incorporate cardiac magnetic resonance imaging (CMR) data, despite its ability to noninvasively assess tissue characterization. CMR offers a comprehensive evaluation of functional and structural changes, including an accurate assessment of right ventricular function. While CMR has been extensively studied for diagnostic purposes in Takotsubo syndrome, its role in prognosis is still debated. Emerging technologies like computed tomography show promise in myocardial characterization but lack robust investigation in prognostic roles. The EVOLUTION registry aims to address this gap by incorporating CMR parameters into a risk stratification score alongside clinical and transthoracic echocardiography data, with machine learning models also explored for enhanced outcome prediction. This initiative seeks to provide a more reliable predictive tool for the optimized management of Takotsubo syndrome patients. The main objective of this study is to enhance risk assessment in Takotsubo syndrome patients by incorporating CMR data alongside demographic, clinical, and transthoracic echocardiography parameters. Specifically, the aim is to analyze CMR data and their association with both short-term and long-term patient outcomes. Additionally, the effectiveness of the proposed risk stratification score for Takotsubo syndrome patients will be evaluated in comparison to existing scoring systems. Moreover, all available CMR, transthoracic echocardiography, and clinical variables will be utilized to develop machine learning models for predictive analysis

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