Right Ventricular Strain as a Key Feature in Interpretable Machine Learning for Identification of Takotsubo Syndrome: A Multicenter CMR-based Study.
Authors
Affiliations (7)
Affiliations (7)
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (Z.D., H.H., C.S., J.M., Y.F., Y.H., X.C., X.G., Z.H., L.J., Y.S., J.J., C.L.); Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (Z.D., H.H., C.S., J.M., Y.F., Y.H., X.C., X.G., Z.H., L.J., Y.S., J.J., C.L.).
- Circle Cardiovascular Imaging, Calgary, AB, Canada (J.B.).
- Department of Cardiovascular Medicine, Lishui Hospital, Zhejiang University, Lishui 323000, China (L.L.).
- Department of Cardiology, Kiang Wu Hospital, Macao, Special Administrative Region of the People's Republic of China (T.C.).
- Department of Radiology, Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai 519000, China (C.P.).
- Division of Cardiology and Heart and Vascular Center, Washington University in St Louis, School of Medicine, Saint Louis (K.L.).
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (Z.D., H.H., C.S., J.M., Y.F., Y.H., X.C., X.G., Z.H., L.J., Y.S., J.J., C.L.); Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (Z.D., H.H., C.S., J.M., Y.F., Y.H., X.C., X.G., Z.H., L.J., Y.S., J.J., C.L.). Electronic address: [email protected].
Abstract
To develop an interpretable machine learning (ML) model based on cardiac magnetic resonance (CMR) multimodal parameters and clinical data to discriminate Takotsubo syndrome (TTS), acute myocardial infarction (AMI), and acute myocarditis (AM), and to further assess the diagnostic value of right ventricular (RV) strain in TTS. This study analyzed CMR and clinical data of 130 patients from three centers. Key features were selected using least absolute shrinkage and selection operator regression and random forest. Data were split into a training cohort and an internal testing cohort (ITC) in the ratio 7:3, with overfitting avoided using leave-one-out cross-validation and bootstrap methods. Nine ML models were evaluated using standard performance metrics, with Shapley additive explanations (SHAP) analysis used for model interpretation. A total of 11 key features were identified. The extreme gradient boosting model showed the best performance, with an area under the curve (AUC) value of 0.94 (95% CI: 0.85-0.97) in the ITC. Right ventricular basal circumferential strain (RVCS-basal) was the most important feature for identifying TTS. Its absolute value was significantly higher in TTS patients than in AMI and AM patients (-9.93%, -5.21%, and -6.18%, respectively, p < 0.001), with values above -6.55% contributing to a diagnosis of TTS. This study developed an interpretable ternary classification ML model for identifying TTS and used SHAP analysis to elucidate the significant value of RVCS-basal in TTS diagnosis. An online calculator (https://lsszxyy.shinyapps.io/XGboost/) based on this model was developed to provide immediate decision support for clinical use.