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Background Takayasu arteritis is a chronic inflammatory disease involving the aorta and its major branches. Acute myocardial infarction rarely but not so much presents in patients with Takayasu arteritis, and the preferable revascularization strategy is still under debate. Case presentation A 22-year-old female with Takayasu arteritis presented with acute myocardial infarction. Coronary angiography and intravenous ultrasound (IVUS) showed that the right coronary artery (RCA) was occluded and that there was severe negative remodelling at the ostium of the left main coronary artery (LMCA). The patient was treated by primary percutaneous transluminal coronary angioplasty (PTCA) with a scoring balloon in the LMCA, without stent implantation. After 3 months of immunosuppressive medication, the patient received RCA revascularization by stenting. There was progressive external elastic membrane (EEM) enlargement of the LMCA ostium demonstrated by IVUS at 3 and 15 months post-initial PTCA. Conclusion Here, we report a case of Takayasu arteritis with involvement of the coronary artery ostium. Through PTCA and long-term immunosuppressive medication, we found that coronary negative remodelling might be reversible in patients with Takayasu arteritis.

Background A number of models have been reported for predicting atrial fibrillation (AF) recurrence after catheter ablation. Although many machine learning (ML) models were developed among them, black-box effect existed widely. It was always difficult to explain how variables affect model output. We sought to implement an explainable ML model and then reveal its decision-making process in identifying patients with paroxysmal AF at high risk for recurrence after catheter ablation. Methods Between January 2018 and December 2020, 471 consecutive patients with paroxysmal AF who had their first catheter ablation procedure were retrospectively enrolled. Patients were randomly assigned into training cohort (70%) and testing cohort (30%). The explainable ML model based on Random Forest (RF) algorithm was developed and modified on training cohort, and tested on testing cohort. In order to gain insight into the association between observed values and model output, Shapley additive explanations (SHAP) analysis was used to visualize the ML model. Results In this cohort, 135 patients experienced tachycardias recurrences. With hyperparameters adjusted, the ML model predicted AF recurrence with an area under the curve of 66.7% in the testing cohort. Summary plots listed the top 15 features in descending order and preliminary showed the association between features and outcome prediction. Early recurrence of AF showed the most positive impact on model output. Dependence plots combined with force plots showed the impact of single feature on model output, and helped determine high risk cut-off points. The thresholds of CHA2DS2-VASc score, systolic blood pressure, AF duration, HAS-BLED score, left atrial diameter and age were 2, 130 mmHg, 48 months, 2, 40 mm and 70 years, respectively. Decision plot recognized significant outliers. Conclusion An explainable ML model effectively revealed its decision-making process in identifying patients with paroxysmal atrial fibrillation at high risk for recurrence after catheter ablation by listing important features, showing the impact of every feature on model output, determining appropriate thresholds and identifying significant outliers. Physicians can combine model output, visualization of model and clinical experience to make better decision.