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Background: The diagnostic performance and clinical validity of automatic intracoronary imaging (ICI) tools for atherosclerotic plaque assessment have not been systematically investigated so far. Methods: We performed a scoping review including studies on automatic tools for automatic plaque components assessment by means of optical coherence tomography (OCT) or intravascular imaging (IVUS). We summarized study characteristics and reported the specifics and diagnostic performance of developed tools. Results: Overall, 42 OCT and 26 IVUS studies fulfilling the eligibility criteria were found, with the majority published in the last 5 years (86% of the OCT and 73% of the IVUS studies). A convolutional neural network deep-learning method was applied in 71% of OCT- and 34% of IVUS-studies. Calcium was the most frequent plaque feature analyzed (26/42 of OCT and 12/26 of IVUS studies), and both modalities showed high discriminatory performance in testing sets [range of area under the curve (AUC): 0.91-0.99 for OCT and 0.89-0.98 for IVUS]. Lipid component was investigated only in OCT studies (n = 26, AUC: 0.82-0.86). Fibrous cap thickness or thin-cap fibroatheroma were mainly investigated in OCT studies (n = 8, AUC: 0.82-0.94). Plaque burden was mainly assessed in IVUS studies (n = 15, testing set AUC reported in one study: 0.70). Conclusion: A limited number of automatic machine learning-derived tools for ICI analysis is currently available. The majority have been developed for calcium detection for either OCT or IVUS images. The reporting of the development and validation process of automated intracoronary imaging analyses is heterogeneous and lacks critical information. Systematic Review Registration: Open Science Framework (OSF), https://osf.io/nps2b/.Graphical AbstractCentral Illustration.
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Introduction: Myocardial injury markers such as high-sensitive cardiac troponin T (hs-cTnT) and creatine kinase MB (CK-MB) reflects the amount of myocardial injury with ablation. The aim of the study was to identify the value of myocardial injury markers to predict outcomes after pulmonary vein isolation (PVI) using three different ablation technologies. Methods: Consecutive patients undergoing PVI using a standard 3.5 mm irrigated-tip radiofrequency catheter (RF-group), an irrigated multielectrode radiofrequency catheter (IMEA-group) and a second-generation cryoballoon (CB-group) were analysed. Blood samples to measure injury markers were taken before and 18-24 hours after the ablation. Procedural complications were collected and standardised follow-up was performed. Logistic regression was used to identify predictors of recurrence and complications. Results: 96 patients (RF group: n=40, IMEA-group: n=17, CB-group: n=39) undergoing PVI only were analysed (82% male, age 59±10 years). After a follow-up of 12 months, atrial fibrillation (AF) recurred in 45% in the RF-group, 29% in the IMEA-group and 36% in the CB-group (p=0.492). Symptomatic pericarditis was observed in 20% of patients in the RF-group, 15% in the IMEA-group and 5% in the CB-group (p=0.131). None of the injury markers was predictive of AF recurrence or PV reconnection after a single procedure. However, hs-cTnT was identified as a predictor of symptomatic pericarditis (OR: 1.003 [1.001 to 1.005], p=0.015). Conclusion: Hs-cTnT and CK-MB were significantly elevated after PVI, irrespective of the ablation technology used. None of the myocardial injury markers were predictive for AF recurrence or PV reconnection, but hs-cTnT release predicts the occurrence of symptomatic pericarditis after PVI.
