Using ultrasound radiomics to forecast adverse cardiovascular events in patients with acute coronary syndrome after percutaneous coronary intervention.

OBJECTIVE: Exploring the performance of ultrasound-based radiomics in forecasting major adverse cardiovascular events (MACE) within 1 year following percutaneous coronary intervention (PCI) of acute coronary syndrome (ACS) patients.

METHODS: In this research, 161 ACS patients who underwent PCI were included (114 patients were randomly assigned to the training set and 47 patients to the validation set). Every patient received echocardiography 3-7 days after PCI and followed up for 1 year. The radiomics features related to MACE occurrence were extracted and selected to formulate the RAD score. Building ultrasound personalized model by incorporating RAD score, LVEF, LVGLS, and NT-ProBNP. The model's capacity to predict was tested using ROC curves.

RESULTS: Multifactorial logistic regression analysis of RAD score with clinical data and echocardiographic parameters indicated RAD score and LVGLS as independent risk factors for the occurrence of MACE. The RAD score predicted MACE, with AUC values of 0.85 and 0.86 in the training and validation sets. The ultrasound personalized model had a superior ability to predict the occurrence of MACE, with AUC values of 0.88 and 0.92, which were higher than those of the clinical model (with AUC of 0.72 and 0.80) without RAD score (Z = 3.711, 2.043, P < .001, P = .041). Furthermore, DCA indicated that the ultrasound personalization model presented a more favorable net clinical benefit.

CONCLUSIONS: Ultrasound radiomics can be a reliable tool to predict the incidence of MACE after PCI in patients with ACS and provides quantifiable data for personalized clinical treatment.