Short-term prognostic predictive value of deep-learning assisted quantitative myocardial contrast echocardiography in ST-elevated myocardial infarction after primary percutaneous coronary intervention
10.3760/cma.j.cn131148-20230109-00010
- VernacularTitle:基于深度学习的心肌声学造影定量分析预测ST段抬高型心肌梗死患者经皮冠状动脉介入术后短期预后
- Author:
Mingqi LI
1
;
Dewen ZENG
;
Wenyue YUAN
;
Yanxiang ZHOU
;
Jinling CHEN
;
Sheng CAO
;
Hongning SONG
;
Bo HU
;
Jing CHEN
;
Yuanting YANG
;
Hao WANG
;
Hongwen FEI
;
Qing ZHOU
Author Information
1. 武汉大学人民医院超声影像科，武汉 430060
- Keywords:
Myocardial contrast echocardiography;
ST-segment elevation myocardial infarction;
Deep neural network;
Coronary microvascular dysfunction;
Prognosis
- From:
Chinese Journal of Ultrasonography
2023;32(7):572-582
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the prognostic predictive value of deep neural network (DNN) assisted myocardial contrast echocardiography (MCE) quantitative analysis of ST-elevated myocardial infarction (STEMI) patients after successful percutaneous coronary intervention(PCI).Methods:A retrospective analysis was performed in 97 STEMI patients with thrombolysis in myocardial infarction-3 flow in infarct vessel after primary PCI in Renmin Hospital of Wuhan University from June to November 2021. MCE was performed within 48 h after PCI. Patients were followed up to 120 days. The adverse events were defined as cardiac death, hospitalization for congestive heart failure, reinfarction, stroke and recurrent angina. The framework consisted of the U-net and hierarchical convolutional LSTMs. The plateau myocardial contrast intensity (A), micro-bubble rate constant (β), and microvascular blood flow (MBF) for all myocardial segments were obtained by the framework, and then underwent variability analysis. Patients were divided into low MBF group and high MBF group based on MBF values, the baseline characteristics and adverse events were compared between the two groups. Other variables included biomarkers, ventricular wall motion analysis, MCE qualitative analysis, and left ventricular ejection fraction. The relationship between various variables and prognosis was investigated using Cox regression analysis. The ROC curve was plotted to evaluate the diagnostic efficacy of the models, and the diagnostic efficacy of the models was compared using the integrated discrimination improvement index (IDI).Results:The time-cost for processing all 3 810 frames from 97 patients was 377 s. 92.89% and 7.11% of the frames were evaluated by an experienced echocardiographer as "good segmentation" and "correction needed". The correlation coefficients of A, β, and MBF ranged from 0.97 to 0.99 for intra-observer and inter-observer variability. During follow-up, 20 patients met the adverse events. Multivariate Cox regression analysis showed that for each increase of 1 IU/s in MBF of the infarct-related artery territory, the risk of adverse events decreased by 6% ( HR 0.94, 95% CI =0.91-0.98). There was a 4.5-fold increased risk of adverse events in the low MBF group ( HR 5.50, 95% CI=1.55-19.49). After incorporating DNN-assisted MCE quantitative analysis into qualitative analysis, the IDI for prognostic prediction was 15% (AUC 0.86, sensitivity 0.78, specificity 0.73). Conclusions:MBF of the area supplied by infarct-related artery after STEMI-PCI is an independent protective factor for short-term prognosis. The DNN-assisted MCE quantitative analysis is an objective, efficient, and reproducible method to evaluate microvascular perfusion. Assessment of culprit-MBF after PCI in STEMI patients adds independent short-term prognostic information over qualitative analysis.It has the potential to be a valuable tool for risk stratification and clinical follow-up.
