Xiaomei Zhu

Aims: As a novel technique, left bundle branch area pacing (LBBAP) can achieve excellent resynchronization in heart failure (HF) patients with left bundle branch block (LBBB). This study used cardiovascular magnetic resonance (CMR) to evaluate the scar burden at the lead deployment site and its ability to predict the echocardiographic response. Methods and results: Eighty‐seven HF patients with LBBB and a left ventricular ejection fraction (LVEF)≤ 35% who underwent CMR examination and successful LBBAP implantation were retrospectively enrolled. Based on the results of the 6‐month echocardiographic assessment after LBBAP, a response was defined as a reduction in the left ventricular end‐systolic volume (LVESV) of ≥ 15%, and a super‐response was defined as an improvement in the LVEF of ≥ 20% or greater than 50%. The myocardial scar, including global, septum, free wall, lateral and scar around lead deployment sites, was assessed by CMR. LBBAP achieved an 86.2% response rate and a 50.6% super‐response rate. Compared with other LGE parameters, a superior negative linear correlation was found between the scar load around lead deployment site and LVESV reduction and improvement in LVEF, indicating that for every 1% increase in the scar burden of the lead deployment site, the delta LVESV decreased by 0.94%, and the delta LVEF decreased by 0.43%. The scar around lead deployment site can strongly predict responders and super‐responders [area under the receiver operating characteristic curve (AUC: 0.87 and 0.76), respectively]. In patients with scars around deployment sites > 12.3% (cut‐off values based on the Youden's index), the rates of response and super‐response decreased substantially to 56.0% and 16.0%, respectively. Conclusion: The scar burden around lead deployment site as assessed by CMR, which is commonly located around the mid anteroseptum, can predict the echocardiographic response. Assessment of myocardial late gadolinium enhancement in the lead deployment site is recommended.

Background: The necessity of thrombolytic therapy before percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients remains controversial, requiring further evaluation of potential benefits. Purpose: To explore the efficacy of half-dose recombinant staphylokinase (r-SAK) intravenous bolus before PCI in STEMI patients. Material and Methods: Patients with STEMI were allocated to r-SAK or normal saline groups before PCI. Cardiac magnetic resonance (CMR) at 5 days after MI evaluated cardiac function, myocardial tissue characteristics, and strain. Segments were classified by late gadolinium enhancement (LGE) extent. Results: A total of 64 STEMI patients were divided into the r-SAK group (n = 33) and NS group (n = 31). Patients in the r-SAK group had a significantly higher left ventricular ejection fraction and cardiac output index ( P = 0.045 and 0.024). There was no significant difference between the two groups in mapping parameters, infarct size, area at risk, or the incidence of microvascular obstruction (MVO) and intramyocardial hemorrhage (IMH) (all P >0.05). Regardless of the extent of LGE in the segments, patients in the r-SAK group exhibited significantly better segmental longitudinal strain (all P P = 0.044). Conclusion: For STEMI patients expected to undergo PCI within 120 min of presentation, a single bolus of half-dose r-SAK administrated before PCI improved short-term cardiac function without increasing incidence of MVO or IMH.

The long-term effects of early thrombolytic therapy administered within 120 minutes of the first medical contact and before primary percutaneous coronary intervention (PCI) on left ventricular (LV) and left atrial (LA) function in ST-segment elevation myocardial infarction (STEMI) patients remain unknown. This study aimed to explore the long-term effects of early recombinant staphylokinase (r-SAK) thrombolysis administered up to 120 minutes before primary PCI on the LV and LA function of STEMI patients using cardiac magnetic resonance (CMR) imaging. This retrospective study analyzed the one-year follow-up CMR imaging data of STEMI patients enrolled in the multi-center, prospective OPTIMA-5 trial, conducted from November 2021 to August 2022. Patients were randomized to receive either a single half-dose of 5 mg r-SAK or normal saline (NS) before primary PCI. LV and LA functional parameters were compared between groups using appropriate statistical tests. A total of 66 patients were included (r-SAK, n=32; NS, n=34). Compared to the NS group, the r-SAK group had a significantly higher cardiac index (2.77 vs. 2.41 L/min/m², P=0.018), LV stroke volume index (42.18 vs. 37.77 mL/m², P=0.029), LV wall thickening (49.86% vs. 44.36%, P2, P=0.004], LA passive ejection fraction (27.34% vs. 20.68%, P=0.007), passive strain (21.80% vs. 17.05%, P=0.045), and peak early negative strain rate (−1.80 vs. −1.50 1/s, P=0.043). The early administration of r-SAK thrombolysis before primary PCI significantly improves long-term LV and LA function in STEMI patients, suggesting a potential strategy for enhancing cardiac recovery.

A deep learning model enabled automated CT myocardial blood flow quantification and accurate evaluation of ischemic myocardial volume, which was an independent predictor for major adverse cardiovascular events and mildly improved cardiovascular risk stratification.

Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiographs in patients with heart failure is currently unclear. The aim of our study is to develop and validate a deep learning survival prediction model using chest X-ray (DLSPCXR) in patients with HF. The study retrospectively enrolled a cohort of 353 patients with HF who underwent chest X-ray (CXR) at our institution between March 2012 and March 2017. The dataset was randomly divided into training (n = 247) and validation (n = 106) datasets. Univariate and multivariate Cox analysis were conducted on the training dataset to develop clinical and imaging survival prediction models. The DLSPCXR was trained and the selected clinical parameters were incorporated into DLSPCXR to establish a new model called DLSPinteg. Discrimination performance was evaluated using the time-dependent area under the receiver operating characteristic curves (TD AUC) at 1, 3, and 5-years survival. Delong’s test was employed for the comparison of differences between two AUCs of different models. The risk-discrimination capability of the optimal model was evaluated by the Kaplan–Meier curve. In multivariable Cox analysis, older age, higher N-terminal pro-B-type natriuretic peptide (NT-ProBNP), systolic pulmonary artery pressure (sPAP) > 50 mmHg, New York Heart Association (NYHA) functional class III–IV and cardiothoracic ratio (CTR) ≥ 0.62 in CXR were independent predictors of poor prognosis in patients with HF. Based on the receiver operating characteristic (ROC) curve analysis, DLSPCXR had better performance at predicting 5-year survival than the imaging Cox model in the validation cohort (AUC: 0.757 vs. 0.561, P = 0.01). DLSPinteg as the optimal model outperforms the clinical Cox model (AUC: 0.826 vs. 0.633, P = 0.03), imaging Cox model (AUC: 0.826 vs. 0.555, P < 0.001), and DLSPCXR (AUC: 0.826 vs. 0.767, P = 0.06). Deep learning models using chest radiographs can predict survival in patients with heart failure with acceptable accuracy.

Background: Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiographs (DLSPCR) in patients with heart failure is currently unclear. Objective: To develop and validate a deep learning survival prediction model using chest radiographs (DLSPCR) in patients with HF. Methods: The study retrospectively enrolled a cohort of 353 patients with HF who underwent chest radiographs (CR) at our institution between March 2012 and March 2017. The dataset was randomly divided into training (n=247) and validation (n=106) datasets. Univariate and multivariate Cox analysis were applied to the training dataset to develop clinical and imaging survival prediction models. The DLSPCR algorithm was trained and the selected clinical parameters were incorporated into DLSPCR to establish a new model called DLSPinteg. Discrimination performance was evaluated by using the time-dependent area under the receiver operating characteristic curves (TD AUC) at 1, 3, and 5-year survival. Delong’s test was employed for the comparison of difference between two AUCs of different models. The risk-discrimination capability of the optimal model was evaluated by Kaplan-Meier curve. Results: In multivariable Cox analysis, older age, higher N-terminal pro-B-type natriuretic peptide (NT-ProBNP), systolic pulmonary artery pressure (SPAP) > 50 mmHg, New York Heart Association (NYHA) functional class III-IV and cardiothoracic ratio (CTR) ≥ 0.62 in CR were independent predictors of poor prognosis in patients with HF. As the receiver operating characteristic (ROC) curve analysis showed, DLSPCR had better performance at predicting 5-year survival than the imaging Cox model in the validation cohort (AUC: 0.757 vs 0.561, P=0.01). DLSPinteg as the optimal model outperforms clinical Cox model (AUC: 0.826 vs 0.633, P=0.03), imaging Cox model (AUC: 0.826 vs 0.555, P<0.001), and DLSPCR (AUC: 0.826 vs 0.767, P=0.06). Conclusions: Deep learning models using chest radiographs can predict survival in patients with heart failure with acceptable accuracy.