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Electrocardiogram (ECG) is a widely used technique for diagnosing cardiovascular disease. The widespread emergence of smart ECG devices has sparked the demand for intelligent single-lead ECG-based diagnostic systems. However, it is challenging to develop a single-lead-based ECG interpretation model for multiple disease diagnosis due to the lack of some key disease information. We aim to improve the diagnostic capabilities of single-lead ECG for multi-label disease classification in a new teacher-student manner, where the teacher trained by multi-lead ECG educates a student who observes only single-lead ECG We present a new disease-aware Contrastive Lead-information Transferring (CLT) to improve the mutual disease information between the single-lead-based ECG interpretation model and multi-lead-based ECG interpretation model. Moreover, We modify the traditional Knowledge Distillation into Multi-label disease Knowledge Distillation (MKD) to make it applicable for multi-label disease diagnosis. The whole knowledge transferring process is inter-lead Multi-View Knowledge Transferring of ECG (MVKT-ECG). By employing the training strategy, we can effectively transfer comprehensive disease knowledge from various views of ECG, such as the 12-lead ECG, to a single-lead-based ECG interpretation model. This enables the model to extract intricate details from single-lead ECG signals and enhances the model's capability of diagnosing and identifying single-lead signals. Extensive experiments on two commonly used public multi-label datasets, ICBEB2018 and PTB-XL demonstrate that our MVKT-ECG yields exceptional diagnostic performance improvements for single-lead ECG. The student outperforms its baseline observably on the PTB-XL dataset (1.3 % on PTB.super, and 1.4 % on PTB.sub), and on ICBEB2018 dataset (3.2 %).
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Background: The effect of 3D-printed bioresorbable vascular scaffolds (BRS) in coronary heart disease has not been clarified. Aims: We aimed to compare the safety and efficacy of 3D-printed BRS with that of metallic sirolimus-eluting stents (SES). Methods: Thirty-two BRS and 32 SES were implanted into 64 porcine coronary arteries. Quantitative coronary angiography (QCA) and optical coherence tomography (OCT) were performed at 14, 28, 97, and 189 days post-implantation. Scanning electron microscopy (SEM) and histopathological analyses were performed at each assessment. Results: All stents/scaffolds were successfully implanted. All animals survived for the duration of the study. QCA showed the two devices had a similar stent/scaffold-to-artery ratio and acute percent recoil. OCT showed the lumen area (LA) and scaffold/stent area (SA) of the BRS were significantly smaller than those of the SES at 14 and 28 days post-implantation (14-day LA: BRS vs SES 4.52±0.41 mm2 vs 5.69±1.11 mm2; p=0.03; 14-day SA: BRS vs SES 4.99±0.45 mm2 vs 6.11±1.06 mm2; p=0.03; 28-day LA: BRS vs SES 2.93±1.03 mm2 vs 4.82±0.74 mm2; p=0.003; 28-day SA: BRS vs SES 3.86±0.98 mm2 vs 5.75±0.71 mm2; p=0.03). Both the LA and SA of the BRS increased over time and were similar to those of the SES at the 97-day and 189-day assessments. SEM and histomorphological analyses showed no significant between-group differences in endothelialisation at each assessment. Conclusions: The novel 3D-printed BRS showed safety and efficacy similar to that of SES in a porcine model. The BRS also showed a long-term positive remodelling effect.
RESUMEN
OBJECTIVE: To examine the prognostic value of HRV measurements during anesthesia for postoperative clinical outcomes prediction using machine learning models. DATA SOURCES: VitalDB, a comprehensive database of 6388 surgical patients admitted to Seoul National University Hospital. ELIGIBILITY CRITERIA FOR STUDY SELECTION: Cases with ECG lead II recording duration of less than one hour were excluded. Cases with more than 20% of missing HRV measurements were also excluded. A total of 5641 cases were eligible for the analyses. METHODS: Six machine learning models including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Trees (GBT), Extreme Gradient Boosting (XGB), and an ensemble of the five baseline models were developed to predict postoperative clinical outcomes. The prediction models were trained using only clinical information, and using both clinical information and HRV features, respectively. Feature importance based on the SHAP method was used to assess the contribution of the HRV measurements to the outcome predictions. Subgroup analysis was also performed to evaluate the risk association between postoperative ICU stay and various HRV measurements such as heart rate, low-frequency power (LFP), and short-term fluctuation DFA [Formula: see text]. RESULT: The final cohort included 5641 unique cases, among whom 4678 (83.0%) cases had ages over 40, 2877 (51.0%) were male, 1073 (19.0%) stayed in ICU after surgery, 52 (0.9%) suffered in-hospital death, and 3167(56.1%) had a total length of hospital stay longer than 7 days. In the final test set, the highest AUROC performance with only clinical information was 0.79 for postoperative ICU stay, 0.58 for in-hospital mortality, and 0.76 for the total length of hospital stay prediction. Importantly, using both clinical information and HRV features, the AUROC performance was 0.83, 0.70, and 0.76 for the three clinical outcome predictions, respectively. Subgroup analysis found that patients with an average heart rate higher than 70, low-frequency power (LFP) < 33, and short-term fluctuation DFA [Formula: see text] < 0.95 during anesthesia, had a significantly higher risk of entering the ICU after surgery. CONCLUSION: This study suggested that HRV measurements during anesthesia are feasible and effective for predicting postoperative clinical outcomes.