Canadian Journal of Cardiology最新文献

Background: Coronary microvascular dysfunction (CMD) is associated with poor prognosis in patients with dilated cardiomyopathy with reduced ejection fraction (DCMrEF). However, its assessment remains challenging in routine clinical practice. This study aims to explore the potential value of Angiography-derived microcirculatory resistance (AMR) in predicting clinical outcomes in DCMrEF patients.

Methods: This multicenter retrospective study included DCMrEF patients (2019-2024) with a 5-year MACE endpoint. AMR was calculated for all major arteries. Its prognostic value was assessed by Kaplan-Meier and multivariate Cox regression. A subgroup analysis was conducted to evaluate the impact of vericiguat treatment on clinical outcomes according to patients' baseline AMR levels.

Results: A total of 531 eligible patients with DCMrEF were enrolled in the study, 204 of whom had endpoint events. The optimal AMR cutoffs were >2.295 for the left anterior descending (LAD) artery (AUC=0.776) and left circumflex (LCX) artery, (AUC=0.761), and >2.5 for the right coronary artery (RCA) (AUC=0.745), all with P < 0.001. Patients were then classified into the higher AMR group and the lower AMR group. Kaplan-Meier and multivariate Cox analyses confirmed higher AMR was independently associated with increased MACE risk. LAD-AMR significantly improved risk reclassification over traditional factors (NRI=0.015, IDI=0.051; both P<0.001). Subgroup analysis revealed vericiguat benefitted only patients with elevated AMR.

Conclusions: AMR may be a powerful independent predictor of poor prognosis in DCMrEF patients, with LAD-AMR showing potential for greater predictive value. Vericiguat may represent a potential precision-targeted agent for the high risk of heart failure mediated by microcirculatory dysfunction.

Background: Indications for pre-emptive aortic surgery are based on ascending aortic diameter. This study aims to assess the diagnostic test properties of novel unadjusted, adjusted, and combined measures of aortic geometry.

Methods: This study comprised an international multicentre analysis of patients undergoing contrast-enhanced computed tomography angiography (CTA) before ATAAD. A historical cohort of non-aneurysmal patients and patients with known aneurysmal disease (TAA) were included as a control group. Receiver operating characteristics (ROC) curves were applied to evaluate the aortic measures' diagnostic accuracy (for [un]adjusted aortic diameter, length, and volume), with sensitivity analyses performed in a matched sample. Clinically intuitive measures such as the number needed to reclassify (NNR) to identify an additional patient at risk of ATAAD were calculated.

Results: Eighty patients underwent CTA before ATAAD occurred, in five centres in the Netherlands and Germany. The control group encompassed 333 patients. The specificity of all measures was 98.8%, 97.0%, and 94.9% at contemporary diameter thresholds of 55mm, 52mm, and 50mm. The sensitivity of diameter, length, or volume was 4.1%, 6.8%, and 14.9% at the 98.8% specificity-threshold (NNR volume versus diameter: 9.3, p=0.008). The combination of diameter, length, and volume as a new criterion resulted in an increased sensitivity at the 55mm and 52mm thresholds (18.9%, NNR=6.8, and 23.0%, NNR=9.3, p=0.001 and p=0.008, respectively). Results were consistent in matched samples.

Conclusion: These newly introduced aortic measures seem promising to identify patients at risk of ATAAD, but their net benefit needs to be validated in real-world cohorts.

Background: This study aimed to develop and validate a dynamic prediction model for acute kidney injury (AKI) in heart failure (HF) patients.

Methods: Using data from 7,636 HF patients in the MIMIC-IV v3.1 database, we constructed a Long Short-Term Memory (LSTM) model with dynamic focal loss to handle class imbalance. The study designed two prediction perspectives: short-term prediction utilized different data collection windows (6 to 72 hours) to dynamically predict the risk of AKI occurrence within subsequent specific time windows (12 to 72 hours); long-term prediction used data from specific time points after admission (12 to 72 hours) to predict the occurrence of AKI during the entire hospitalization.

Results: The model demonstrated robust performance across all prediction tasks (AUC range: 0.80-0.94). Analysis of prediction lead time showed that the model could provide early warnings: the median lead times for predicting AKI occurrence within 12, 24, 48, and 72 hours were 9.57, 12.89, 18.77, and 27.25 hours, respectively. Feature importance analysis revealed that urine output, Sequential Organ Failure Assessment score (SOFA score), and systolic blood pressure played dominant roles in short-term prediction, while TroponinT and history of cardiovascular surgery were more important in long-term prediction.

Conclusions: The LSTM-based model proposed in this study captures dynamic physiological changes in HF patients and provides dynamic risk assessments for AKI with sufficient lead time.