JACC. Heart failure最新文献

Background: The lack of automated tools for measuring care quality limits the implementation of a national program to assess guideline-directed care in heart failure with reduced ejection fraction (HFrEF).

Objectives: The authors aimed to automate the identification of patients with HFrEF at hospital discharge, an opportunity to evaluate and improve the quality of care.

Methods: The authors developed a novel deep-learning language model for identifying patients with HFrEF from discharge summaries of hospitalizations with heart failure at Yale New Haven Hospital during 2015 to 2019. HFrEF was defined by left ventricular ejection fraction <40% on antecedent echocardiography. The authors externally validated the model at Northwestern Medicine, community hospitals of Yale, and the MIMIC-III (Medical Information Mart for Intensive Care III) database.

Results: A total of 13,251 notes from 5,392 unique individuals (age 73 ± 14 years, 48% women), including 2,487 patients with HFrEF (46.1%), were used for model development (train/held-out: 70%/30%). The model achieved an area under receiver-operating characteristic curve (AUROC) of 0.97 and area under precision recall curve (AUPRC) of 0.97 in detecting HFrEF on the held-out set. The model had high performance in identifying HFrEF with AUROC = 0.94 and AUPRC = 0.91 on 19,242 notes from Northwestern Medicine, AUROC = 0.95 and AUPRC = 0.96 on 139 manually abstracted notes from Yale community hospitals, and AUROC = 0.91 and AUPRC = 0.92 on 146 manually reviewed notes from MIMIC-III. Model-based predictions of HFrEF corresponded to a net reclassification improvement of 60.2% ± 1.9% compared with diagnosis codes (P < 0.001).

Conclusions: The authors developed a language model that identifies HFrEF from clinical notes with high precision and accuracy, representing a key element in automating quality assessment for individuals with HFrEF.

Gene therapy has emerged as a possible treatment for progressive, debilitating Mendelian cardiomyopathies with limited therapeutic options. This paper arises from discussions at the 2023 Cardiovascular Clinical Trialists Forum and highlights several challenges relevant to gene therapy clinical trials, including low prevalence and high phenotypic heterogeneity of Mendelian cardiomyopathies, outcome selection complexities and resulting regulatory uncertainty, and immune responses to the adeno-associated viral vectors that are being used in ongoing studies. Avenues to address these challenges such as natural history studies, external controls, novel regulatory pathways, and immunosuppression are discussed. Relevant cases of recent therapy approvals are highlighted. Ultimately, this work aims to broadly frame discussions on and provide potential future avenues for clinical trial design for rare cardiomyopathy gene therapies.

Background: Donor-recipient heart size matching is crucial in heart transplantation; however, the often-used predicted heart mass (PHM) ratio may be inaccurate in the setting of obesity.

Objectives: In this study, the authors sought to investigate the association between echocardiographically measured donor left ventricular mass (LVM) for heart size matching and the risk of the primary 1-year composite outcome of death or retransplantation.

Methods: The Donor Heart Study was a prospective, multicenter, observational cohort study that collected echocardiograms from brain-dead donors. The measured LVM ratio (donor measured LVM/recipient predicted LVM) was defined as the exposure variable, and the association with the primary outcome was analyzed with Cox proportional hazard modeling. Secondary analyses evaluated the association of the PHM and predicted LVM (donor predicted LVM/recipient predicted LVM) ratios with the primary outcome.

Results: In 2,015 heart transplants, the measured LVM ratio demonstrated that undersized matches (<0.80) had a 47% higher risk (adjusted HR [aHR]: 1.47; 95% CI: 1.01-2.15) and oversized (>1.20) matches had a 58% increased risk (aHR: 1.58; 95% CI: 1.05-2.37) of the 1-year composite outcome compared with ideally matched transplants. However, the PHM and predicted LVM ratios were not associated with the primary outcome. Nonlinear modeling demonstrated a U-shaped relationship between the measured LVM ratio and composite outcome. The measured LVM ratio had superior predictive power for poor post-transplantation outcomes in obese recipients.

Conclusions: Measuring donor LVM with the use of echocardiography may provide a more accurate method for donor-recipient heart size matching that could improve heart transplant outcomes, especially in obese recipients.