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Adverse Pregnancy Outcomes and Long-Term Risk of Heart Failure in Women: National Cohort and Co-Sibling Study.
IF 10.3 1区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-07 DOI: 10.1016/j.jchf.2024.11.004
Casey Crump, Jan Sundquist, Kristina Sundquist

Background: Adverse pregnancy outcomes, such as preterm delivery and hypertensive disorders of pregnancy, may be associated with higher future risks of heart failure (HF). However, the comparative effects of different adverse pregnancy outcomes on long-term risk of HF, and their potential causality, are unclear.

Objectives: The authors sought to examine 5 major adverse pregnancy outcomes in relation to long-term risk of HF in a large population-based cohort.

Methods: A national cohort study was conducted of all 2,201,638 women with a singleton delivery in Sweden in 1973-2015, followed up for HF identified from nationwide outpatient and inpatient diagnoses through 2018. Cox regression was used to compute HRs for HF associated with preterm delivery, small for gestational age, preeclampsia, other hypertensive disorders of pregnancy, and gestational diabetes, while adjusting for other adverse pregnancy outcomes and maternal factors. Co-sibling analyses assessed for potential confounding by shared familial (genetic or environmental) factors.

Results: In 48 million person-years of follow-up, 667,774 women (30%) experienced an adverse pregnancy outcome, and 19,922 women (0.9%) were diagnosed with HF (median age, 61 years). All 5 adverse pregnancy outcomes were independently associated with long-term increased risk of HF. With up to 46 years of follow-up after delivery, adjusted HRs for HF associated with specific adverse pregnancy outcomes were: gestational diabetes, 2.19 (95% CI: 1.95-2.45); preterm delivery, 1.68 (95% CI: 1.61-1.75); other hypertensive disorders, 1.68 (95% CI: 1.48-1.90); preeclampsia, 1.59 (95% CI: 1.53-1.66); and small for gestational age, 1.35 (95% CI: 1.31-1.40). All HRs remained significantly elevated (1.3- to 3.0-fold) even 30 to 46 years after delivery. These findings were only partially explained by shared familial factors. Women with multiple adverse pregnancy outcomes had further increases in risk (eg, up to 46 years after delivery, adjusted HRs associated with 1, 2, or ≥3 adverse pregnancy outcomes were 1.51 [95% CI: 1.47-1.56], 2.31 [95% CI: 2.19-2.45], and 3.18 [95% CI: 2.85-3.56], respectively).

Conclusions: In this large national cohort, women who experienced any of 5 major adverse pregnancy outcomes had increased risk for HF up to 46 years later. Women with adverse pregnancy outcomes need early preventive actions and long-term clinical care to reduce the risk of HF.

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引用次数: 0
Digital Solutions for the Optimization of Pharmacologic Therapy for Heart Failure. 优化心力衰竭药物治疗的数字化解决方案。
IF 10.3 1区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-03 DOI: 10.1016/j.jchf.2024.10.014
Adam D DeVore, Mary Norine Walsh, Orly Vardeny, Nancy M Albert, Akshay S Desai

Data from large-scale, randomized, controlled trials demonstrate that contemporary treatments for heart failure (HF) can substantially improve morbidity and mortality. Despite this, observed outcomes for patients living with HF are poor, and they have not improved over time. The are many potential reasons for this important problem, but inadequate use of optimal medical therapy for patients with HF, an important component of guideline-directed medical therapy, in routine practice is a principal and modifiable contributor. In this state-of-the-art review, we focus on digital interventions that specifically target the rapid initiation and titration of medical therapy for HF, typically not involving face-to-face encounters. Early data suggest that digital interventions that use data collected outside of structured episodes of care can facilitate initiation and titration of guideline-directed medical therapy for patients with HF. More data are necessary, however, to understand the safety and efficacy of these interventions compared with current care models. In addition, specific efforts by key constituents are necessary to generate sufficient data on the effectiveness and sustainability of digital interventions in routine practice and to ensure that they do not exacerbate existing disparities in care.

来自大规模、随机、对照试验的数据表明,心力衰竭(HF)的现代治疗可以显著改善发病率和死亡率。尽管如此,观察到的心衰患者的预后很差,而且没有随着时间的推移而改善。这一重要问题有许多潜在的原因,但在常规实践中,对心衰患者使用最佳药物治疗的不足是主要的和可改变的因素,这是指导药物治疗的重要组成部分。在这篇最新的综述中,我们专注于数字干预,专门针对心衰药物治疗的快速启动和滴定,通常不涉及面对面的接触。早期数据表明,使用结构化护理事件之外收集的数据的数字干预措施可以促进心衰患者开始和滴定指南指导的药物治疗。然而,与目前的护理模式相比,需要更多的数据来了解这些干预措施的安全性和有效性。此外,需要关键组成部分做出具体努力,以生成关于数字干预措施在日常实践中的有效性和可持续性的充分数据,并确保它们不会加剧现有的护理差距。
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引用次数: 0
Automated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing 利用基于深度学习的自然语言处理技术自动识别射血分数降低的心力衰竭。
IF 10.3 1区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-01 DOI: 10.1016/j.jchf.2024.08.012
Arash A. Nargesi MD, MPH , Philip Adejumo BS , Lovedeep Singh Dhingra MD , Benjamin Rosand BS , Astrid Hengartner BS , Andreas Coppi BS , Simon Benigeri BA, MS , Sounok Sen MD , Tariq Ahmad MD, MPH , Girish N. Nadkarni MD, MPH , Zhenqiu Lin PhD , Faraz S. Ahmad MD, MS , Harlan M. Krumholz MD, SM , Rohan Khera MD, MS

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.
背景:由于缺乏衡量医疗质量的自动化工具,限制了射血分数减低型心力衰竭指导性医疗评估国家计划的实施:由于缺乏衡量护理质量的自动化工具,限制了射血分数减低型心力衰竭(HFrEF)指导性护理评估国家计划的实施:作者旨在自动识别出院时的射血分数降低型心力衰竭(HFrEF)患者,为评估和改善护理质量提供机会:作者开发了一种新型深度学习语言模型,用于从耶鲁纽黑文医院 2015 年至 2019 年期间的心衰住院患者出院摘要中识别 HFrEF 患者。HFrEF根据左心室射血分数定义 结果:模型开发共使用了来自 5392 名独特个体(年龄 73 ± 14 岁,48% 为女性)的 13251 份记录,其中包括 2487 名 HFrEF 患者(46.1%)(训练/暂停:70%/30%)。该模型在检测保留组中的 HFrEF 时,接收者工作特征曲线下面积 (AUROC) 为 0.97,精确召回曲线下面积 (AUPRC) 为 0.97。该模型在识别 HFrEF 方面表现出色,在西北医学中心的 19,242 份病历中,AUROC = 0.94,AUPRC = 0.91;在耶鲁大学社区医院的 139 份人工摘录病历中,AUROC = 0.95,AUPRC = 0.96;在 MIMIC-III 的 146 份人工审核病历中,AUROC = 0.91,AUPRC = 0.92。与诊断代码相比,基于模型的 HFrEF 预测净重分类率提高了 60.2 ± 1.9%(P < 0.001):作者开发了一种语言模型,能从临床笔记中高精度、高准确性地识别出 HFrEF,是实现 HFrEF 患者质量评估自动化的关键因素。
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引用次数: 0
Challenges in Cardiomyopathy Gene Therapy Clinical Trial Design 心肌病基因疗法临床试验设计的挑战。
IF 10.3 1区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-01 DOI: 10.1016/j.jchf.2024.08.024
Tejus Satish MD , Kimberly N. Hong MD, MHSA , Juan Pablo Kaski MD , Barry H. Greenberg MD
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.
基因疗法已成为治疗进展性、衰弱性孟德尔型心肌病的一种可能疗法,但其治疗方案有限。本文源自 2023 年心血管临床试验医师论坛的讨论,重点介绍了与基因疗法临床试验相关的几项挑战,包括孟德尔型心肌病的低患病率和高表型异质性、结果选择的复杂性和由此导致的监管不确定性,以及对正在进行的研究中使用的腺相关病毒载体的免疫反应。本文讨论了应对这些挑战的途径,如自然史研究、外部对照、新型调节途径和免疫抑制。还重点介绍了最近批准的相关治疗案例。最终,这项工作旨在为罕见心肌病基因疗法的临床试验设计提供广泛的讨论框架和潜在的未来途径。
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引用次数: 0
Optimizing the Posthospital Period After Admission for Worsening Heart Failure 优化因心力衰竭恶化入院后的后期治疗。
IF 10.3 1区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-01 DOI: 10.1016/j.jchf.2024.09.010
Giuseppe M.C. Rosano MD , Gianluigi Savarese MD, PhD , Michael Böhm MD, PhD , John R. Teerlink MD
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引用次数: 0
“Pharmacoequity” in Heart Failure Treatment 心力衰竭治疗中的“药物公平”:每个人的权利,而不仅仅是少数人的权利。
IF 10.3 1区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-01 DOI: 10.1016/j.jchf.2024.10.015
Ikeoluwapo Kendra Bolakale-Rufai MD, MS , Khadijah Breathett MD, MS
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引用次数: 0
From Better Models to Better Care 从更好的模型到更好的护理。
IF 10.3 1区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-01 DOI: 10.1016/j.jchf.2024.09.021
Nigam H. Shah MBBS, PhD , Sneha S. Jain MD, MBA
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引用次数: 0
Laying a Foundation for 4 Pillars of In-Hospital Guideline-Directed Medical Therapy Optimization in Patients With Heart Failure With Reduced Ejection Fraction 为心力衰竭伴射血分数降低患者院内指导药物治疗优化的4个支柱奠定基础
IF 10.3 1区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-01 DOI: 10.1016/j.jchf.2024.11.001
Nancy M. Albert PhD, APRN, CCNS , J. Bradley Williams PharmD
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引用次数: 0
The Association of Echocardiographically Measured Donor Left Ventricular Mass and 1-Year Outcomes After Heart Transplantation 超声心动图测量的供体左心室质量与心脏移植术后 1 年预后的关系
IF 10.3 1区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-01 DOI: 10.1016/j.jchf.2024.10.001
Christian O’Donnell MD , Natalie Tapaskar MD , Pablo A. Sanchez MD , Brian Wayda MD , Everton J. Santana MSc , Rafael C. Pulgrossi BS , Kirsten Steffner MD , Shiqi Zhang MS , Yingjie Weng PhD , Louise Y. Sun MD, SM , Darren Malinoski MD , Jonathan Zaroff MD , Francois Haddad MD , Kiran K. Khush MD, MAS

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.
背景:在心脏移植手术中,供体与受体的心脏大小匹配至关重要:供体-受体心脏大小匹配在心脏移植中至关重要;然而,经常使用的预测心脏质量(PHM)比值在肥胖的情况下可能并不准确:在这项研究中,作者试图调查用于心脏大小匹配的超声心动图测量的供体左心室质量(LVM)与1年主要综合结果(死亡或再移植)风险之间的关系:捐献者心脏研究是一项前瞻性、多中心、观察性队列研究,收集了脑死亡捐献者的超声心动图。测量的左心室容积比(捐献者测量的左心室容积/受者预测的左心室容积)被定义为暴露变量,其与主要结果的关系采用 Cox 比例危险模型进行分析。次要分析评估了 PHM 和预测 LVM(供体预测 LVM/受体预测 LVM)比率与主要结果的关系:结果:在 2,015 例心脏移植中,测量的 LVM 比值显示,与匹配度理想的移植相比,匹配度不足(1.20)的移植出现 1 年综合结果的风险增加了 58%(aHR:1.58;95% CI:1.05-2.37)。然而,PHM 和预测 LVM 比率与主要结果无关。非线性模型显示,测量的左心室容积比与综合结果之间呈 U 型关系。测量的左心室容积比对肥胖受者移植后不良预后的预测能力更强:结论:使用超声心动图测量供体左心室容积可为供体和受体心脏大小匹配提供更准确的方法,从而改善心脏移植的预后,尤其是肥胖受体的预后。
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引用次数: 0
National Trends in Heart Failure Hospitalizations and Readmissions From 2010 to 2021 2010 年至 2021 年全国心力衰竭住院和再入院趋势。
IF 10.3 1区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2025-01-01 DOI: 10.1016/j.jchf.2024.08.016
Manyoo A. Agarwal MD , Gregg C. Fonarow MD , Boback Ziaeian MD, PhD
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引用次数: 0
期刊
JACC. Heart failure
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