Revista española de cardiología (English ed.)最新文献

英文中文

Does the amount and intensity of physical activity matter for low-density lipoprotein oxidation? 运动量和运动强度对低密度脂蛋白氧化有影响吗？

IF 7.2 2区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

Revista española de cardiología (English ed.)

Pub Date : 2024-07-08 DOI: 10.1016/j.rec.2024.06.005

Roger Benet-Pozo, Montserrat Fitó, Jaume Marrugat, Cosme García-García, Isaac Subirana, Roberto Elosua

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Impact of an integrated cardiology-intensive care medicine model on mortality in STEMI. 心脏病学-重症监护医学综合模式对 STEMI 死亡率的影响。

IF 7.2 2区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

Revista española de cardiología (English ed.)

Pub Date : 2024-07-08 DOI: 10.1016/j.rec.2024.05.012

Javier Lorenzo González, Marcos Farráis Villalba, Cristina López Ferraz, Sergio Huertas Nieto, Mar Martín Velasco, Julio Hernández Afonso

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Correction in the article by de Juan Bagudá et al. “Repetitive ambulatory levosimendan as a bridge to heart transplantation”, Rev Esp Cardiol. 2024;77:290-301 de Juan Bagudá等人的文章 "重复性非卧床左西孟旦作为心脏移植的桥梁 "中的更正，Rev Esp Cardiol.2024;77:290-301.

IF 7.2 2区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

Revista española de cardiología (English ed.)

Pub Date : 2024-07-03 DOI: 10.1016/j.rec.2024.05.010

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In recognition of the Spanish origin of CRISPR/Cas9. Implications for the treatment of familial heart disease 纪念 CRISPR/Cas9 的西班牙起源。对治疗家族性心脏病的意义。

IF 7.2 2区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

Revista española de cardiología (English ed.)

Pub Date : 2024-07-01 DOI: 10.1016/j.rec.2024.01.007

María Sabater Molina , Juan Ramón Gimeno Blanes

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Rest in PEACE? 安息？

IF 7.2 2区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

Revista española de cardiología (English ed.)

Pub Date : 2024-07-01 DOI: 10.1016/j.rec.2024.01.004

Raban V. Jeger, Gregor Fahrni

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Cardiovascular magnetic resonance in the working diagnosis of MINOCA: the sooner, the better? 心血管磁共振在 MINOCA 工作诊断中的应用：越早越好？

IF 7.2 2区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

Revista española de cardiología (English ed.)

Pub Date : 2024-07-01 DOI: 10.1016/j.rec.2023.12.013

Rocío Párraga , Carlos Real , Rodrigo Fernández-Jiménez

引用次数: 0

Machine learning methods for developing a predictive model of the incidence of delirium in cardiac intensive care units 用机器学习方法开发心脏重症监护病房谵妄发生率的预测模型。

IF 7.2 2区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

Revista española de cardiología (English ed.)

Pub Date : 2024-07-01 DOI: 10.1016/j.rec.2023.12.007

Ryoung-Eun Ko , Jihye Lee , Sungeun Kim , Joong Hyun Ahn , Soo Jin Na , Jeong Hoon Yang

Introduction and objectives

Delirium, recognized as a crucial prognostic factor in the cardiac intensive care unit (CICU), has evolved in response to the changing demographics among critically ill cardiac patients. This study aimed to create a predictive model for delirium for patients in the CICU.

Methods

This study included consecutive patients admitted to the CICU of the Samsung Medical Center. To assess the candidate variables for the model: we applied the following machine learning methods: random forest, extreme gradient boosting, partial least squares, and Plmnet-elastic.net. After selecting relevant variables, we performed a logistic regression analysis to derive the model formula. Internal validation was conducted using 100-repeated hold-out validation.

Results

We analyzed 2774 patients, 677 (24.4%) of whom developed delirium in the CICU. Machine learning-based models showed good predictive performance. Clinically significant and frequently important predictors were selected to construct a delirium prediction scoring model for CICU patients. The model included albumin level, international normalized ratio, blood urea nitrogen, white blood cell count, C-reactive protein level, age, heart rate, and mechanical ventilation. The model had an area under the receiver operating characteristics curve (AUROC) of 0.861 (95%CI, 0.843-0.879). Similar results were obtained in internal validation with 100-repeated cross-validation (AUROC, 0.854; 95%CI, 0.826-0.883).

Conclusions

Using variables frequently ranked as highly important in four machine learning methods, we created a novel delirium prediction model. This model could serve as a useful and simple tool for risk stratification for the occurrence of delirium at the patient's bedside in the CICU.

引言和目的：谵妄被认为是心脏重症监护病房（CICU）的一个重要预后因素，随着重症心脏病患者人口统计学的变化而不断发展。本研究旨在为 CICU 患者建立一个谵妄预测模型：本研究包括三星医疗中心 CICU 的连续入院患者。为了评估模型的候选变量，我们采用了以下机器学习方法：随机森林、极梯度提升、偏最小二乘法和 Plmnet-elastic.net。选定相关变量后，我们进行了逻辑回归分析，得出了模型公式。内部验证采用 100 次重复保持验证：我们分析了2774名患者，其中677人（24.4%）在CICU中出现了谵妄。基于机器学习的模型显示出良好的预测性能。我们选择了具有临床意义且经常出现的重要预测因子，为 CICU 患者构建了谵妄预测评分模型。该模型包括白蛋白水平、国际标准化比率、血尿素氮、白细胞计数、C反应蛋白水平、年龄、心率和机械通气。该模型的接收者操作特征曲线下面积 (AUROC) 为 0.861（95%CI，0.843-0.879）。通过 100 次重复交叉验证的内部验证也得到了类似的结果（AUROC，0.854；95%CI，0.826-0.883）：利用四种机器学习方法中经常被列为高度重要的变量，我们创建了一个新的谵妄预测模型。该模型可作为一种有用而简单的工具，用于对 CICU 患者床旁发生谵妄的风险进行分层。

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引用次数: 0

Leandro Plaza Celemín Leandro Plaza Celemín.

IF 7.2 2区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

Revista española de cardiología (English ed.)

Pub Date : 2024-07-01 DOI: 10.1016/j.rec.2024.04.010

Miguel Ángel García Fernández , Luis Rodríguez Padial

引用次数: 0

Impact of prior use of antiplatelets on COVID-19 susceptibility, progression, and severity: a population-based study 先前使用抗血小板对 COVID-19 易感性、进展和严重程度的影响：一项基于人群的研究

IF 7.2 2区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

Revista española de cardiología (English ed.)

Pub Date : 2024-07-01 DOI: 10.1016/j.rec.2023.12.004

Ángela Prieto-Campo , Maruxa Zapata-Cachafeiro , Manuel Portela-Romero , María Piñeiro-Lamas , Adolfo Figueiras , Ángel Salgado-Barreira

Introduction and objectives

Hypercoagulability and thromboembolism are processes that arise from severe acute respiratory syndrome coronavirus 2 infection and are responsible for a high degree of coronavirus disease 2019 (COVID-19)-related morbidity and mortality. This study sought to assess the effect of antiplatelet drugs on COVID-19 severity (risk of hospitalization and mortality), susceptibility to severe acute respiratory syndrome coronavirus 2 infection, and progression to severe COVID-19.

Methods

We conducted a population-based case-control study in a northwestern region of Spain in 2020. The study involved 3060 participants with a positive polymerase chain reaction test who were hospitalized, 26 757 participants with a positive polymerase chain reaction test who were not hospitalized, and 56 785 healthy controls.

Results

Triflusal seemed to be associated with a significant increase in risk of hospitalization (aOR, 1.97; 95%CI, 1.27-3.04) and susceptibility to infection (OR, 1.45; 95%CI, 1.07-1.96). It also appeared to lead to a nonsignificant increase in the risk of mortality (OR, 2.23; 95%CI, 0.89-5.55) and/or progression to more severe disease stages (OR, 1.42; 95%CI, 0.8-2.51). Aspirin seemed to be associated with a statistically significant decrease in susceptibility to severe acute respiratory syndrome coronavirus 2 infection (OR, 0.92; 95%CI, 0.86-0.98).

Conclusions

Triflusal use appears to increase the risk of susceptibility to COVID-19 infection and an even higher risk of hospitalization, whereas the other antiplatelets could be associated with a reduction in the risk of the various outcomes or have no effect on risk. These findings could support reconsideration of triflusal prescription in COVID-19 pandemic situations.

引言和目的高凝状态和血栓栓塞是严重急性呼吸综合征冠状病毒2感染引起的过程，也是冠状病毒2019病（COVID-19）相关发病率和死亡率居高不下的原因。本研究旨在评估抗血小板药物对 COVID-19 严重程度（住院和死亡风险）、严重急性呼吸综合征冠状病毒 2 感染易感性以及进展为严重 COVID-19 的影响。方法我们于 2020 年在西班牙西北部地区开展了一项基于人群的病例对照研究。结果三氟草胺似乎与住院风险（aOR，1.97；95%CI，1.27-3.04）和感染易感性（OR，1.45；95%CI，1.07-1.96）的显著增加有关。阿司匹林似乎也会导致死亡风险的非显著性增加（OR，2.23；95%CI，0.89-5.55）和/或疾病发展到更严重阶段（OR，1.42；95%CI，0.8-2.51）。阿司匹林似乎与严重急性呼吸系统综合征冠状病毒 2 感染的易感性显著降低有关（OR，0.92；95%CI，0.86-0.98）。结论使用三氟片似乎会增加 COVID-19 感染的易感性风险，甚至会增加住院风险，而其他抗血小板药物可能会降低各种结果的风险或对风险没有影响。这些发现可支持在COVID-19大流行的情况下重新考虑三氟沙星的处方。

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引用次数: 0

ChatGPT-4 versus human assessment in cardiology peer review 心脏病学同行评审中的 ChatGPT-4 与人工评估。

IF 7.2 2区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

Revista española de cardiología (English ed.)

Pub Date : 2024-07-01 DOI: 10.1016/j.rec.2024.02.004

Agustín Fernández-Cisnal , Pablo Avanzas , David Filgueiras-Rama , Pablo Garcia-Pavia , Laura Sanchis , Juan Sanchis

引用次数: 0

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