Medicina intensiva最新文献

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Harlequin syndrome: a picture speaks a thousand words 哈莱金综合症：一图胜千言。

Medicina intensiva

Pub Date : 2024-09-01 DOI: 10.1016/j.medine.2024.02.002

引用次数: 0

Right ventricular dysfunction in the critically ill. Echocardiographic evaluation 重症患者的右心室功能障碍。超声心动图评估。

Medicina intensiva

Pub Date : 2024-09-01 DOI: 10.1016/j.medine.2024.06.019

Virginia Fraile-Gutiérrez , Lluis Zapata-Fenor , Aaron Blandino-Ortiz , Manuel Guerrero-Mier , Ana Ochagavia-Calvo

Right ventricular dysfunction is common in critically ill patients, and is associated with increased mortality. Its diagnosis moreover remains challenging. In this review, we aim to outline the potential mechanisms underlying abnormal biomechanics of the right ventricle and the different injury phenotypes. A comprehensive understanding of the pathophysiology and natural history of right ventricular injury can be informative for the intensivist in the diagnosis and management of this condition, and may serve to guide individualized treatment strategies.

We describe the main recommended parameters for assessing right ventricular systolic and diastolic function. We also define how to evaluate cardiac output and pulmonary circulation pressures with echocardiography, with a focus on the diagnosis of acute cor pulmonale and relevant applications in critical disorders such as distress, septic shock, and right ventricular infarction.

右心室功能障碍在危重病人中很常见，并与死亡率增加有关。此外，其诊断仍然具有挑战性。在这篇综述中，我们旨在概述右心室生物力学异常和不同损伤表型的潜在机制。全面了解右心室损伤的病理生理学和自然史对重症监护医师诊断和处理该病症具有参考价值，并有助于指导个体化治疗策略。我们介绍了评估右心室收缩和舒张功能的主要推荐参数。我们还定义了如何用超声心动图评估心输出量和肺循环压力，重点关注急性肺心病的诊断以及在危重症（如窘迫、脓毒性休克和右心室梗死）中的相关应用。

引用次数: 0

A shattered heart: Discovery of two lethal mechanical complications. 一颗破碎的心脏发现两种致命的机械并发症

Medicina intensiva

Pub Date : 2024-08-30 DOI: 10.1016/j.medine.2024.08.001

Alberto Garrido Callén, Irene Fernández Muñoz, Marta Martín Muñoz

引用次数: 0

Incorporating the perspective of the critically ill patient: A matter of fashion or necessity? 纳入危重病人的视角：时尚还是必要？

Medicina intensiva

Pub Date : 2024-08-30 DOI: 10.1016/j.medine.2024.07.005

Olga Rubio Sanchiz , Joan Escarrabill

引用次数: 0

Application of a machine learning model for early prediction of in-hospital cardiac arrests: Retrospective observational cohort study. 应用机器学习模型对院内心脏骤停进行早期预测：回顾性观察队列研究。

Medicina intensiva

Pub Date : 2024-08-29 DOI: 10.1016/j.medine.2024.07.004

L Socias Crespí, L Gutiérrez Madroñal, M Fiorella Sarubbo, M Borges-Sa, A Serrano García, D López Ramos, C Pruenza Garcia-Hinojosa, E Martin Garijo

Objective: To describe the results of the application of a Machine Learning (ML) model to predict in-hospital cardiac arrests (ICA) 24 hours in advance in the hospital wards.

Design: Retrospective observational cohort study.

Setting: Hospital Wards.

Patients: Data were extracted from the hospital's Electronic Health Record (EHR). The resulting database contained a total of 750 records corresponding to 620 different patients (370 patients with ICA and 250 control), between may 2009 and december 2021.

Interventions: No.

Main variables of interest: As predictors of ICA, a set of 28 variables including personal history, vital signs and laboratory data was employed.

Models: For the early prediction of ICA, predictive models based on the following ML algorithms and using the mentioned variables, were developed and compared: K Nearest Neighbours, Support Vector Machine, Multilayer Perceptron, Random Forest, Gradient Boosting and Custom Ensemble of Gradient Boosting estimators (CEGB).

Experiments: Model training and evaluation was carried out using cross validation. Among metrics of performance, accuracy, specificity, sensitivity and AUC were estimated.

Results: The best performance was provided by the CEGB model, which obtained an AUC = 0.90, a specificity = 0.84 and a sensitivity = 0.81. The main variables with influence to predict ICA were level of consciousness, haemoglobin, glucose, urea, blood pressure, heart rate, creatinine, age and hypertension, among others.

Conclusions: The use of ML models could be of great support in the early detection of ICA, as the case of the CEGB model endorsed, which enabled good predictions of ICA.

目的：描述应用机器学习（ML）模型预测院内心脏骤停（ICA）的结果：描述应用机器学习（ML）模型提前 24 小时预测医院病房内心脏骤停（ICA）的结果：设计：回顾性观察队列研究：环境：医院病房：数据来自医院的电子健康记录（EHR）。由此产生的数据库包含 2009 年 5 月至 2021 年 12 月期间 620 名不同患者（370 名 ICA 患者和 250 名对照组患者）的 750 条记录：无：主要研究变量：采用一组包括个人病史、生命体征和实验室数据在内的 28 个变量作为 ICA 的预测因子：模型：为早期预测 ICA，基于以下 ML 算法并使用上述变量开发了预测模型，并进行了比较：K 近邻、支持向量机、多层感知器、随机森林、梯度提升和梯度提升估计器自定义组合（CEGB）：实验：采用交叉验证法进行模型训练和评估。在性能指标中，对准确率、特异性、灵敏度和 AUC 进行了估算：结果：CEGB 模型的性能最佳，其 AUC = 0.90，特异性 = 0.84，灵敏度 = 0.81。对预测 ICA 有影响的主要变量是意识水平、血红蛋白、葡萄糖、尿素、血压、心率、肌酐、年龄和高血压等：正如 CEGB 模型所认可的那样，使用 ML 模型对早期发现 ICA 有很大帮助，它能很好地预测 ICA。

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引用次数: 0

Removal of giant bronchial clot with cryoadhesion probe. 用低温粘附探针移除巨大的支气管血栓。

Medicina intensiva

Pub Date : 2024-08-20 DOI: 10.1016/j.medine.2024.07.012

Mónica García Simón, Cristina Sanchís Piqueras, Georgia García Fernández

引用次数: 0

Outcomes of an extracorporeal cardiopulmonary resuscitation (ECPR) program for in- and out-of-hospital cardiac arrest in a tertiary hospital in Spain 西班牙一家三甲医院针对院内和院外心脏骤停实施的体外心肺复苏（ECPR）项目的效果。

Medicina intensiva

Pub Date : 2024-08-02 DOI: 10.1016/j.medine.2024.06.021

María Martínez-Martínez , María Vidal-Burdeus , Jordi Riera , Aitor Uribarri , Elisabet Gallart , Laia Milà , Pau Torrella , Irene Buera , Luis Chiscano-Camon , Bruno García del Blanco , Carlota Vigil-Escalera , José A. Barrabés , Jordi Llaneras , Juan Carlos Ruiz-Rodríguez , Cristopher Mazo , Jorge Morales , Ricard Ferrer , Ignacio Ferreira-Gonzalez , Eduard Argudo

Objective

To analyze if the implementation of a multidisciplinary extracorporeal cardiopulmonary resuscitation (ECPR) program in a tertiary hospital in Spain is feasible and could yield survival outcomes similar to international published experiences.

Design

Retrospective observational cohort study.

Setting

One tertiary referral university hospital in Spain.

Patients

All adult patients receiving ECPR between January 2019 and April 2023.

Interventions

Prospective collection of variables and follow-up for up to 180 days.

Main variables of interest

To assess outcomes, survival with good neurological outcome defined as a Cerebral Performance Categories scale 1–2 at 180 days was used. Secondary variables were collected including demographics and comorbidities, cardiac arrest and cannulation characteristics, ROSC, ECMO-related complications, survival to ECMO decannulation, survival at Intensive Care Unit (ICU) discharge, survival at 180 days, neurological outcome, cause of death and eligibility for organ donation.

Results

Fifty-four patients received ECPR, 29 for OHCA and 25 for IHCA. Initial shockable rhythm was identified in 27 (50%) patients. The most common cause for cardiac arrest was acute coronary syndrome [29 (53.7%)] followed by pulmonary embolism [7 (13%)] and accidental hypothermia [5 (9.3%)]. Sixteen (29.6%) patients were alive at 180 days, 15 with good neurological outcome. Ten deceased patients (30.3%) became organ donors after neuroprognostication.

Conclusions

The implementation of a multidisciplinary ECPR program in an experienced Extracorporeal Membrane Oxygenation center in Spain is feasible and can lead to good survival outcomes and valid organ donors.

目的分析在西班牙一家三甲医院实施多学科体外心肺复苏（ECPR）计划是否可行，以及能否获得与国际上已发表经验相似的生存结果：设计：回顾性观察队列研究：背景：西班牙一家三级转诊大学医院：2019年1月至2023年4月期间接受ECPR的所有成年患者：前瞻性收集变量并随访长达 180 天：为了评估结果，使用了180天时神经功能结果良好的存活率，定义为脑功能分级1-2级。收集的次要变量包括人口统计学和合并症、心脏骤停和插管特征、ROSC、ECMO 相关并发症、ECMO 解除后的存活率、重症监护室（ICU）出院时的存活率、180 天时的存活率、神经系统结果、死亡原因和器官捐献资格：54 名患者接受了 ECPR，其中 29 人死于 OHCA，25 人死于 IHCA。27名患者（50%）确定了最初的可电击心律。心脏骤停最常见的原因是急性冠状动脉综合征[29例（53.7%）]，其次是肺栓塞[7例（13%）]和意外低体温[5例（9.3%）]。16名患者（29.6%）在180天后存活，其中15名患者神经功能状况良好。10名死亡患者（30.3%）在神经诊断后成为器官捐献者：结论：在西班牙经验丰富的体外膜肺氧合中心实施多学科 ECPR 项目是可行的，可获得良好的生存结果和有效的器官捐献者。

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引用次数: 0

Hemodynamic monitoring and echocardiographic evaluation in cardiogenic shock 心源性休克的血流动力学监测和超声心动图评估。

Medicina intensiva

Pub Date : 2024-08-02 DOI: 10.1016/j.medine.2024.07.001

Ana Ochagavía , Nora Palomo-López , Virginia Fraile , Luis Zapata

Cardiogenic shock (CS) is characterized by the presence of a state of tissue hypoperfusion secondary to ventricular dysfunction. Hemodynamic monitoring allows us to obtain information about cardiovascular pathophysiology that will help us make the diagnosis and guide therapy in CS situations. The most used monitoring system in CS is the pulmonary artery catheter since it provides key hemodynamic variables in CS, such as cardiac output, pulmonary artery pressure, and pulmonary artery occlusion pressure. On the other hand, echocardiography makes it possible to obtain, at the bedside, anatomical and hemodynamic data that complement the information obtained through continuous monitoring devices.

CS monitoring can be considered multimodal and integrative by including hemodynamic, metabolic, and echocardiographic parameters that allow describing the characteristics of CS and guiding therapeutic interventions during hemodynamic resuscitation.

心源性休克（CS）的特点是继发于心室功能障碍的组织低灌注状态。通过血流动力学监测，我们可以获得有关心血管病理生理学的信息，这将有助于我们在 CS 情况下做出诊断和指导治疗。CS 中最常用的监测系统是肺动脉导管，因为它能提供 CS 中的关键血液动力学变量，如心输出量、肺动脉压力和肺动脉闭塞压力。另一方面，超声心动图可在床边获得解剖和血液动力学数据，对通过连续监测设备获得的信息进行补充。CS 监测包括血液动力学、新陈代谢和超声心动图参数，可以描述 CS 的特征并指导血液动力学复苏期间的治疗干预，因此可以被视为多模式综合监测。

引用次数: 0

Ventricular tachycardia in a young female 一名年轻女性的室性心动过速。

Medicina intensiva

Pub Date : 2024-08-01 DOI: 10.1016/j.medine.2024.02.004

引用次数: 0

Muscle wasting in ICU-patients with COVID-19 - Descriptive analysis and risk factors identification COVID-19重症监护病房患者的肌肉萎缩--描述性分析和风险因素识别。

Medicina intensiva

Pub Date : 2024-08-01 DOI: 10.1016/j.medine.2024.04.010

引用次数: 0

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