Medicina Intensiva最新文献

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Junta Directiva de la SEMICYUC, Comité Local y Comité Científico SEMICYUC 董事会、地方委员会和科学委员会

IF 3 4区医学 Q2 CRITICAL CARE MEDICINE

Medicina Intensiva

Pub Date : 2024-05-01 DOI: 10.1016/S0210-5691(24)00195-5

引用次数: 0

Pósteres Orales. Infección/Antibióticos I. Patología Digestiva Crítica. Organización/Gestión/Calidad I. Neurointensivismo I. Donación/Trasplantes. Cirugía Cardiaca I 口头海报。感染/抗生素 I.重症消化病理学组织/管理/质量 I.神经紧张症 I.捐献/移植心脏外科 I

IF 3 4区医学 Q2 CRITICAL CARE MEDICINE

Medicina Intensiva

Pub Date : 2024-05-01 DOI: 10.1016/S0210-5691(24)00197-9

引用次数: 0

Comunicaciones Orales. Comunicaciones a Premio 口头交流。获奖论文

IF 3 4区医学 Q2 CRITICAL CARE MEDICINE

Medicina Intensiva

Pub Date : 2024-05-01 DOI: 10.1016/S0210-5691(24)00199-2

引用次数: 0

Índice de autores 作者索引

IF 3 4区医学 Q2 CRITICAL CARE MEDICINE

Medicina Intensiva

Pub Date : 2024-05-01 DOI: 10.1016/S0210-5691(24)00210-9

引用次数: 0

Comunicaciones Orales. Neurointensivismo/Traumatismos/ Sedación/Analgesia/Simulación 口头交流。神经过敏/创伤/镇静/镇痛/模拟

IF 3 4区医学 Q2 CRITICAL CARE MEDICINE

Medicina Intensiva

Pub Date : 2024-05-01 DOI: 10.1016/S0210-5691(24)00205-5

引用次数: 0

Pósteres Orales. Sepsis/Fracaso Multiorgánico II. Sedación/Analgesia III. Simulación/Nuevas Tecnologías. Urgencias/Emergencias III. Hematología III. Organización/Gestión/Calidad VI 口头海报。败血症/多器官功能衰竭 II.镇静/镇痛 III.模拟/新技术。急诊/紧急情况 III.血液学 III.组织/管理/质量 VI.

IF 3 4区医学 Q2 CRITICAL CARE MEDICINE

Medicina Intensiva

Pub Date : 2024-05-01 DOI: 10.1016/S0210-5691(24)00209-2

引用次数: 0

Pósteres Pantalla. Cirugía Cardiaca II. Respiratorio I. Síndrome Coronario Agudo/Monitorización Hemodinámica/Cirugía Cardiaca. Neurointensivismo II. Infección/Antibióticos II. Organización/Gestión/Calidad II. Hematología I. Urgencias/Emergencias I. 海报展示。心脏外科 II.呼吸系统 I.急性冠状动脉综合征/血流动力学监护/心脏手术。神经紧张症 II.感染/抗生素 II.组织/管理/质量 II.血液学 I.急诊/急救 I.

IF 3 4区医学 Q2 CRITICAL CARE MEDICINE

Medicina Intensiva

Pub Date : 2024-05-01 DOI: 10.1016/S0210-5691(24)00198-0

引用次数: 0

Pósteres Pantalla. Marcapasos/Arritmias I. Infección/Antibióticos VI. Síndrome Coronario Agudo II. Infección/Antibióticos VII. Organización/Gestión/Calidad IV. Sedación/Analgesia II. Urgencias/Emergencias. Cardiovascular II. Trasplantes I. Ventilación 海报展示。起搏器/心律失常 I.感染/抗生素 VI.急性冠状动脉综合征 II.感染/抗生素 VII.组织/管理/质量 IV.镇静/镇痛 II.紧急情况/紧急情况。心血管 II.移植 I.通气

IF 3 4区医学 Q2 CRITICAL CARE MEDICINE

Medicina Intensiva

Pub Date : 2024-05-01 DOI: 10.1016/S0210-5691(24)00203-1

引用次数: 0

Sepsis death risk factor score based on systemic inflammatory response syndrome, quick sequential organ failure assessment, and comorbidities 基于全身炎症反应综合征、快速序贯器官衰竭评估和合并症的脓毒症死亡风险因素评分

IF 3 4区医学 Q2 CRITICAL CARE MEDICINE

Medicina Intensiva

Pub Date : 2024-04-26 DOI: 10.1016/j.medin.2024.02.010

Vinicius Nakad Orsatti, Victoria Stadler Tasca Ribeiro, Carolina de Oliveira Montenegro, Clarice Juski Costa, Eduardo Albanske Raboni, Eduardo Ramos Sampaio, Fernando Michielin, Juliano Gasparetto, João Paulo Telles, Felipe Francisco Tuon

Objective

In this study, we aimed to evaluate the death risk factors of patients included in the sepsis protocol bundle, using clinical data from qSOFA, SIRS, and comorbidities, as well as development of a mortality risk score.

Design

This retrospective cohort study was conducted between 2016 and 2021.

Setting

Two university hospitals in Brazil.

Participants

Patients with sepsis.

Interventions

Several clinical and laboratory data were collected focused on SIRS, qSOFA, and comorbidities.

Main variable of interest

In-hospital mortality was the primary outcome variable. A mortality risk score was developed after logistic regression analysis.

Results

A total of 1,808 patients were included with a death rate of 36%. Ten variables remained independent factors related to death in multivariate analysis: temperature ≥38 °C (odds ratio [OR] = 0.65), previous sepsis (OR = 1.42), qSOFA ≥ 2 (OR = 1.43), leukocytes >12,000 or <4,000 cells/mm³ (OR = 1.61), encephalic vascular accident (OR = 1.88), age >60 years (OR = 1.93), cancer (OR = 2.2), length of hospital stay before sepsis >7 days (OR = 2.22,), dialysis (OR = 2.51), and cirrhosis (OR = 3.97). Considering the equation of the binary regression logistic analysis, the score presented an area under curve of 0.668, is not a potential model for death prediction.

Conclusions

Several risk factors are independently associated with mortality, allowing the development of a prediction score based on qSOFA, SIRS, and comorbidities data, however, the performance of this score is low.

目标在这项研究中，我们旨在利用 qSOFA、SIRS 和合并症的临床数据，评估脓毒症方案捆绑包中患者的死亡风险因素，并制定死亡风险评分。主要关注变量院内死亡率是主要结局变量。结果共纳入 1,808 名患者，死亡率为 36%。在多变量分析中，有十个变量仍然是与死亡相关的独立因素：体温≥38 °C（比值比 [OR] = 0.65）、既往败血症（OR = 1.42）、qSOFA ≥ 2（OR = 1.43）、白细胞>12,000 或 <4,000个/mm3（OR = 1.61）、脑血管意外（OR=1.88）、年龄>60岁（OR=1.93）、癌症（OR=2.2）、败血症前住院时间>7天（OR=2.22）、透析（OR=2.51）和肝硬化（OR=3.97）。考虑到二元回归逻辑分析的方程，该评分的曲线下面积为 0.668，不是一个潜在的死亡预测模型。结论多个风险因素与死亡率独立相关，因此可以根据 qSOFA、SIRS 和合并症数据制定预测评分，但该评分的性能较低。

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引用次数: 0

Current perspectives on the use of artificial intelligence in critical patient safety. 人工智能在危重病人安全中的应用现状。

IF 3 4区医学 Q2 CRITICAL CARE MEDICINE

Medicina Intensiva

Pub Date : 2024-04-26 DOI: 10.1016/j.medine.2024.04.002

Jesús Abelardo Barea Mendoza, Marcos Valiente Fernandez, Alex Pardo Fernandez, Josep Gómez Álvarez

Intensive Care Units (ICUs) have undergone enhancements in patient safety, and artificial intelligence (AI) emerges as a disruptive technology offering novel opportunities. While the published evidence is limited and presents methodological issues, certain areas show promise, such as decision support systems, detection of adverse events, and prescription error identification. The application of AI in safety may pursue predictive or diagnostic objectives. Implementing AI-based systems necessitates procedures to ensure secure assistance, addressing challenges including trust in such systems, biases, data quality, scalability, and ethical and confidentiality considerations. The development and application of AI demand thorough testing, encompassing retrospective data assessments, real-time validation with prospective cohorts, and efficacy demonstration in clinical trials. Algorithmic transparency and explainability are essential, with active involvement of clinical professionals being crucial in the implementation process.

重症监护病房（ICU）的患者安全得到了加强，而人工智能（AI）作为一种颠覆性技术的出现则提供了新的机遇。虽然已发表的证据有限，且存在方法论问题，但某些领域仍大有可为，如决策支持系统、不良事件检测和处方错误识别。人工智能在安全领域的应用可以实现预测或诊断目标。要实施基于人工智能的系统，就必须制定确保安全协助的程序，应对包括对此类系统的信任、偏差、数据质量、可扩展性以及道德和保密考虑在内的挑战。人工智能的开发和应用需要全面的测试，包括回顾性数据评估、前瞻性队列的实时验证以及临床试验中的疗效展示。算法的透明度和可解释性至关重要，临床专业人员的积极参与对实施过程至关重要。

引用次数: 0

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