Journal of intensive medicine最新文献_第5页

The association of fluid balance with traumatic brain injury outcomes: A systematic review 液体平衡与外伤性脑损伤结局的关系：系统综述

Journal of intensive medicine

Pub Date : 2025-07-01 DOI: 10.1016/j.jointm.2025.01.002

Antonis Kalakoutas , Ashley Thomas , Thomas Fisher , Bethany Lane

Background

Fluid balance management is critical in moderate and severe traumatic brain injury (TBI) due to impaired cerebrovascular autoregulation. This study systematically reviews the association of fluid volume management with outcomes in moderate to severe TBI.

Methods

We conducted a systematic literature search on MEDLINE, EMBASE, CINAHL, The Cochrane Database, and bibliographies of included articles. Studies assessing fluid volume management and outcomes in moderate/severe TBI patients were included. Risk of bias, publication bias, and heterogeneity were comprehensively assessed. Primary outcomes were short/long-term mortality and neurological outcomes. Secondary outcomes included the effect on intracranial pressure, development of acute kidney injury (AKI), refractory intracranial hypertension (RIH), pulmonary edema/acute respiratory distress syndrome, length of stay, and length of mechanical ventilation. Fluid balance groups were categorized into restrictive, euvolemic, and liberal.

Results

Out of 2668 studies identified, 12 studies (seven observational and five randomized controlled trials [RCTs]) involving 9184 TBI patients were included. Euvolemic fluid balance was associated with lower odds of mortality compared to restrictive (odds ratio [OR] = 0.39, 95% confidence interval [CI]: 0.27 to 0.57, P <0.00001) and liberal groups (OR=0.47, 95% CI: 0.31 to 0.70, P=0.0003), and improved odds of favorable neurological outcomes compared to restrictive (OR=2.51, 95% CI: 1.72 to 3.66, P <0.00001) and liberal groups (OR=1.86, 95% CI: 1.18 to 2.92, P=0.007). Euvolemic balance also reduced the odds of AKI and RIH, and shortened the mean length of mechanical ventilation compared to liberal fluid balance groups but not the restrictive ones.

Conclusions

Euvolemic fluid balance may improve key outcomes in TBI patients, including reduced mortality and better neurological outcomes. These findings underscore the need for RCTs to further assess euvolemic fluid management protocols in neurocritical care and their potential to inform clinical guidelines.

背景：由于脑血管自身调节功能受损，中重度创伤性脑损伤（TBI）患者的体液平衡管理至关重要。本研究系统地回顾了液体容量管理与中重度脑外伤预后的关系。方法对MEDLINE、EMBASE、CINAHL、Cochrane数据库进行系统文献检索，并纳入文献参考书目。研究评估了中/重度脑外伤患者的液量管理和预后。对偏倚风险、发表偏倚风险和异质性进行综合评估。主要结局是短期/长期死亡率和神经预后。次要结局包括对颅内压的影响、急性肾损伤（AKI）的发展、难治性颅内高压（RIH）、肺水肿/急性呼吸窘迫综合征、住院时间和机械通气时间。体液平衡组分为限制性组、大容量组和自由组。结果在2668项研究中，纳入了12项研究（7项观察性试验和5项随机对照试验[rct]），涉及9184例TBI患者。与限制性组（比值比[OR] = 0.39, 95%可信区间[CI]: 0.27至0.57,P <0.00001）和自由组（OR=0.47, 95% CI: 0.31至0.70，P=0.0003）相比，euvolic体液平衡与较低的死亡率相关，与限制性组（OR=2.51, 95% CI: 1.72至3.66,P <0.00001）和自由组（OR=1.86, 95% CI: 1.18至2.92，P=0.007）相比，神经系统预后良好的几率更高。与自由体液平衡组相比，体液平衡组也降低了AKI和RIH的几率，缩短了机械通气的平均时间，但与限制组相比没有。结论血容液平衡可改善TBI患者的关键预后，包括降低死亡率和改善神经系统预后。这些发现强调了rct进一步评估神经危重症护理中容血液管理方案及其为临床指南提供信息的潜力的必要性。

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引用次数: 0

Deep learning integration of chest computed tomography and plasma proteomics to identify novel aspects of severe COVID-19 pneumonia 深度学习整合胸部计算机断层扫描和血浆蛋白质组学，以识别COVID-19重症肺炎的新方面

Journal of intensive medicine

Pub Date : 2025-07-01 DOI: 10.1016/j.jointm.2024.11.001

Yucai Hong , Lin Chen , Yang Yu , Ziyue Zhao , Ronghua Wu , Rui Gong , Yandong Cheng , Lingmin Yuan , Shaojun Zheng , Cheng Zheng , Ronghai Lin , Jianping Chen , Kangwei Sun , Ping Xu , Li Ye , Chaoting Han , Xihao Zhou , Yaqing Liu , Jianhua Yu , Yaqin Zheng , Zhongheng Zhang

Background

Heterogeneity is a critical characteristic of severe coronavirus disease 2019 (COVID-19) pneumonia. Integrating chest computed tomography (CT) imaging and plasma proteomics holds the potential to elucidate Image-Expression Axes (IEAs) that can effectively address this disease heterogeneity.

Methods

A cohort of subjects diagnosed with severe COVID-19 pneumonia at 12 participating hospitals between December 2022 and March 2023 was prospectively screened for eligibility. Context-aware self-supervised representation learning (CSRL) was employed to extract intricate features from CT images. Quantification of plasma proteins was achieved using the Olink® inflammation panel. A deep learning model was meticulously trained, with CSRL features serving as input and the proteomic data as the target. This trained model facilitated the construction of IEAs, offering a representation of the underlying disease heterogeneity. The potential of these IEAs for prognostic and predictive enrichment was subsequently explored via conventional regression models.

Results

The study cohort comprised 1979 eligible patients, who were stratified into a training set of 630 individuals and a testing set of 1349 individuals. Three distinct IEAs were identified: IEA1 was correlated with shock conditions, IEA2 was associated with the systemic inflammatory response syndrome (SIRS), and IEA3 was reflective of the coagulation profile. Notably, IEA1 (odds ratio [OR]= 0.52, 95 % confidence interval [CI]: 0.40 to 0.67, P < 0.001) and IEA2 (OR=0.74, 95 % CI: 0.62 to 0.90, P=0.002) exhibited significant associations with the risk of mortality. Intriguingly, patients characterized by lower IEA1 values (<-2, indicative of more severe shock) demonstrated a reduced mortality risk when administered with steroids. Conversely, patients with higher IEA2 values seemed to benefit from a judicious approach to fluid infusion.

Conclusions

Our comprehensive approach, seamlessly integrating advanced deep learning techniques, proteomic profiling, and clinical data, has unraveled intricate interdependencies between IEAs, protein abundance patterns, therapeutic interventions, and ultimate patient outcomes in the context of severe COVID-19 pneumonia. These discoveries make a significant contribution to the rapidly advancing field of precision medicine, paving the way for tailored therapeutic strategies that can significantly impact patient care.

异质性是2019年严重冠状病毒病（COVID-19）肺炎的关键特征。整合胸部计算机断层扫描（CT）成像和血浆蛋白质组学具有阐明图像表达轴（IEAs）的潜力，可以有效地解决这种疾病的异质性。方法前瞻性筛选2022年12月至2023年3月期间在12家参与医院诊断为COVID-19重症肺炎的受试者。采用上下文感知自监督表示学习（CSRL）从CT图像中提取复杂特征。使用Olink®炎症面板实现血浆蛋白定量。以CSRL特征作为输入，以蛋白质组学数据为目标，精心训练深度学习模型。这个经过训练的模型促进了IEAs的构建，提供了潜在疾病异质性的表示。随后通过传统回归模型探索了这些IEAs在预测和预测富集方面的潜力。结果研究队列包括1979名符合条件的患者，他们被分为630名训练组和1349名测试组。确定了三种不同的iea: IEA1与休克状况相关，IEA2与全身炎症反应综合征（SIRS）相关，IEA3反映凝血状况。值得注意的是，IEA1(优势比[OR]= 0.52, 95 %置信区间[CI]: 0.40至0.67,P <；0.001)和IEA2 （OR=0.74, 95 % CI: 0.62 ~ 0.90， P=0.002）与死亡风险显著相关。有趣的是，IEA1值较低（<-2，表明休克更严重）的患者在服用类固醇后死亡风险降低。相反，较高IEA2值的患者似乎受益于明智的输液方法。我们的综合方法无缝整合了先进的深度学习技术、蛋白质组学分析和临床数据，揭示了COVID-19重症肺炎背景下IEAs、蛋白质丰度模式、治疗干预和最终患者结局之间复杂的相互依赖关系。这些发现为快速发展的精准医学领域做出了重大贡献，为定制治疗策略铺平了道路，可以显著影响患者护理。

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引用次数: 0

Anemia as a potent marker of in-hospital mortality in patients admitted to the cardiac intensive care unit: Data from the Critical Care Cardiology Trials Network (CCCTN) Registry 贫血是心脏重症监护病房入院患者住院死亡率的一个有效标志：来自重症监护心脏病学试验网络（CCCTN）注册的数据

Journal of intensive medicine

Pub Date : 2025-07-01 DOI: 10.1016/j.jointm.2024.12.006

Christine Chow , Rasheed Durowoju , Carlos Alviar , Gregory W Barsness , Howard A Cooper , Lori B Daniels , Xuan Ding , Shahab Ghafghazi , Umesh Gidwani , Michael Goldfarb , Dustin Hillerson , Jason N Katz , Paul Marano , Jeong-Gun Park , Matthew Pierce , Scott W Rose , Robert O Roswell , Sean van Diepen , Anjali Wagle , Erin A Bohula , Younghoon Kwon

Background

Anemia is common in critically ill patients and is associated with poor outcomes. We investigated the prevalence of anemia and its association with in-hospital outcomes among patients admitted to cardiac intensive care units (CICUs) and subgroups within this population.

Methods

The Critical Care Cardiology Trials Network (CCCTN) is a multicenter network of tertiary CICUs in North America. CICU admissions with available baseline hemoglobin (Hgb) between 2017 and 2023 were included in this analysis. Patients were stratified by Hgb levels (Hgb <8 g/dL, 8 g/dL ≤ Hgb <10 g/dL, 10 g/dL ≤ Hgb <12 g/dL, 12 g/dL ≤ Hgb <14 g/dL, and ≥14 g/dL). The ≥14 g/dL group was used for reference. The association of Hgb level and in-hospital mortality was examined by multivariable logistic regression.

Results

Among 28,585 patient admissions (median age 67 years, 36.7 % female), the median Hgb was 12.1 g/dL (interquartile range: 10.1–13.9), with 48.3 % of patients who meet criteria for anemia (Hgb <12 g/dL). The adjusted relative odds of in-hospital mortality was highest for patients with Hgb <8 g/dL (1.60, 95 % confidence interval [CI]: 1.35 to 1.89, P < 0.0001), followed by patients with 8 g/dL≤ Hgb <10 g/dL (adjusted relative odds =1.51, 95 % CI: 1.32 to 1.73, P < 0.0001), and patients with Hgb 10 g/dL≤ Hgb<12 g/dL (adjusted relative odds=1.24, 95 % CI: 1.09 to 1.41, P=0.0012). This association was present among those with non-acute coronary syndrome (ACS) cardiogenic shock (n=4255) and those with non-cardiogenic shock ACS (n=7194).

Conclusions

Anemia was present in nearly half of patients admitted to CICUs. Lower admission Hgb is independently associated with higher in-hospital mortality in a graded relationship among patients with cardiac critical illness.

背景：贫血在危重患者中很常见，并与不良预后相关。我们调查了心脏重症监护病房（CICUs）住院患者及其亚组中贫血的患病率及其与住院预后的关系。方法重症监护心脏病学试验网络（CCCTN）是北美三级重症监护中心的多中心网络。该分析纳入了2017年至2023年期间可用基线血红蛋白（Hgb）的CICU入院患者。患者按Hgb水平分层（Hgb <、8g /dL、8g /dL≤Hgb <、10g /dL、10g /dL≤Hgb <、12g /dL、12g /dL≤Hgb <、14g /dL、≥14g /dL）。≥14 g/dL组为参考。采用多变量logistic回归检验Hgb水平与住院死亡率的关系。结果28,585例入院患者（中位年龄67岁，36.7% %女性）中位Hgb为12.1 g/dL（四分位数范围：10.1-13.9），48.3% %的患者符合贫血标准（Hgb <12 g/dL）。Hgb <；8 g/dL患者的调整后住院死亡率相对赔率最高(1.60,95 %置信区间[CI]: 1.35 ~ 1.89, P <；0.0001)，其次是8 g/dL≤Hgb <；10 g/dL(校正相对优势=1.51,95 % CI: 1.32 ~ 1.73, P <；0.0001)， hgb10g /dL≤hgb12g /dL患者（校正相对优势=1.24,95 % CI: 1.09 ~ 1.41， P=0.0012）。在非急性冠脉综合征（ACS）心源性休克（n=4255）和非心源性休克ACS （n=7194）患者中存在这种关联。结论近半数住院患者存在贫血。在心脏危重症患者中，较低的入院Hgb与较高的住院死亡率呈分级关系独立相关。

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引用次数: 0

The glyoxalase system: A new target for inflammatory diseases 乙二醛酶系统：炎症性疾病的新靶点

Journal of intensive medicine

Pub Date : 2025-07-01 DOI: 10.1016/j.jointm.2025.03.002

Yingyi Yang , Rui Kang , Huiting Zhou , Daolin Tang

引用次数: 0

CHA2DS2-VASc scores to predict left atrial/left atrial appendage abnormalities in patients with sepsis-induced atrial fibrillation: A preliminary investigation CHA2DS2-VASc评分预测脓毒症心房颤动患者左房/左房附件异常的初步研究

Journal of intensive medicine

Pub Date : 2025-07-01 DOI: 10.1016/j.jointm.2024.12.007

Vincent Labbé , Stephane Ederhy , David Legouis , Jérémie Joffre , Keyvan Razazi , Oumar Sy , Frank Chemouni , Armand Mekontso Dessap , Muriel Fartoukh , Ariel Cohen , FAST Study Group

引用次数: 0

Title Page 标题页

Journal of intensive medicine

Pub Date : 2025-03-29 DOI: 10.1016/S2667-100X(25)00007-6

引用次数: 0

Predicting multiple organ dysfunction syndrome in trauma-induced sepsis: Nomogram and machine learning approaches 预测创伤性败血症中的多器官功能障碍综合征：Nomogram和machine learning方法

Journal of intensive medicine

Pub Date : 2025-02-08 DOI: 10.1016/j.jointm.2024.12.008

Jinyu Peng , Yun Li , Chao Liu , Zhi Mao , Hongjun Kang , Feihu Zhou

Background

Multiple organ dysfunction syndrome (MODS) is a critical complication in trauma-induced sepsis patients and is associated with a high mortality rate. This study aimed to develop and validate predictive models for MODS in this patient population using a nomogram and machine learning approaches.

Methods

This retrospective cohort study utilized data from the Medical Information Mart for Intensive Care-IV 2.2 database, focusing on trauma patients diagnosed with sepsis within the first day of intensive care unit (ICU) admission. Predictive variables were extracted from the initial 24 h of ICU data. The dataset (2008–2019) was divided into a training set (2008–2016) and a temporal validation set (2017–2019). Feature selection was conducted using the Boruta algorithm. Predictive models were developed and validated using a nomogram and various machine learning techniques. Model performance was evaluated based on discrimination, calibration, and decision curve analysis.

Results

Among 1295 trauma patients with sepsis, 349 (26.95%) developed MODS. The 28-day mortality rates were 11.21% for non-MODS patients and 23.82% for MODS patients. Key predictors of MODS included the simplified acute physiology score II score, use of mechanical ventilation, and vasopressor administration. In temporal validation, all models significantly outperformed traditional scoring systems (all P <0.05). The nomogram achieved an area under the curve (AUC) of 0.757 (95% confidence interval [CI]: 0.700 to 0.814), while the random forest model demonstrated the highest performance with an AUC of 0.769 (95% CI: 0.712 to 0.826). Calibration plots showed excellent agreement between predicted and observed probabilities, and decision curve analysis indicated a consistently higher net benefit for the newly developed models.

Conclusion

The nomogram and machine learning models provide enhanced predictive accuracy for MODS in trauma-induced sepsis patients compared to traditional scoring systems. These tools, accessible via web-based applications, have the potential to improve early risk stratification and guide clinical decision-making, ultimately enhancing outcomes for trauma patients. Further external validation is recommended to confirm their generalizability.

背景：多器官功能障碍综合征（MODS）是创伤性脓毒症患者的重要并发症，死亡率高。本研究旨在利用nomogram和机器学习方法开发并验证该患者群体MODS的预测模型。方法本回顾性队列研究利用重症监护医学信息市场- iv - 2.2数据库的数据，重点研究重症监护病房（ICU）入院第一天诊断为败血症的创伤患者。从ICU的最初24小时数据中提取预测变量。数据集（2008-2019）分为训练集（2008-2016）和时间验证集（2017-2019）。采用Boruta算法进行特征选择。使用nomogram和各种机器学习技术开发并验证了预测模型。基于判别、校准和决策曲线分析对模型性能进行评估。结果1295例创伤脓毒症患者中，349例（26.95%）发生MODS。非MODS患者28天死亡率为11.21%，MODS患者为23.82%。MODS的主要预测因素包括简化急性生理评分、机械通气的使用和血管加压药的使用。在时间验证中，所有模型都显著优于传统评分系统（均P <；0.05）。模态图的曲线下面积（AUC）为0.757(95%可信区间[CI]: 0.700至0.814)，而随机森林模型的AUC为0.769 (95% CI: 0.712至0.826)，表现出最高的性能。校正图显示预测概率和观测概率非常吻合，决策曲线分析表明新开发模型的净效益始终较高。结论与传统评分系统相比，nomogram和machine learning模型可提高创伤性败血症患者MODS的预测准确性。这些工具可通过基于网络的应用程序访问，具有改善早期风险分层和指导临床决策的潜力，最终提高创伤患者的预后。建议进一步进行外部验证以确认其通用性。

{"title":"Predicting multiple organ dysfunction syndrome in trauma-induced sepsis: Nomogram and machine learning approaches","authors":"Jinyu Peng , Yun Li , Chao Liu , Zhi Mao , Hongjun Kang , Feihu Zhou","doi":"10.1016/j.jointm.2024.12.008","DOIUrl":"10.1016/j.jointm.2024.12.008","url":null,"abstract":"<div><h3>Background</h3><div>Multiple organ dysfunction syndrome (MODS) is a critical complication in trauma-induced sepsis patients and is associated with a high mortality rate. This study aimed to develop and validate predictive models for MODS in this patient population using a nomogram and machine learning approaches.</div></div><div><h3>Methods</h3><div>This retrospective cohort study utilized data from the Medical Information Mart for Intensive Care-IV 2.2 database, focusing on trauma patients diagnosed with sepsis within the first day of intensive care unit (ICU) admission. Predictive variables were extracted from the initial 24 h of ICU data. The dataset (2008–2019) was divided into a training set (2008–2016) and a temporal validation set (2017–2019). Feature selection was conducted using the Boruta algorithm. Predictive models were developed and validated using a nomogram and various machine learning techniques. Model performance was evaluated based on discrimination, calibration, and decision curve analysis.</div></div><div><h3>Results</h3><div>Among 1295 trauma patients with sepsis, 349 (26.95%) developed MODS. The 28-day mortality rates were 11.21% for non-MODS patients and 23.82% for MODS patients. Key predictors of MODS included the simplified acute physiology score II score, use of mechanical ventilation, and vasopressor administration. In temporal validation, all models significantly outperformed traditional scoring systems (all <em>P</em> <0.05). The nomogram achieved an area under the curve (AUC) of 0.757 (95% confidence interval [CI]: 0.700 to 0.814), while the random forest model demonstrated the highest performance with an AUC of 0.769 (95% CI: 0.712 to 0.826). Calibration plots showed excellent agreement between predicted and observed probabilities, and decision curve analysis indicated a consistently higher net benefit for the newly developed models.</div></div><div><h3>Conclusion</h3><div>The nomogram and machine learning models provide enhanced predictive accuracy for MODS in trauma-induced sepsis patients compared to traditional scoring systems. These tools, accessible via web-based applications, have the potential to improve early risk stratification and guide clinical decision-making, ultimately enhancing outcomes for trauma patients. Further external validation is recommended to confirm their generalizability.</div></div>","PeriodicalId":73799,"journal":{"name":"Journal of intensive medicine","volume":"5 2","pages":"Pages 193-201"},"PeriodicalIF":0.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Causes of fulminant tropical probable myocarditis: A retrospective cohort study in the French West Indies 暴发性热带可能心肌炎的病因：法属西印度群岛的一项回顾性队列研究。

Journal of intensive medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.jointm.2024.07.001

Laurent Camous , Nicolas Paulo , Frederic Martino , Sylvaine Bastian , Marc Valette , Jean-David Pommier

引用次数: 0

Outcomes and risk factors of transported patients with extracorporeal membrane oxygenation: An ECMO center experience 体外膜氧合转运患者的疗效和风险因素：ECMO 中心的经验

Journal of intensive medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.jointm.2024.04.003

Lingjuan Liu , Dingji Hu , Tong Hao , Shanshan Chen , Lei Chen , Yike Zhu , Chenhui Jin , Jing Wu , Haoya Fu , Haibo Qiu , Yi Yang , Songqiao Liu

Background

Extracorporeal membrane oxygenation (ECMO) has been proven to be a support method and technology for patients with cardiopulmonary failure. However, the transport of patients under ECMO support is challenging given the high-risk technical maneuvers and patient-care concerns involved. Herein, we examined the safety of ECMO during the transport of critically ill patients and its impact on mortality rates, to provide more secure and effective transport strategies in clinical practice.

Method

To assess the safety of ECMO patient transport, this study conducted a retrospective analysis on critically ill adults who required ECMO support and transport at the intensive care unit (ICU) center between 2017 and 2023. The study utilized standard ECMO transport protocols and conducted a comprehensive statistical analysis of the collected clinical data and transport processes. The 28-day survival rate for ECMO patients was determined using Kaplan–Meier analysis, while logistic regression identified prognostic factors.

Result

Out of 303 patients supported with ECMO, 111 (36.6%) were transported. 69.4% of the transport group were male, mean age was (42.0±17.0) years, mean body mass index was (24.4±4.6) kg/m², and veno-arterial-ECMO accounted for 52.5%. The median transportation distance was 190 (interquartile range [IQR]: 70–260) km, and the longest distance was 8100 km. The median transit time was 180 (IQR: 100–260) min, and the maximum duration was 1720 min. No severe adverse events including death or mechanical failure occurred during transportation. The 28-day survival rate was 64.7% (n=196) and ICU survival rate was 56.1% (n=170) for the entire cohort; whereas, the 28-day survival rate was 72.1% (n=80) and ICU survival rate was 66.7% (n=74) in the transport group. A non-significant difference in 28-day survival was observed between the two groups after propensity score matching (P=0.56). Additionally, we found that acute physiology and chronic health evaluation II score (odds ratio [OR]=1.06, P <0.01), lactate levels (>5 mmol/L, OR=2.80, P=0.01), and renal replacement therapy initiation (OR=3.03, P <0.01) were associated with increased mortality risk.

Conclusion

Transporting patients on ECMO between medical facilities is a safe procedure that does not increase patient mortality rates, provided it is orchestrated and executed by proficient transport teams. The prognostic outcome for these patients is predominantly influenced by their pre-existing medical conditions or by complications that may develop during the course of ECMO therapy. These results form the basis for the creation of specialized ECMO network hubs within healthcare regions.

体外膜氧合（ECMO）已被证明是一种支持心肺衰竭患者的方法和技术。然而，考虑到高风险的技术操作和涉及的患者护理问题，在ECMO支持下运送患者是具有挑战性的。在此，我们研究了重症患者转运过程中ECMO的安全性及其对死亡率的影响，以期在临床实践中提供更安全有效的转运策略。方法为了评估ECMO患者运输的安全性，本研究对2017年至2023年在重症监护病房（ICU）中心需要ECMO支持和运输的危重成人患者进行回顾性分析。本研究采用标准ECMO转运方案，并对收集到的临床数据和转运过程进行了全面的统计分析。采用Kaplan-Meier分析确定ECMO患者的28天生存率，同时采用logistic回归确定预后因素。结果303例ECMO患者中，111例（36.6%）被转移。转运组69.4%为男性，平均年龄（42.0±17.0）岁，平均体重指数（24.4±4.6）kg/m2，静脉-动脉- ecmo占52.5%。运输距离中位数为190 km（四分位间距[IQR]: 70-260），最长运输距离为8100 km。中位运输时间为180 (IQR: 100-260) min，最长时间为1720 min。运输过程中未发生包括死亡或机械故障在内的严重不良事件。整个队列28天生存率为64.7% (n=196)， ICU生存率为56.1% (n=170)；转运组28天生存率为72.1% (n=80)， ICU生存率为66.7% （n=74）。倾向评分匹配后，两组28天生存率无显著差异（P=0.56）。此外，我们发现急性生理和慢性健康评估II评分（优势比[OR]=1.06, P <0.01）、乳酸水平（>5 mmol/L， OR=2.80， P=0.01）和肾脏替代治疗开始（OR=3.03, P <0.01）与死亡风险增加相关。结论：在不同医疗机构之间进行ECMO患者的转运是一种安全的操作，如果由熟练的转运团队精心安排和执行，不会增加患者的死亡率。这些患者的预后主要受其先前存在的医疗状况或在ECMO治疗过程中可能出现的并发症的影响。这些结果构成了在医疗保健区域内创建专门ECMO网络中心的基础。

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引用次数: 0

A modified screening protocol for ARDS in patients with respiratory support based on SpO2 and FiO2: A single-center prospective, observational study 基于SpO2和FiO2的呼吸支持患者ARDS改进筛查方案：一项单中心前瞻性观察性研究。

Journal of intensive medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.jointm.2024.06.002

Yan Xia , Qiancheng Xu , Zhiyuan Guo , Huijuan Zhang , Yingya Cao , Yupeng Qi , Qun Chen , Weihua Lu

Background

The purpose is to formulate a modified screening protocol for acute respiratory distress syndrome (ARDS) in patients with respiratory support based on saturation of pulse oximetry (SpO₂) and inspired oxygen concentration (FiO₂).

Methods

This prospective observational study was conducted from August to October 2020 at the Department of Critical Care Medicine of Yijishan Hospital Affiliated with Wannan Medical College. All patients admitted during the study period and required arterial blood gas analysis and electrocardiogram monitoring were included in this study. Patients with contraindications to arterial puncture, methemoglobinemia, carbon monoxide poisoning, and other factors that could affect data collection were excluded. The demographic and clinical data, immediate percutaneous SpO₂, FiO₂, arterial oxygen partial pressure (PaO₂), and respiratory rate were recorded; and the SpO₂/FiO₂ ratio (SFR) and PaO₂/FiO₂ ratio (PFR) values were calculated according to the above information. The patients were divided into two cohorts by random number table: the establishment cohort and the verification cohort. In the established part, data were divided into group H and group N according to whether SpO₂ >97 %. For group H (SpO₂ ≤97 %), the regression equation was established between SFR and PFR. For group N (SpO₂ >97 %), the correlation between each observation data and PFR was analyzed. Then, a new diagnostic process was established, and the reliability was verified with the Berlin definition set as the gold standard for diagnosis and classification.

Results

There were 341 patients were included. Among them, 161 patients were used to establish the model, and 180 patients were used to verify the validity of the model. In this new diagnosis progress, when SpO₂ ≤97 %, if SFR ≤352, ARDS may exist; when SpO₂ >97 %, if FiO_2min >39 %, there may be ARDS. The sensitivity, specificity, negative predictive value, positive predictive value, and accuracy of the new diagnosis progress for ARDS were 91.1 %, 76.7 %, 89.6 %, 79.6 %, and 83.9 %, respectively.

Conclusion

The SpO₂/FiO₂ ratio demonstrates notable sensitivity and specificity in diagnosing ARDS, presenting as a credible alternative to PFR.

Trail Registration Chinese Clinical Trial Registry Identifier: ChiCTR2000029217

背景：目的：基于脉搏血氧饱和度（SpO2）和吸入氧浓度（FiO2）制定呼吸支持患者急性呼吸窘迫综合征（ARDS）的改进筛查方案。方法：本前瞻性观察研究于2020年8月至10月在皖南医学院附属一积山医院重症医学科进行。所有在研究期间入院并需要进行动脉血气分析和心电图监测的患者均纳入本研究。排除动脉穿刺禁忌症、高铁血红蛋白血症、一氧化碳中毒等可能影响数据收集的因素。记录患者的人口统计学和临床资料，即时经皮SpO2、FiO2、动脉氧分压（PaO2）和呼吸频率；并根据上述信息计算SpO2/FiO2比值（SFR）和PaO2/FiO2比值（PFR）值。采用随机数字表法将患者分为两组：建立组和验证组。在所建立的部分中，根据SpO2是否达到97%将数据分为H组和N组。H组（SpO2≤97%），SFR与PFR之间建立回归方程。对于N组（SpO2 bb0 97%），分析各观测数据与PFR的相关性。然后，建立了一种新的诊断流程，并以柏林定义集作为诊断和分类的金标准，验证了可靠性。结果：共纳入341例患者。其中，用161例患者建立模型，用180例患者验证模型的有效性。在这一新诊断进展中，当SpO2≤97%，SFR≤352时，可能存在ARDS；当SpO2 >为97%，FiO2min >为39%时，可能发生ARDS。ARDS新诊断进展的敏感性、特异性、阴性预测值、阳性预测值和准确率分别为91.1%、76.7%、89.6%、79.6%和83.9%。结论：SpO2/FiO2比值诊断ARDS具有明显的敏感性和特异性，可作为PFR的可靠替代指标。中国临床试验注册号：ChiCTR2000029217。

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引用次数: 0