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Endotracheal surfactant for infants with life-threatening bronchiolitis (BESS): a randomised, blinded, sham-controlled, phase 2 trial 气管内表面活性剂治疗危及生命的毛细支气管炎(BESS):一项随机、盲法、假对照的2期试验
IF 76.2 1区 医学 Q1 CRITICAL CARE MEDICINE Pub Date : 2026-03-22 DOI: 10.1016/s2213-2600(26)00008-1
Malcolm G Semple, Chloe Donohue, Laura Price, Rachael Cooper, Carly Vaughan, Tracy Moitt, Lynsey Finnetty, Paul C Ritson, Blessing Osaghae, Evette Allen, Clare Fowler, Rachel S Agbeko, Edgar Brincat, Jane V Cassidy, Patrick E Davies, Peter J Davis, Elisabeth Day, Constantinos Kanaris, Simona Lampariello, Richard Levin, Tsz-Yan Milly Lo, Kevin P Morris, Ahmed Osman, Stephen D Playfor, Julie Richardson, Gerri Sefton, Santosh Sundararajan, James Weitz, Annmarie Wherton-Whitehurst, Elizabeth Deja, Kerry Woolfall, Jens Madsen, Anthony D Postle, Catrin Barker, Carrol Gamble, Howard W Clark, John Pappachan, Kentigern Thorburn, Matthew Peak, Paul S McNamara, Mark A Turner, Ashley P Jones, Malcolm G Semple, Chloe Donohue, Laura Price, Rachael Cooper, Carly Vaughan, Tracy Moitt, Lynsey Finnetty, Paul C Ritson, Blessing Osaghae, Evette Allen, Clare Fowler, Rachel S Agbeko, Edgar Brincat, Jane V Cassidy, Patrick E Davies, Peter J Davis, Elisabeth Day, Constantinos Kanaris, Simona Lampariello, Richard Levin, Tsz-Yan Milly Lo, Kevin P Morris, Ahmed Osman, Stephen D Playfor, Julie Richardson, Gerri Sefton, Santosh Sundararajan, James Weitz, Annmarie Wherton-Whitehurst, Elizabeth Deja, Kerry Woolfall, Jens Madsen, Anthony Postle, Catrin Barker, Carrol Gamble, Howard W Clark, John Pappachan, Kentigern Thorburn, Matthew Peak, Paul S McNamara, Mark A Turner, Ashley P Jones, Helen Adamson, Cara Alexander, John Alexander, Laura Anderson, David Armstrong, Lydia Ashton, Judit Bak, Katherine Baptiste, Kirsten Beadon, Rebecca Beckley, Lynne Bell, Ashley Best, Amy Brammar, Wendy Browne, Lara T Bunni, Lisa-Marie Butt, Hilary Callaghan, Lorena Caruana, Tania Castillo Hernandez, Susanne Cathcart, Hannah Child, Bessie Cipriano, Hannah Clarke, Stephanie Clarke, Rob Claydon, Sophie Coles, Vanessa Compton, Amber Cook, Lindsay Crate, Tracey Curtis, Sarah Daggett, Anne Dawson, Laura Dodge, Rachael Dore, Sarah-Jayne Eames, Nichola Etherington, Samantha Finn, Sarah Fox, Crawford Fulton, Simon Gates, Bernadette C Gavin, Annabel Giddings, Jessica Green, Michael Griksaitis, Paris-Lucia Harrison, Ellen Haskins, Nadine Heenan, Elizabeth Henderson, Rebecca Hill, Sarah Hopton, Claire F Jennings, Rebecca Jennings, Petr Jirasek, Dawn Jones, Ebraheem Junaid, Nosheen Khalid, Tahmina Khatun, Ramiya Kirupananthan, Craig Knott, Ramesh Kumar, Samantha La Roche, Stephanie Laidlaw, Benjamin Lakin, Christopher Lamb, Flora Lewis, Christina Linton, Vicki Linton, Lisa Lucyk, Jeremy Lyons, Christine Mackerness, Dave Malabar, Michael Mander, Muhammed Pradhika Mapindra, Helen Marley, Ross Marscheider, Michael J Marsh, Rebecca Marshall, Lindy Martin, Shelley Mayor, Joseph McCann, Nicola McClelland, Laura McConaghy, Jenni McCorkell, Jackie McCormick, Liz McCullagh, Rachel McMinnis, Samantha Mead, Christie Mellish, Natalie Milburn, Bethany Millman, Philip Milner, Holly Minchin, Amisha Mistry, Sarah Mogan, Seana Molloy, Dave J Morgan, Tara Murray, Manoj Kumar Muthusamy, Clara Nelson, Katie M Neville, Bethan Newell, Heather Nicholls, Olivia Nugent, Laura O'Malley, Udeme Ohia, Zoe Oliver, Madhuri Panchal, Helen Parker, Holly Parkin, Roger Parslow, Rekha Patel, Simone Paulson, Harriet Payne, Rachael Percival, Nicolene Plaatjies, Jenny Pond, Catherine Postlethwaite, Jennifer Preston, Laura Rad, Raghu N Ramaiah, Pavanasam Ramesh, Maxine Ramsay, Samantha Reed, Kathryn Reeves, Emma K Riley, Laura Rimmer, Ceri Robbins, Natasha Roberts, Christa Ronan, Tomasz Rygielski, Avishay Sarfatti, Rohit Saxena, Alvin Schadenberg, Emily Scriven, Chidambaram Sethuraman, Chris Simons, Frances Sinfield, Anna Stancombe, John Stiven, Elizabeth Stoddart, Colin Summers, Laura J Sutton, Kate Teeley, Mark Terris, Carla Thomas, Charlotte Thompson, Clare Thompson, Natasha Thorn, Joanne Tomlinson, Patrick Tomlinson, Carly Tooke, Marie Turner, Clare van Miert, Anand Wagh, Laura Wallis, Penny Walsh, Marie Ward, Sophia Ward, Jacqueline Waters, Jack Watson, Grace Williamson, Helen Winmill, Andrea Wood, Laura Wooler
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引用次数: 0
Surfactant therapy in severe infant bronchiolitis: evidence from the BESS trial 表面活性剂治疗重症婴儿细支气管炎:来自BESS试验的证据
IF 76.2 1区 医学 Q1 CRITICAL CARE MEDICINE Pub Date : 2026-03-21 DOI: 10.1016/s2213-2600(26)00043-3
Sailesh Kotecha
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引用次数: 0
Caring for caregivers: supporting invisible partners of the ICU 照顾照顾者:支持ICU的隐形伙伴
IF 76.2 1区 医学 Q1 CRITICAL CARE MEDICINE Pub Date : 2026-03-18 DOI: 10.1016/s2213-2600(26)00090-1
No Abstract
没有抽象的
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引用次数: 0
Acute respiratory failure in immunocompromised adults: the case for personalised care strategies 免疫功能低下成人急性呼吸衰竭:个体化护理策略的案例
IF 76.2 1区 医学 Q1 CRITICAL CARE MEDICINE Pub Date : 2026-03-16 DOI: 10.1016/s2213-2600(26)00075-5
Carol L Hodgson, Richard A Greendyk
No Abstract
没有抽象的
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引用次数: 0
The host response in sepsis: can it be quantified? 脓毒症的宿主反应:可以量化吗?
IF 76.2 1区 医学 Q1 CRITICAL CARE MEDICINE Pub Date : 2026-03-16 DOI: 10.1016/s2213-2600(25)00465-5
Pratik Sinha, Nuala J Meyer
No Abstract
没有抽象的
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引用次数: 0
Quantifying immune dysregulation in pneumonia and sepsis with a parsimonious machine-learning model: a multicohort analysis across care settings and reanalysis of a hydrocortisone randomised controlled trial 用简化的机器学习模型量化肺炎和败血症的免疫失调:跨护理环境的多队列分析和氢化可的松随机对照试验的再分析
IF 76.2 1区 医学 Q1 CRITICAL CARE MEDICINE Pub Date : 2026-03-16 DOI: 10.1016/s2213-2600(25)00429-1
Erik H A Michels, Pierre-François Dequin, Joe M Butler, Antoine Guillon, Bruno Evrard, Fleur P Paling, Tom D Y Reijnders, Alex R Schuurman, Tjitske S R van Engelen, Xanthe Brands, Bastiaan W Haak, Lieuwe D J Bos, Carolyn Leroux, Evangelos J Giamarellos-Bourboulis, Jaap Stoker, Jan M Prins, Daniël R Faber, Renée A Douma, Timothy E Sweeney, Surbhi Malhotra-Kumar, Tom van der Poll
<h3>Background</h3>Sepsis is a dysregulated host response to infection resulting in life-threatening organ failure. Although immune dysregulation is central to the sepsis definition, immunomodulation trials enrol participants based on clinical severity, not the extent of dysregulation, which could contribute to treatment heterogeneity. A pragmatic way to quantify immune dysregulation could improve prognostication, help to evaluate treatment responses, and identify individuals most likely to benefit from immunomodulation. We aimed to construct a parsimonious machine-learning tool that defines and quantifies immune dysregulation, thereby supporting biologically informed immunomodulation.<h3>Methods</h3>In this multicohort analysis and reanalysis of a randomised controlled trial, the primary objective was to derive and validate a categorical and continuous immune dysregulation score that is independent of clinical presentation or outcome. We measured 35 plasma biomarkers reflecting key host response domains in individuals with community-acquired pneumonia (CAP) across different care settings (emergency department, general ward, and intensive care unit) and disease severities using data from three independent cohorts. We applied unsupervised trajectory inference analysis to identify an immune dysregulation gradient captured as discrete immune dysregulation stages (Dysregulated Immune Profile [DIP]) and a continuous score (cDIP; 0–1). We developed two parsimonious machine-learning models to predict the DIP stages and cDIP scores based on 35 biomarkers, and validated their ability to capture immune dysregulation and predict clinical outcomes in five independent cohorts. On the basis of our hypothesis that only individuals with severe immune dysregulation benefit from immunomodulation, we carried out a post-hoc analysis of a randomised trial evaluating hydrocortisone in severe CAP (CAPE COD trial, <span><span>NCT02517489</span><svg aria-label="Opens in new window" focusable="false" height="20" viewbox="0 0 8 8"><path d="M1.12949 2.1072V1H7V6.85795H5.89111V2.90281L0.784057 8L0 7.21635L5.11902 2.1072H1.12949Z"></path></svg></span>), assessing treatment effects across DIP stages and the cDIP continuum, and how hydrocortisone influenced dysregulation trajectories over time.<h3>Findings</h3>We organised 398 participants with CAP along a continuum of immune dysregulation from mild to severe on the basis of 35 plasma biomarkers, yielding three dysregulation stages (DIP1–3) and a continuous score (cDIP). Clinical severity proved to be an inadequate proxy for immune dysregulation. A three-biomarker machine-learning framework (procalcitonin, soluble TREM-1, and IL-6) accurately predicted the degree of dysregulation derived from 35 biomarkers (DIP stage accuracy 91·2%; cDIP root mean square error 0·056). Although the framework was not designed for outcome prediction, increased immune dysregulation—reflected in DIP and cDIP—was associated with a gradual rise in mor
脓毒症是宿主对感染的失调反应,导致危及生命的器官衰竭。尽管免疫失调是脓毒症定义的核心,但免疫调节试验根据临床严重程度而不是失调程度招募参与者,这可能导致治疗异质性。一种量化免疫失调的实用方法可以改善预后,帮助评估治疗反应,并确定最有可能从免疫调节中受益的个体。我们的目标是构建一个简约的机器学习工具来定义和量化免疫失调,从而支持生物知情的免疫调节。方法在这项随机对照试验的多队列分析和再分析中,主要目的是推导和验证一个独立于临床表现或结果的分类和连续免疫失调评分。我们使用来自三个独立队列的数据,测量了35个血浆生物标志物,反映了不同护理环境(急诊科、普通病房和重症监护病房)和疾病严重程度的社区获得性肺炎(CAP)患者的关键宿主反应域。我们应用无监督轨迹推断分析来识别被捕获为离散免疫失调阶段(Dysregulated immune Profile [DIP])和连续评分(cDIP; 0-1)的免疫失调梯度。我们开发了两个简洁的机器学习模型来预测基于35个生物标志物的DIP阶段和cDIP评分,并在五个独立队列中验证了它们捕获免疫失调和预测临床结果的能力。基于我们的假设,只有严重免疫失调的个体才能从免疫调节中获益,我们对一项评估氢化可体松治疗严重CAP的随机试验(CAPE COD试验,NCT02517489)进行了事后分析,评估了DIP分期和cDIP连续体的治疗效果,以及氢化可体松如何随时间影响失调轨迹。研究结果:根据35种血浆生物标志物,我们组织了398名CAP患者,他们的免疫失调从轻微到严重,产生了三个失调阶段(DIP1-3)和一个连续评分(cDIP)。临床严重程度被证明是免疫失调的一个不充分的代理。一个三生物标志物机器学习框架(降钙素原、可溶性TREM-1和IL-6)准确预测了35个生物标志物的失调程度(DIP阶段精度90.2%;cDIP均方根误差0.056)。虽然该框架不是为预测结果而设计的,但免疫失调的增加——反映在DIP和cDIP中——与死亡率的逐渐上升相关(cDIP优势比[OR]每增加10% 1.26 [95% CI 1.13 - 1.40], p= 0.0001)和继发感染(OR [OR]每增加10% 1.50[1.22 - 1·93],p= 0.0005),与临床严重程度无关。三种生物标志物工具在5个不同感染、严重程度和护理环境的外部队列中得到验证(n=1191)。CAPE - COD试验的再分析显示,氢化可体松仅在我们的模型中被分类为严重失调的参与者(30天死亡率:DIP3 OR 0.25 [0.05 - 0.85], p= 0.042; cDIP≥0.63 OR 0.21 [0.10 - 0.72], p= 0.011)中获得生存益处,并伴有更快的免疫恢复(时间×治疗相互作用,p< 0.0001)。当根据临床严重程度对参与者进行分层时,没有观察到这种效果的改变。我们提供了一个公开可用的三种生物标志物框架,以确定宿主反应失调的程度,并具有精确指导免疫调节治疗的潜在价值。资助欧盟地平线2020。
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引用次数: 0
Epidemiology, ventilation, and outcomes of acute respiratory failure in immunocompromised patients from 103 intensive care units in 26 countries: a retrospective observational study 来自26个国家103个重症监护病房的免疫功能低下患者急性呼吸衰竭的流行病学、通气和结局:一项回顾性观察性研究
IF 76.2 1区 医学 Q1 CRITICAL CARE MEDICINE Pub Date : 2026-03-16 DOI: 10.1016/s2213-2600(26)00046-9
Elie Azoulay, Colleen McEvoy, Pedro Castro, Ali Ait Hssain, Fabio Silvio Taccone, Sheila N Myatra, Guillaume Dumas, Jan-Hendrik Naendrup, Gaston Burghi, Cristina Gutiérrez, Joseph Nates, Ricard Ferrer, Laurent Argaud, Raphaël Clere-Jehl, Damien Vimpere, Huiqing Ge, Anne-Sophie Moreau, Yaseen M Arabi, Maria Theodorakopoulou, Christina-Chrysanthi Theocharidou, Manuel Perez Torres
<h3>Background</h3>Acute hypoxaemic respiratory failure (ARF) is the leading cause of intensive care unit (ICU) admission among immunocompromised patients. However, contemporary data regarding the epidemiology, management, and outcomes of ARF in this population remain scarce. We aimed to identify predictors of mortality and intubation in immunocompromised patients admitted to the ICU with ARF.<h3>Methods</h3>This retrospective observational study was conducted in 103 ICUs in 26 countries. Adults (≥18 years) with ARF and immunodeficiency were eligible for inclusion. Patient data, including information on the nature of underlying immunosuppression, the cause of ARF, and the oxygenation strategy, were obtained from electronic medical records or medical charts. The primary outcome was to report 30-day mortality and identify associated factors in patients with complete data for all variables. Cox proportional hazards models were used to identify variables associated with mortality, and differences between groups were compared with χ<sup>2</sup> tests or two-sided Wilcoxon rank-sum tests, with p values of less than 0·05 considered significant.<h3>Findings</h3>9854 immunocompromised patients with ARF admitted to participating ICUs between Jan 1, 2017, and Dec 31, 2023, were included in the study. The median age was 64 years (IQR 54–71); 3941 (40·0%) patients were female and 5913 (60·0%) were male. The main causes of immunodeficiency were a haematological malignancy (4759 [48·3%] of 9854 patients) or solid malignancy (3818 [38·7%] patients). Infection was the leading cause of ARF (6610 [62·0%] of 9854 patients); 5288 (53·7%) patients had more than one contributing cause of ARF, and no cause was identified in 1490 (15·1%) patients. The median partial pressure of oxygen in arterial blood (PaO<sub>2</sub>)/fractional concentration of oxygen in inspired air (FiO<sub>2</sub>) ratio was 198 [IQR 141–208]. The 30-day mortality rate was 47·3% (4662 patients). Predictors of higher mortality were older age (hazard ratio 1·01 [IQR 1·00–1·02]), higher Charlson Comorbidity Index score (1·04 [1·01–1·07]), higher Frailty Index score (1·22 [1·16–1·28]), longer time from hospital to ICU admission (1·02 [1·01–1·03]), higher respiratory rate (1·02 [1·02–1·03]), coma at ICU admission (2·04 [1·72–2·43]), invasive fungal infection as cause of ARF (1·82 [1·45–2·28]), disease-specific infiltrates (1·73 [1·32–2·26]), unidentified cause of ARF (2·16 [1·74–2·68]), and use of vasoactive drugs (2·45 [2·10–2·86]) or renal replacement therapy (2·07 [1·74–2·48]). Protective factors included receipt of a solid organ transplant (0·62 [0·49–0·79]), systemic vasculitis or connective tissue disease (0·61 (0·47–0·78]), higher PaO<sub>2</sub>/FiO<sub>2</sub> ratio (0·78 [0·72–0·84]), receipt of high-flow nasal oxygen therapy (0·78 [0·64–0·95]), and cardiogenic pulmonary oedema (0·67 [0·51–0·89]).<h3>Interpretation</h3>In this large international cohort of immunocompromised patients with ARF,
背景:急性低氧性呼吸衰竭(ARF)是免疫功能低下患者入住重症监护病房(ICU)的主要原因。然而,关于这一人群中ARF的流行病学、管理和结果的当代数据仍然很少。我们的目的是确定因ARF而入住ICU的免疫功能低下患者的死亡率和插管的预测因素。方法对26个国家103个icu进行回顾性观察性研究。伴有ARF和免疫缺陷的成人(≥18岁)符合纳入条件。从电子病历或医疗图表中获得患者数据,包括潜在免疫抑制的性质、ARF的原因和氧合策略。主要结果是报告30天死亡率,并确定所有变量数据完整的患者的相关因素。采用Cox比例风险模型识别与死亡率相关的变量,采用χ2检验或双侧Wilcoxon秩和检验比较组间差异,p值小于0.05认为差异有统计学意义。在2017年1月1日至2023年12月31日期间,9854名ARF免疫功能低下患者被纳入研究。中位年龄64岁(IQR 54-71);女性3941例(40.0%),男性5913例(60.00%)。免疫缺陷的主要原因是血液恶性肿瘤(9854例患者中4759例[48.3%])或实体恶性肿瘤(3818例[38.7%])。感染是ARF的主要原因(9854例患者中有6610例[62.0%]);5288例(53.7%)患者有一个以上的ARF病因,1490例(15.1%)患者未确定病因。动脉血中氧分压(PaO2)与吸入空气中氧分数浓度(FiO2)之比为198 [IQR 141-208]。30天死亡率为47.3%(4662例)。较高死亡率的预测因子为年龄较大(危险比1.01 [IQR 1.00 - 1.02])、较高的Charlson共病指数评分(1.04[1.01 - 1.07])、较高的衰弱指数评分(1.22[1.16 - 1.28])、较长的入院时间(1.02[1.01 - 1.03])、较高的呼吸频率(1.02[1.02 - 1.03])、入院时昏迷(2.04[1.72 - 2.43])、引起ARF的侵袭性真菌感染(1.82[1.45 - 2.28])、疾病特异性浸润(1.73[1.32 - 2.26])、不明原因的ARF(2.16[1.74 - 2.68])、使用血管活性药物(2.45[2.10 - 2.86])或肾脏替代治疗(2.07[1.74 - 2.48])。保护因素包括接受实体器官移植(0.62[0.49 - 0.79])、全身性血管炎或结缔组织疾病(0.61(0.47 - 0.78))、较高的PaO2/FiO2比值(0.78[0.72 - 0.84])、接受高流量鼻吸氧治疗(0.78[0.64 - 0.95])、心源性肺水肿(0.67[0.51 - 0.89])。在这一大型国际ARF免疫功能低下患者队列中,我们确定了死亡率和插管的关键风险和保护因素。这些发现可以通过及时的临床决策、护理目标的讨论和弱势群体的管理来改善结果。资助克尔斯滕和弗雷迪·约翰森基金会和组织的研究,在 以人为单位。
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引用次数: 0
Ten unanswered questions about dipeptidyl peptidase-1 inhibition in bronchiectasis 支气管扩张症中二肽基肽酶-1抑制的十个未解问题
IF 76.2 1区 医学 Q1 CRITICAL CARE MEDICINE Pub Date : 2026-03-14 DOI: 10.1016/s2213-2600(26)00077-9
Jiahui He, Chloe Hughes, Merete B Long, James D Chalmers
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引用次数: 0
UK trials AI and robotics to detect lung cancer earlier 英国试验人工智能和机器人技术来早期检测肺癌
IF 76.2 1区 医学 Q1 CRITICAL CARE MEDICINE Pub Date : 2026-03-11 DOI: 10.1016/s2213-2600(26)00085-8
Priya Venkatesan
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引用次数: 0
When evidence is not enough: making videolaryngoscopy the default for tracheal intubation 当证据不足时:将视频喉镜作为气管插管的默认选择
IF 76.2 1区 医学 Q1 CRITICAL CARE MEDICINE Pub Date : 2026-03-05 DOI: 10.1016/s2213-2600(26)00048-2
Manuel Ángel Gómez-Ríos, André A J Van Zundert
No Abstract
没有抽象的
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引用次数: 0
期刊
Lancet Respiratory Medicine
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