Critical Care Medicine最新文献

{"title":"The authors reply.","authors":"Christopher S Cotton, Abid Alam, Sophie Tosta, Timothy G Buchman, David M Maslove","doi":"10.1097/CCM.0000000000006756","DOIUrl":"https://doi.org/10.1097/CCM.0000000000006756","url":null,"abstract":"","PeriodicalId":10765,"journal":{"name":"Critical Care Medicine","volume":"53 9","pages":"e1840-e1841"},"PeriodicalIF":6.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Indicators Reflecting the Speed of Diaphragm Shortening: Mean Contraction Velocity Versus Peak Contraction Velocity.","authors":"Haotian Zhao, Kai Liu, Li Li","doi":"10.1097/CCM.0000000000006727","DOIUrl":"https://doi.org/10.1097/CCM.0000000000006727","url":null,"abstract":"","PeriodicalId":10765,"journal":{"name":"Critical Care Medicine","volume":"53 9","pages":"e1851-e1852"},"PeriodicalIF":6.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Risk to Recovery: Predicting Quality of Life After Critical Illness.","authors":"Kimia Honarmand","doi":"10.1097/CCM.0000000000006776","DOIUrl":"10.1097/CCM.0000000000006776","url":null,"abstract":"","PeriodicalId":10765,"journal":{"name":"Critical Care Medicine","volume":" ","pages":"e1830-e1832"},"PeriodicalIF":6.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144642031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consideration of Sociodemographics in Machine Learning-Driven Sepsis Risk Prediction.","authors":"Katrina E Hauschildt, Annie Pan, Taylor Bernstein, Andrew J Admon, Bhramar Mukherjee, Theodore J Iwashyna, Lillian Rountree","doi":"10.1097/CCM.0000000000006741","DOIUrl":"10.1097/CCM.0000000000006741","url":null,"abstract":"<p><strong>Objectives: </strong>Use of machine learning (ML) and artificial intelligence (AI) in prediction of sepsis and related outcomes is growing. Guidelines call for explicit reporting of study data demographics and stratified performance analyses to assess potential sociodemographic bias. We assessed reporting of sociodemographic data and other considerations, such as use of stratified analyses or use of so-call \"fairness metrics\", among AI and ML models in sepsis.</p><p><strong>Data sources: </strong>PubMed identified systematic and narrative reviews from which studies were extracted using PubMed and Google Scholar.</p><p><strong>Study selection: </strong>Studies were extracted from selected review articles published between January 1, 2023, and June 30, 2024, and related to sepsis, risk prediction, and ML; we extracted studies predicting sepsis, sepsis-related outcomes, or sepsis treatment in adult populations.</p><p><strong>Data extraction: </strong>Data were extracted by two reviewers using predefined forms, and included study type, outcome of interest, setting, dataset used, reporting of sample sociodemographics, inclusion of sociodemographics as predictors, stratification by sociodemographics or assessment of fairness metrics, and reporting a lack of sociodemographic considerations as a limitation.</p><p><strong>Data synthesis: </strong>Thirteen of 96 review studies (14%) met inclusion criteria: six systematic reviews and seven narrative reviews. One hundred twenty of 170 studies (71%) extracted from these review articles were included in our review. Ninety-nine of 120 studies (83%) reported a measure of geography or where data was collected. Eighty (67%) reported sex/gender, 24 (20%) reported race/ethnicity, and 4 (3%) reported other sociodemographics. Only three stratified performance results (2%) by sociodemographics; none reported formal fairness metrics. Beyond a lack of geographic heterogeneity (39/120, 33%), few studies reported a lack of sociodemographic consideration as a limitation.</p><p><strong>Conclusions: </strong>The inclusion of sociodemographic data and stratified assessment of performance-essential steps in developing equitable risk prediction tools-are possible but have yet to be consistently adopted.</p>","PeriodicalId":10765,"journal":{"name":"Critical Care Medicine","volume":" ","pages":"e1815-e1820"},"PeriodicalIF":6.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144246868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors Influencing Quality of Life After Intensive Care: A Systematic Review and Meta-Analysis.","authors":"Weilin Jiang, Qiqi Ni, Chuchu Zhang, Yuheng Dong, Jia Yi, Ran Yan, Zhenzhen Huang, Li Wang, Weijing Sui, Xiaoyan Gong, Yiyu Zhuang","doi":"10.1097/CCM.0000000000006770","DOIUrl":"10.1097/CCM.0000000000006770","url":null,"abstract":"<p><strong>Objectives: </strong>The factors influencing quality of life (QOL) after intensive care are diverse and complex, and the QOL levels remain unclear. This systematic review and meta-analysis aimed to identify the factors influencing QOL and QOL levels in post-ICU patients.</p><p><strong>Data sources: </strong>We searched eight databases: PubMed, Embase, EBSCOhost, Cochrane Library, Web of Science, China National Knowledge Infrastructure, WeiPu, and WanFang, from inception to October 15, 2024.</p><p><strong>Study selection: </strong>We included observational studies that examined factors influencing QOL in post-ICU patients.</p><p><strong>Data extraction: </strong>Two independent reviewers extracted and recorded the data.</p><p><strong>Data synthesis: </strong>A total of 65 studies, encompassing 17,298 post-ICU patients, met the inclusion criteria. The key pre-ICU factors are advanced age (per 1-yr increase) (β: -0.045 [95% CI, -0.057 to -0.033]) and female gender (odds ratio: 1.104 (95% CI, 1.035-1.177]). The key intra-ICU factors are length of ICU stay (per 1-d increase) (β: -0.012 [95% CI, -0.019 to -0.005]), length of mechanical ventilation (per 1-d increase) (β: -0.005 [95% CI, -0.009 to -0.001]), and length of hospital stay (per 1-d increase) (β: -0.107 [95% CI, -0.161 to -0.054]). The pooled overall QOL score was 58.835 (95% CI, 52.935-64.735), the pooled physical component summary (PCS) score was 49.517 (95% CI, 45.781-53.253), the pooled mental component summary (MCS) score was 53.509 (95% CI, 50.301-56.718), and the pooled overall QOL index was 0.750 (95% CI, 0.713-0.787).</p><p><strong>Conclusions: </strong>Most pre-ICU and intra-ICU factors demonstrated strong associations with post-ICU QOL. The QOL in post-ICU patients remains at a moderate level, with the PCS score indicating greater impairment than the MCS score. Further research is highly recommended to explore effective intervention strategies to improve QOL in post-ICU patients, particularly concerning their physical well-being.</p>","PeriodicalId":10765,"journal":{"name":"Critical Care Medicine","volume":" ","pages":"e1803-e1814"},"PeriodicalIF":6.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12393068/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144636473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Delirium Severity Trajectories in Critically Ill Adults Using the Intensive Care Delirium Screening Checklist: A Population-Based Cohort.","authors":"Heidi Lindroth, Kirsten M Fiest, Chel Hee Lee, Kenny Adefila, Janelle Boram Lee, Sikandar Khan, Babar A Khan, Malaz A Boustani, Karla D Krewulak","doi":"10.1097/CCM.0000000000006768","DOIUrl":"10.1097/CCM.0000000000006768","url":null,"abstract":"<p><strong>Objectives: </strong>The delirium course of critically ill adults can be classified into trajectories based on the severity and duration of delirium as shown by a recent study. It is unknown whether these trajectories and associated outcomes are reproducible. We aimed to define delirium severity trajectories using the Intensive Care Delirium Screening Checklist (ICDSC) and delirium duration and evaluate the association of trajectory membership with clinical characteristics and 30-day post-discharge mortality.</p><p><strong>Design: </strong>Population-based retrospective cohort.</p><p><strong>Setting: </strong>Fourteen medical-surgical ICUs in Alberta, Canada from January 1, 2014, to December 21, 2019.</p><p><strong>Patients: </strong>We included adult patients (≥ 18 yr old) with an ICU length of stay of greater than or equal to 24 hours, an ICDSC score indicating delirium (≥ 4), and 30-day follow-up data were included. Group-based trajectory modeling identified trajectories over a 7-day period with SAS v9.4 (SAS Institute, Cary, NC).</p><p><strong>Interventions: </strong>None.</p><p><strong>Measurements and main results: </strong>Demographic (age, sex) and clinical data (2 × d ICDSC score, comorbidities, illness severity, admission reason, procedures, length of stay, in-hospital, and 30-d post-discharge mortality) were captured from electronic medical records. In total, 21,071 patients were included, with a median age of 59 years (interquartile range, 46-70 yr), 59% male ( n = 12,547), and 3% died at 30 days ( n = 541). The five-trajectory model was selected. These trajectories followed previously defined patterns: 1) Mild-Brief (19.4%); 2); Severe-Rapid Recovers (18.5%); 3) Severe-Slow Recovers (31%); 4) Mild-Accelerating (14.1%); and 5) Severe-Nonrecovers (16.9%). Trajectory membership was not significantly associated with 30-day post-discharge mortality; however, clinically relevant trends were observed.</p><p><strong>Conclusions: </strong>The current study substantiates the proof-of-concept model of five delirium severity trajectories. Trajectory membership did not predict 30-day post-discharge mortality. Further research is needed to understand the associations between trajectory membership, biological-based biomarkers, and patient-relevant outcomes.</p>","PeriodicalId":10765,"journal":{"name":"Critical Care Medicine","volume":" ","pages":"e1748-e1758"},"PeriodicalIF":6.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144495057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Added Value of Late Auditory Evoked Potentials in the Multimodal Prognostication of Patients With Disorders of Consciousness.","authors":"Julie Lévi-Strauss, Sarah Benghanem, Bertrand Hermann, Eléonore Bouchereau, Camille Legouy, Tarek Sharshar, Martine Gavaret, Estelle Pruvost-Robieux","doi":"10.1097/CCM.0000000000006766","DOIUrl":"10.1097/CCM.0000000000006766","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to evaluate the added prognostic value of late auditory evoked potentials (AEPs): mismatch negativity (MMN) and P3, alongside other prognostic markers (electroencephalogram, somatosensory evoked potentials, absent pupillary reflex), in the prognostication of patients with disorders of consciousness. We stratified our analysis based on the type of brain injury: hypoxic-ischemic brain injury (HIBI) vs. non-HIBI.</p><p><strong>Design and setting: </strong>We performed a single-center retrospective study comparing the prognostic values of late AEPs and other neurophysiologic markers in predicting unfavorable outcomes, defined as a Glasgow Outcome Scale-Extended of 1-2 at 3 months, in both HIBI and non-HIBI groups. We compared the prognostic performance of a model including late AEPs to a model including only well-established markers.</p><p><strong>Patients: </strong>We included 148 patients from one institution at the subacute phase after coma onset (median, 20.0 d). The main cause of disorder of consciousness was HIBI in 43.9% of cases, followed by stroke in 31.8%.</p><p><strong>Interventions: </strong>None.</p><p><strong>Measurements and main results: </strong>The absence of P3 and MMN responses had a positive predictive value (PPV) for unfavorable outcome of 92.9% and 88.4% in HIBI, and 67.9% and 57.4% in non-HIBI, respectively. Predictive values of neurophysiologic markers were generally lower in non-HIBI compared with HIBI patients. Specifically, the PPV for unfavorable outcome of electroencephalogram malignant patterns and absent reactivity was significantly higher in HIBI compared with non-HIBI patients (76.9% vs. 31.3%, and 88.9% vs. 33.3%; p = 0.04, respectively). A model including AEPs significantly reduced the differences between individual predicted probabilities and actual outcome, both in the HIBI and non-HIBI contexts.</p><p><strong>Conclusions: </strong>Integrating late AEPs into multimodal assessments enhances the model's predictive performance. Their contribution to neuroprognostication may be particularly relevant in the non-HIBI context, where the predictive values of neurophysiologic tools are lower than in the HIBI context. Late AEPs are interesting and cost-effective tools for neuroprognostication in both HIBI and non-HIBI contexts.</p>","PeriodicalId":10765,"journal":{"name":"Critical Care Medicine","volume":" ","pages":"e1736-e1747"},"PeriodicalIF":6.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144599632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The authors reply.","authors":"Fausto Biancari, Giorgia Bonalumi, Timo Mäkikallio","doi":"10.1097/CCM.0000000000006781","DOIUrl":"https://doi.org/10.1097/CCM.0000000000006781","url":null,"abstract":"","PeriodicalId":10765,"journal":{"name":"Critical Care Medicine","volume":"53 9","pages":"e1861-e1862"},"PeriodicalIF":6.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The authors reply.","authors":"Elizabeth Levy, Gary E Weissman, Meeta Prasad Kerlin, Michael W Sjoding","doi":"10.1097/CCM.0000000000006762","DOIUrl":"https://doi.org/10.1097/CCM.0000000000006762","url":null,"abstract":"","PeriodicalId":10765,"journal":{"name":"Critical Care Medicine","volume":"53 9","pages":"e1849-e1850"},"PeriodicalIF":6.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association Between Driving Pressure and Subsequent Development of Acute Kidney Injury in Acute Respiratory Distress Syndrome.","authors":"Ioannis Andrianopoulos, Panagiotis Kremmydas, Eleni Papoutsi, Eleni N Sertaridou, Kyriaki Parisi, Eleni A Vavouraki, Ilias I Siempos, Stelios Kokkoris","doi":"10.1097/CCM.0000000000006772","DOIUrl":"10.1097/CCM.0000000000006772","url":null,"abstract":"<p><strong>Objectives: </strong>Although preclinical evidence indicates that injurious mechanical ventilation may lead to acute kidney injury (AKI), relevant clinical evidence is limited. We aimed to investigate the association of driving pressure (a marker of injurious mechanical ventilation) with subsequent development of AKI in patients with acute respiratory distress syndrome (ARDS).</p><p><strong>Design: </strong>Secondary analysis of individual patient-level data from seven ARDS Network and Prevention and Early Treatment of Acute Lung Injury (PETAL) Network randomized controlled clinical trials.</p><p><strong>Setting: </strong>Adult ICUs participating in the ARDS Network and PETAL Network trials.</p><p><strong>Patients: </strong>After exclusion of patients with early AKI (i.e., those who met AKI criteria within the first 2 d following ARDS onset), we classified the study population into two groups: \"late AKI\" and \"no AKI.\" The \"late AKI\" group included patients who developed AKI more than 2 days but no longer than 7 days following ARDS onset.</p><p><strong>Interventions: </strong>None.</p><p><strong>Measurements and main results: </strong>Of 5367 patients with ARDS initially enrolled in trials, 2960 patients were included in the main analysis. Late AKI developed in 1000 patients (33.8%). After controlling for confounders, baseline driving pressure was independently associated with development of late AKI (each 1 sd increase in driving pressure was associated with a 35% increase in the odds of late AKI [odds ratio, 1.35; 95% CI, 1.15-1.58]). This result persisted in the sensitivity analysis, which did not exclude patients with early AKI, and in the sensitivity analysis, which included patients who developed AKI later than 7 days following ARDS onset. There was a threshold of driving pressure equal to 15 cm H 2 O for its association with development of late AKI.</p><p><strong>Conclusions: </strong>Driving pressure was associated with subsequent development of AKI in patients with ARDS suggesting that injurious mechanical ventilation may lead to AKI.</p>","PeriodicalId":10765,"journal":{"name":"Critical Care Medicine","volume":" ","pages":"e1770-e1780"},"PeriodicalIF":6.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}