{"title":"Artificial intelligence for early detection and prevention of ventilator-induced lung injury: a scoping review","authors":"Valentina Quevedo Sánchez,Alejandra Charry Ávila,Jeronimo Cárdenas Montoya,Erwin Hernando Hernández Rincon","doi":"10.1186/s13054-026-06354-6","DOIUrl":"https://doi.org/10.1186/s13054-026-06354-6","url":null,"abstract":"Abstract Background The integration of artificial intelligence (AI) into intensive care unit (ICU) ventilator management has attracted growing research interest, with studies exploring applications ranging from surveillance and decision support toward more autonomous ventilatory adjustment. Whether this represents a clinically validated transition from reactive to proactive ventilation management remains to be established through prospective evidence. This review maps the available literature on these developments in the context of ventilator-induced lung injury (VILI), a heterogeneous syndrome including barotrauma, volutrauma, atelectrauma, and biotrauma. This review maps AI applications relevant to VILI detection and prevention, rather than studies that directly demonstrate VILI prevention. Methods This scoping review mapped and synthesized evidence on the use of AI-based systems for the early detection and prevention of VILI in critically ill adults receiving invasive mechanical ventilation. Following Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, a systematic search was conducted across PubMed, Scopus, Web of Science, ScienceDirect, Google Scholar, and the Biblioteca Virtual en Salud (BVS) for literature published between 2010 and 2026. Results A total of 52 studies met full-text inclusion criteria. Most AI applications focused on automated detection of patient-ventilator asynchronies, prediction of complications related to acute respiratory distress syndrome (ARDS), and optimization of lung-protective ventilation parameters through machine learning and reinforcement learning approaches. Although AI-based systems may support more individualized ventilatory management strategies, their clinical implementation remains limited by the lack of external validation, methodological heterogeneity, and scarce evidence in low- and middle-income settings. Conclusions The evidence identified is predominantly technical, retrospective and exploratory, and rests largely on surrogate physiological or classification endpoints. High diagnostic accuracy, or improvement in surrogate parameters, does not by itself constitute evidence that these systems prevent VILI or improve patient-centred outcomes. Current evidence therefore supports the promise of these applications and the prioritization of prospective research, but not their routine clinical implementation.","PeriodicalId":10811,"journal":{"name":"Critical Care","volume":"58 1","pages":""},"PeriodicalIF":15.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149088267","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}
Critical CarePub Date : 2026-09-30DOI: 10.1186/s13054-026-06358-2
Amit Kansal,Jos M. Latour,Sumeet Rai,Craig M. Coopersmith,Kay Choong See,Raymond Dominic Savio,Louis Trent,Gloria M. Rodriguez-Vega,Deepak Bhonagiri,Srinivas Samavedam,Nitin Arora,Ahsina Jahan Lopa,Jorge Hidalgo,Maurizio Cecconi,Monika Gulati,Julia Wendon,Andrew Conway Morris,Michelle S. Chew,Vinay M. Nadkarni,Jerry J. Zimmerman,Rajesh C. Mishra
{"title":"Barriers and facilitators to successful implementation of cost-effective interventions and the development of the Achieving Cost-Effectiveness in ICU (ACE-ICU) self-assessment toolkit: an international, multidisciplinary modified eDelphi study","authors":"Amit Kansal,Jos M. Latour,Sumeet Rai,Craig M. Coopersmith,Kay Choong See,Raymond Dominic Savio,Louis Trent,Gloria M. Rodriguez-Vega,Deepak Bhonagiri,Srinivas Samavedam,Nitin Arora,Ahsina Jahan Lopa,Jorge Hidalgo,Maurizio Cecconi,Monika Gulati,Julia Wendon,Andrew Conway Morris,Michelle S. Chew,Vinay M. Nadkarni,Jerry J. Zimmerman,Rajesh C. Mishra","doi":"10.1186/s13054-026-06358-2","DOIUrl":"https://doi.org/10.1186/s13054-026-06358-2","url":null,"abstract":"Abstract Background Cost-effective interventions balance clinical benefits with economic considerations. Several cost-effective interventions in intensive care unit (ICU) settings have been identified, yet little is known about optimal implementation strategies. The aim of this study was to identify barriers and facilitators to the successful implementation of cost-effective interventions in ICUs and to develop a self-assessment toolkit to guide their implementation. Methods We used a 3-round modified eDelphi design involving ICU healthcare professionals (HCPs), with a focus on multinational and multidisciplinary participation. Round 1 was a qualitative survey with two open-ended questions. The qualitative analysis of Round 1 was conducted using an adapted framework analysis. Round 2 included 38 questions, 29 quantitative and 9 qualitative, divided into 5 domains. Round 3 involved only the Steering Committee members to reach consensus on the statements to retain in the final output. This was followed by iterative discussions to provide actionable insights and develop a self-assessment implementation toolkit, the Achieving Cost-Effectiveness in ICUs (ACE-ICU) toolkit. Results There were 366 respondents in Round 1, and 161 (44%) in Round 2. Approximately two-fifths of respondents were from low- and middle-income countries defined by World Bank-endorsed gross national income per capita. This was followed by Round 3 of the Steering Committee members ( n = 21). Round 1 and 2 identified 29 key barriers and facilitators across 5 domains: (1) individual HCP-related issues; (2) institutional issues; (3) infrastructure and systems; (4) governance and research; (5) patient and family engagement. Notable similarity was observed across country categories. In Round 3, the consolidated 12-point self-assessment ACE-ICU toolkit was developed and agreed upon, including knowledge, skills, and attitude of HCPs, training standards, budget allocation and implementation within allocated budget, ICU leadership team readiness to embrace cost-effective practices, infrastructure and systems, governance, regulations and incentives, and patient and family engagement. Conclusion Our study identified 29 key barriers and facilitators, along with actionable guidance to successfully implement cost-effective interventions in ICUs. The observed similarities across country categories suggest that establishing a global framework for this purpose is feasible. The proposed 12-point self-assessment ACE-ICU toolkit will help stakeholders to identify and implement successfully cost-effective interventions in their local ICU context. Trial registration Clinical trial number: not applicable. Open Science Framework (OSF) Registration DOI: https://doi.org/10.17605/OSF.IO/6U3FZ . Clinical trial number Not applicable because this study utilized a Delphi consensus methodology among an expert panel and did not involve prospective assignment of human participants to clinical interventions.","PeriodicalId":10811,"journal":{"name":"Critical Care","volume":"11 1","pages":""},"PeriodicalIF":15.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149088246","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}
Critical CarePub Date : 2026-09-29DOI: 10.1186/s13054-026-06290-5
Baptiste Duchamp,Fabrice Vallée,Jérôme Cartailler,Romain Barthélémy,Benjamin G. Chousterman,Manuel Kindermans
{"title":"Continuous tissue CO₂ monitoring for early microcirculatory assessment across a broad ICU population : a descriptive and prognostic study","authors":"Baptiste Duchamp,Fabrice Vallée,Jérôme Cartailler,Romain Barthélémy,Benjamin G. Chousterman,Manuel Kindermans","doi":"10.1186/s13054-026-06290-5","DOIUrl":"https://doi.org/10.1186/s13054-026-06290-5","url":null,"abstract":"","PeriodicalId":10811,"journal":{"name":"Critical Care","volume":"32 1","pages":""},"PeriodicalIF":15.1,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149088270","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}
Critical CarePub Date : 2026-09-29DOI: 10.1186/s13054-026-06333-x
Tarso Augusto Duenhas Accorsi, Renato Carneiro de Freitas Chaves, Carlos Henrique Sartorato Pedrotti, Danilo Teixeira Noritomi, Otavio Tavares Ranzani, Adriano José Pereira
{"title":"Tele-ICU for patients after cardiac surgery: experience from the TELESCOPE trial.","authors":"Tarso Augusto Duenhas Accorsi, Renato Carneiro de Freitas Chaves, Carlos Henrique Sartorato Pedrotti, Danilo Teixeira Noritomi, Otavio Tavares Ranzani, Adriano José Pereira","doi":"10.1186/s13054-026-06333-x","DOIUrl":"10.1186/s13054-026-06333-x","url":null,"abstract":"","PeriodicalId":10811,"journal":{"name":"Critical Care","volume":"30 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13625340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149079941","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}
Critical CarePub Date : 2026-09-29DOI: 10.1186/s13054-026-05980-4
Chiang Dung-Hung, Tian Cong, Jiang Zeyu, Ou-Yang Yu-Shan, Lin Yung-Yang
{"title":"Correction: External validation of a machine learning model to predict hemodynamic instability in intensive care unit.","authors":"Chiang Dung-Hung, Tian Cong, Jiang Zeyu, Ou-Yang Yu-Shan, Lin Yung-Yang","doi":"10.1186/s13054-026-05980-4","DOIUrl":"10.1186/s13054-026-05980-4","url":null,"abstract":"","PeriodicalId":10811,"journal":{"name":"Critical Care","volume":"30 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13621493/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149079969","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}
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
