Pub Date : 2026-01-17DOI: 10.1016/j.bja.2025.12.030
Sarah Gaffney
{"title":"Educational outcomes of simulation-based training in regional anaesthesia: assessing educational outcomes. Comment on Br J Anaesth 2025; 134: 523-34.","authors":"Sarah Gaffney","doi":"10.1016/j.bja.2025.12.030","DOIUrl":"https://doi.org/10.1016/j.bja.2025.12.030","url":null,"abstract":"","PeriodicalId":9250,"journal":{"name":"British journal of anaesthesia","volume":"15 1","pages":""},"PeriodicalIF":9.8,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993031","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}
BACKGROUNDMachine learning prediction models require prospective validation to ensure implementation fidelity and feasibility. Our primary objective was to prospectively validate a previously reported postoperative mortality prediction model in inpatients undergoing surgery. Our secondary objective was to evaluate feasibility of a pilot clinical decision support tool.METHODSWe prospectively validated and implemented a random forest machine learning model trained to predict in-hospital mortality using data from a single academic medical centre. A reduced 32-feature model was implemented into the electronic health record (EHR) using a real-time data mart at the same institution. To assess model performance, the area under the receiver operating characteristic curve (AUROC), area under the curve precision-recall (AUCPR), and other performance measures were calculated. To assess feasibility, implementation workflow metrics were evaluated and a survey was administered to anaesthesiologists trained to use the pilot clinical decision support tool.RESULTSThe AUROC for the prospectively implemented model was 0.874 (95% confidence interval [CI] 0.860-0.887), and the AUCPR was 0.111. By comparison, the AUROC for the 58-feature model was 0.925 (95% CI 0.900-0.947), and for ASA physical status the AUROC was 0.814 (95% CI 0.802-0.827) and the AUCPR was 0.103. The implementation demonstrated feasibility through real-time data updates, automated transfer of model outputs to the EHR, and provider survey entries.CONCLUSIONSThis prospective validation and EHR implementation of a previously published random forest machine learning model predicting postoperative in-hospital mortality demonstrated acceptable real-world performance of the implemented model and feasibility of integrating such a system into clinical practice.
机器学习预测模型需要前瞻性验证，以确保实现的保真度和可行性。我们的主要目的是前瞻性地验证先前报道的住院手术患者术后死亡率预测模型。我们的第二个目标是评估试点临床决策支持工具的可行性。方法我们前瞻性地验证并实施了一个随机森林机器学习模型，该模型使用来自单个学术医疗中心的数据进行训练，以预测住院死亡率。利用同一机构的实时数据集市，在电子健康记录（EHR）中实现了一个精简的32个特征模型。为了评估模型的性能，计算了受试者工作特征曲线下面积（AUROC）、曲线下面积精确召回率（AUCPR）和其他性能指标。为了评估可行性，对实施工作流程指标进行了评估，并对接受过使用试点临床决策支持工具培训的麻醉师进行了调查。结果前瞻性实施模型的AUROC为0.874(95%可信区间[CI] 0.860 ~ 0.887)， AUCPR为0.111。相比之下，58个特征模型的AUROC为0.925 (95% CI 0.900 ~ 0.947)， ASA身体状态的AUROC为0.814 (95% CI 0.802 ~ 0.827)， AUCPR为0.103。通过实时数据更新、模型输出自动传输到EHR和供应商调查条目，实现了可行性。结论：对先前发表的预测术后住院死亡率的随机森林机器学习模型进行前瞻性验证和电子病历实施，证明了所实施模型在现实世界中的可接受性能，以及将该系统集成到临床实践中的可行性。
{"title":"Prospective validation and real-time implementation of an automated machine learning postoperative mortality prediction model.","authors":"Theodora Wingert,Tiffany Williams,Briana Syed,Brian Hill,Tristan Grogan,Andrew Young,Zarah Antongiorgi,Valiollah Salari,Alexandre Joosten,Ira Hofer,Eran Halperin,Maxime Cannesson,Eilon Gabel","doi":"10.1016/j.bja.2025.11.042","DOIUrl":"https://doi.org/10.1016/j.bja.2025.11.042","url":null,"abstract":"BACKGROUNDMachine learning prediction models require prospective validation to ensure implementation fidelity and feasibility. Our primary objective was to prospectively validate a previously reported postoperative mortality prediction model in inpatients undergoing surgery. Our secondary objective was to evaluate feasibility of a pilot clinical decision support tool.METHODSWe prospectively validated and implemented a random forest machine learning model trained to predict in-hospital mortality using data from a single academic medical centre. A reduced 32-feature model was implemented into the electronic health record (EHR) using a real-time data mart at the same institution. To assess model performance, the area under the receiver operating characteristic curve (AUROC), area under the curve precision-recall (AUCPR), and other performance measures were calculated. To assess feasibility, implementation workflow metrics were evaluated and a survey was administered to anaesthesiologists trained to use the pilot clinical decision support tool.RESULTSThe AUROC for the prospectively implemented model was 0.874 (95% confidence interval [CI] 0.860-0.887), and the AUCPR was 0.111. By comparison, the AUROC for the 58-feature model was 0.925 (95% CI 0.900-0.947), and for ASA physical status the AUROC was 0.814 (95% CI 0.802-0.827) and the AUCPR was 0.103. The implementation demonstrated feasibility through real-time data updates, automated transfer of model outputs to the EHR, and provider survey entries.CONCLUSIONSThis prospective validation and EHR implementation of a previously published random forest machine learning model predicting postoperative in-hospital mortality demonstrated acceptable real-world performance of the implemented model and feasibility of integrating such a system into clinical practice.","PeriodicalId":9250,"journal":{"name":"British journal of anaesthesia","volume":"98 1","pages":""},"PeriodicalIF":9.8,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994921","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}
Pub Date : 2026-01-17DOI: 10.1016/j.bja.2025.12.042
Roman Sýkora,Jiří Chvojka,Metoděj Renza,František Duška
{"title":"Difficult Airway Society 2025 guidelines for the management of unanticipated difficult tracheal intubation in adults: sugammadex is not a prehospital airway rescue strategy. Comment on Br J Anaesth 2026; 136: 283-307.","authors":"Roman Sýkora,Jiří Chvojka,Metoděj Renza,František Duška","doi":"10.1016/j.bja.2025.12.042","DOIUrl":"https://doi.org/10.1016/j.bja.2025.12.042","url":null,"abstract":"","PeriodicalId":9250,"journal":{"name":"British journal of anaesthesia","volume":"21 1","pages":""},"PeriodicalIF":9.8,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993037","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}
Pub Date : 2026-01-17DOI: 10.1016/j.bja.2025.11.020
Goodarz Kolifarhood, Marc Parisien, Matt Fillingim, Charlise Chen, Yiran Chen, Nikolay Dimitrov, Maha Zidan, Lovéni Hanumunthadu, Peter H Her, Sahir Bhatnagar, Luda Diatchenko, Audrey V Grant
Background: Chronic pain and asthma are associated, but the direction and basis of their genetic and biological relationship remain unclear.
Methods: We conducted genome-wide association studies (GWAS), multi-trait analysis of GWAS (MTAG), polygenic risk score (PRS) prediction, bivariate causal modelling, and Mendelian randomisation (MR) across nine chronic pain traits and three asthma age-of-onset strata (<18, 18-40, and >40 yr for childhood-, adult-, and late-onset asthma, respectively) in 456 958 UK Biobank and 25 275 Canadian Longitudinal Study on Aging participants of European descent. We analysed shared and distinct genetic architecture using gene-, pathway-, tissue-, and cell-type-based enrichment analyses.
Results: Multisite chronic pain (MCP) showed the strongest and most consistent genetic overlap with asthma, with genetic correlation increasing from childhood (rg=0.01) to late-onset asthma (rg=0.40). Estimated causal variants for late-onset asthma (∼1.8 K), and fewer for childhood asthma (∼0.2 K), were nested within a broader MCP profile (∼9.4 K). Using PRS, MR, and longitudinal analyses, we found that MCP contributes causally to late-onset asthma. Top causal variants from MR mapped to GMPPB-RNF123, DCC, and FOXP2. Conditioning by MCP amplified late-onset asthma variant effect sizes using MTAG, and uncovered genes enriched for immune and CNS function across pathways, tissues, and cell types. In contrast, childhood asthma showed immune-specific enrichment alone.
Conclusions: These findings reveal neurological function linking chronic pain to late-onset asthma, distinct from childhood asthma, and highlight a CNS contribution to asthma emerging later in life.
{"title":"Causal evidence linking chronic pain genetics to late-onset asthma via the nervous system.","authors":"Goodarz Kolifarhood, Marc Parisien, Matt Fillingim, Charlise Chen, Yiran Chen, Nikolay Dimitrov, Maha Zidan, Lovéni Hanumunthadu, Peter H Her, Sahir Bhatnagar, Luda Diatchenko, Audrey V Grant","doi":"10.1016/j.bja.2025.11.020","DOIUrl":"10.1016/j.bja.2025.11.020","url":null,"abstract":"<p><strong>Background: </strong>Chronic pain and asthma are associated, but the direction and basis of their genetic and biological relationship remain unclear.</p><p><strong>Methods: </strong>We conducted genome-wide association studies (GWAS), multi-trait analysis of GWAS (MTAG), polygenic risk score (PRS) prediction, bivariate causal modelling, and Mendelian randomisation (MR) across nine chronic pain traits and three asthma age-of-onset strata (<18, 18-40, and >40 yr for childhood-, adult-, and late-onset asthma, respectively) in 456 958 UK Biobank and 25 275 Canadian Longitudinal Study on Aging participants of European descent. We analysed shared and distinct genetic architecture using gene-, pathway-, tissue-, and cell-type-based enrichment analyses.</p><p><strong>Results: </strong>Multisite chronic pain (MCP) showed the strongest and most consistent genetic overlap with asthma, with genetic correlation increasing from childhood (r<sub>g</sub>=0.01) to late-onset asthma (r<sub>g</sub>=0.40). Estimated causal variants for late-onset asthma (∼1.8 K), and fewer for childhood asthma (∼0.2 K), were nested within a broader MCP profile (∼9.4 K). Using PRS, MR, and longitudinal analyses, we found that MCP contributes causally to late-onset asthma. Top causal variants from MR mapped to GMPPB-RNF123, DCC, and FOXP2. Conditioning by MCP amplified late-onset asthma variant effect sizes using MTAG, and uncovered genes enriched for immune and CNS function across pathways, tissues, and cell types. In contrast, childhood asthma showed immune-specific enrichment alone.</p><p><strong>Conclusions: </strong>These findings reveal neurological function linking chronic pain to late-onset asthma, distinct from childhood asthma, and highlight a CNS contribution to asthma emerging later in life.</p>","PeriodicalId":9250,"journal":{"name":"British journal of anaesthesia","volume":" ","pages":""},"PeriodicalIF":9.2,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994182","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}
Pub Date : 2026-01-16DOI: 10.1016/j.bja.2025.12.019
Irma L A Geenen,René Willems,Leonie J P Slegers,Tijs J van den Berg,Anna L M J van der Knijff-van Dortmont,Oscar F C van den Bosch
{"title":"Development and implementation of a ready-to-use intrathecal mixture of hyperbaric bupivacaine and morphine for Caesarean delivery.","authors":"Irma L A Geenen,René Willems,Leonie J P Slegers,Tijs J van den Berg,Anna L M J van der Knijff-van Dortmont,Oscar F C van den Bosch","doi":"10.1016/j.bja.2025.12.019","DOIUrl":"https://doi.org/10.1016/j.bja.2025.12.019","url":null,"abstract":"","PeriodicalId":9250,"journal":{"name":"British journal of anaesthesia","volume":"40 1","pages":""},"PeriodicalIF":9.8,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993038","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}
Pub Date : 2026-01-16DOI: 10.1016/j.bja.2025.12.025
Prem S D Tank,Olivia Jeffries,Maximillian W R Chambers,Eleanor West,Nicholas Bruce
{"title":"Environmental impact of single-use volatile anaesthetic capture devices: estimation of carbon emissions by life cycle assessment.","authors":"Prem S D Tank,Olivia Jeffries,Maximillian W R Chambers,Eleanor West,Nicholas Bruce","doi":"10.1016/j.bja.2025.12.025","DOIUrl":"https://doi.org/10.1016/j.bja.2025.12.025","url":null,"abstract":"","PeriodicalId":9250,"journal":{"name":"British journal of anaesthesia","volume":"38 1","pages":""},"PeriodicalIF":9.8,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993083","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}
Pub Date : 2026-01-16DOI: 10.1016/j.bja.2025.12.016
Tao Chen,Tao Luo
{"title":"Impact of propofol or sevoflurane on the renoprotective effect of remote ischaemic preconditioning in cardiac surgery. Comment on Br J Anaesth 2025; 135: 1626-34.","authors":"Tao Chen,Tao Luo","doi":"10.1016/j.bja.2025.12.016","DOIUrl":"https://doi.org/10.1016/j.bja.2025.12.016","url":null,"abstract":"","PeriodicalId":9250,"journal":{"name":"British journal of anaesthesia","volume":"83 1","pages":""},"PeriodicalIF":9.8,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993033","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：