Claire J. Swarbrick , Helen A. Blake , Peter Martin , Judith S.L. Partridge , Iain K. Moppett
{"title":"SNAP-3中有向无环图最小化偏倚和优化因果推断:一项老年外科患者虚弱、多病和谵妄的观察性队列研究。","authors":"Claire J. Swarbrick , Helen A. Blake , Peter Martin , Judith S.L. Partridge , Iain K. Moppett","doi":"10.1016/j.bja.2025.04.027","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>The 3rd Sprint National Anaesthesia Project (SNAP-3) aims to describe the impact of frailty, multimorbidity, and delirium, and their management, on outcomes after surgery within the older surgical population. Causal diagrams, such as directed acyclic graphs (DAGs), are a useful tool for visually representing relationships between variables and for clarifying the causal assumptions underlying a chosen statistical model.</div></div><div><h3>Methods</h3><div>A description of how the SNAP-3 cohort study's DAGs were developed is provided. DAGs have been created for the exposure–outcome relationships between frailty, multimorbidity, and delirium (as an exposure) with postoperative outcomes (length of hospital stay, delirium, morbidity, mortality). DAGs were created following the approach of ‘Evidence synthesis for constructing directed acyclic graphs’, and revised after independent clinical expert input.</div></div><div><h3>Results</h3><div>DAGs provide visual representations of assumptions made, and provide an objective approach to appropriate statistical adjustments. Key nodes within all the DAGs included age, dementia, genetic predisposition, hearing and visual impairment, length of stay, malignancy, operative severity, polypharmacy, postoperative perioperative medicine service, preoperative clinic review, sex, social deprivation, urgency, with delirium, frailty, multimorbidity, interaction, morbidity acting as exposures, or outcomes in certain DAGs.</div></div><div><h3>Conclusions</h3><div>DAGs provide a transparent framework for statistical decision-making in observational research. We provide an overview of DAGs using the SNAP-3 DAGs as examples to explain fundamental concepts for developing and using causal diagrams. This overview acknowledges the complexities of exploring clinical relationships and the assumptions that are necessary, providing an opportunity for critique of the relationships described and refinements for future studies.</div></div>","PeriodicalId":9250,"journal":{"name":"British journal of anaesthesia","volume":"135 1","pages":"Pages 177-187"},"PeriodicalIF":9.1000,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Directed acyclic graphs to minimise bias and optimise causal inference in SNAP-3: an observational cohort study of frailty, multimorbidity, and delirium in older surgical patients\",\"authors\":\"Claire J. Swarbrick , Helen A. Blake , Peter Martin , Judith S.L. Partridge , Iain K. Moppett\",\"doi\":\"10.1016/j.bja.2025.04.027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>The 3rd Sprint National Anaesthesia Project (SNAP-3) aims to describe the impact of frailty, multimorbidity, and delirium, and their management, on outcomes after surgery within the older surgical population. Causal diagrams, such as directed acyclic graphs (DAGs), are a useful tool for visually representing relationships between variables and for clarifying the causal assumptions underlying a chosen statistical model.</div></div><div><h3>Methods</h3><div>A description of how the SNAP-3 cohort study's DAGs were developed is provided. DAGs have been created for the exposure–outcome relationships between frailty, multimorbidity, and delirium (as an exposure) with postoperative outcomes (length of hospital stay, delirium, morbidity, mortality). DAGs were created following the approach of ‘Evidence synthesis for constructing directed acyclic graphs’, and revised after independent clinical expert input.</div></div><div><h3>Results</h3><div>DAGs provide visual representations of assumptions made, and provide an objective approach to appropriate statistical adjustments. Key nodes within all the DAGs included age, dementia, genetic predisposition, hearing and visual impairment, length of stay, malignancy, operative severity, polypharmacy, postoperative perioperative medicine service, preoperative clinic review, sex, social deprivation, urgency, with delirium, frailty, multimorbidity, interaction, morbidity acting as exposures, or outcomes in certain DAGs.</div></div><div><h3>Conclusions</h3><div>DAGs provide a transparent framework for statistical decision-making in observational research. We provide an overview of DAGs using the SNAP-3 DAGs as examples to explain fundamental concepts for developing and using causal diagrams. This overview acknowledges the complexities of exploring clinical relationships and the assumptions that are necessary, providing an opportunity for critique of the relationships described and refinements for future studies.</div></div>\",\"PeriodicalId\":9250,\"journal\":{\"name\":\"British journal of anaesthesia\",\"volume\":\"135 1\",\"pages\":\"Pages 177-187\"},\"PeriodicalIF\":9.1000,\"publicationDate\":\"2025-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"British journal of anaesthesia\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0007091225002697\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ANESTHESIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"British journal of anaesthesia","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0007091225002697","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ANESTHESIOLOGY","Score":null,"Total":0}
Directed acyclic graphs to minimise bias and optimise causal inference in SNAP-3: an observational cohort study of frailty, multimorbidity, and delirium in older surgical patients
Background
The 3rd Sprint National Anaesthesia Project (SNAP-3) aims to describe the impact of frailty, multimorbidity, and delirium, and their management, on outcomes after surgery within the older surgical population. Causal diagrams, such as directed acyclic graphs (DAGs), are a useful tool for visually representing relationships between variables and for clarifying the causal assumptions underlying a chosen statistical model.
Methods
A description of how the SNAP-3 cohort study's DAGs were developed is provided. DAGs have been created for the exposure–outcome relationships between frailty, multimorbidity, and delirium (as an exposure) with postoperative outcomes (length of hospital stay, delirium, morbidity, mortality). DAGs were created following the approach of ‘Evidence synthesis for constructing directed acyclic graphs’, and revised after independent clinical expert input.
Results
DAGs provide visual representations of assumptions made, and provide an objective approach to appropriate statistical adjustments. Key nodes within all the DAGs included age, dementia, genetic predisposition, hearing and visual impairment, length of stay, malignancy, operative severity, polypharmacy, postoperative perioperative medicine service, preoperative clinic review, sex, social deprivation, urgency, with delirium, frailty, multimorbidity, interaction, morbidity acting as exposures, or outcomes in certain DAGs.
Conclusions
DAGs provide a transparent framework for statistical decision-making in observational research. We provide an overview of DAGs using the SNAP-3 DAGs as examples to explain fundamental concepts for developing and using causal diagrams. This overview acknowledges the complexities of exploring clinical relationships and the assumptions that are necessary, providing an opportunity for critique of the relationships described and refinements for future studies.
期刊介绍:
The British Journal of Anaesthesia (BJA) is a prestigious publication that covers a wide range of topics in anaesthesia, critical care medicine, pain medicine, and perioperative medicine. It aims to disseminate high-impact original research, spanning fundamental, translational, and clinical sciences, as well as clinical practice, technology, education, and training. Additionally, the journal features review articles, notable case reports, correspondence, and special articles that appeal to a broader audience.
The BJA is proudly associated with The Royal College of Anaesthetists, The College of Anaesthesiologists of Ireland, and The Hong Kong College of Anaesthesiologists. This partnership provides members of these esteemed institutions with access to not only the BJA but also its sister publication, BJA Education. It is essential to note that both journals maintain their editorial independence.
Overall, the BJA offers a diverse and comprehensive platform for anaesthetists, critical care physicians, pain specialists, and perioperative medicine practitioners to contribute and stay updated with the latest advancements in their respective fields.