Bayesian networks identify determinants of outcomes following cardiac surgery in a UK population

BMC Cardiovascular Disorders

Research

Sarah Berger Veith¹  Akshay J. Patel²  Giles Roberts³  Khurum Mazhar³  Lognathen Balacumaraswami³  Saifullah Mohamed³  Qamar Abid³  Marko Raseta³  Richard Warwick³ 

[1] Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany;Institute of Immunology and Immunotherapy, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK;Royal Stoke University Hospital, Stoke on Trent, UK;

关键词: Bayesian network;    Risk stratification;    EuroSCORE;    Cardiac surgery;    Outcomes;   

DOI  :  10.1186/s12872-023-03100-6

 received in 2022-05-29, accepted in 2023-01-30,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundTraditional risk stratification tools do not describe the complex principle determinant relationships that exist amongst pre-operative and peri-operative factors and their influence on cardiac surgical outcomes. This paper reports on the use of Bayesian networks to investigate such outcomes.MethodsData were prospectively collected from 4776 adult patients undergoing cardiac surgery at a single UK institute between April 2012 and May 2019. Machine learning techniques were used to construct Bayesian networks for four key short-term outcomes including death, stroke and renal failure.ResultsDuration of operation was the most important determinant of death irrespective of EuroSCORE. Duration of cardiopulmonary bypass was the most important determinant of re-operation for bleeding. EuroSCORE was predictive of new renal replacement therapy but not mortality.ConclusionsMachine-learning algorithms have allowed us to analyse the significance of dynamic processes that occur between pre-operative and peri-operative elements. Length of procedure and duration of cardiopulmonary bypass predicted mortality and morbidity in patients undergoing cardiac surgery in the UK. Bayesian networks can be used to explore potential principle determinant mechanisms underlying outcomes and be used to help develop future risk models.

【 授权许可】

CC BY   
© The Author(s) 2023

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