Royal Stoke University Hospital, Stoke on Trent, UK.
Institute of Immunology and Immunotherapy, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
BMC Cardiovasc Disord. 2023 Feb 6;23(1):70. doi: 10.1186/s12872-023-03100-6.
Traditional 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.
Data 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.
Duration 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.
Machine-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.
传统的风险分层工具无法描述术前和围手术期因素之间存在的复杂原则决定关系，以及它们对心脏手术结果的影响。本文报告了使用贝叶斯网络来研究这些结果。
数据是从 2012 年 4 月至 2019 年 5 月期间在英国一家机构接受心脏手术的 4776 名成年患者中前瞻性收集的。机器学习技术被用于构建四个关键短期结果（包括死亡、中风和肾衰竭）的贝叶斯网络。
手术持续时间是无论 EuroSCORE 如何，死亡的最重要决定因素。体外循环持续时间是出血再手术的最重要决定因素。EuroSCORE 预测新的肾脏替代治疗，但不预测死亡率。
机器学习算法使我们能够分析发生在术前和围手术期因素之间的动态过程的意义。手术时间和体外循环时间可预测英国心脏手术患者的死亡率和发病率。贝叶斯网络可用于探索潜在的决定机制，用于帮助开发未来的风险模型。
