目的:展示将美国胸外科协会质量网关(AQG)结果模型应用于成人心脏手术质量保证的外科医生案例研究。
方法:该案例研究包括2001年至2023年由一名外科医生在克利夫兰诊所对成年人进行的6,989例心脏和胸主动脉手术。AQG模型用于预测手术死亡率和主要发病率的预期概率。并比较医院的结果,手术类型,风险概况,和使用虚拟(数字)孪生因果推断的个体风险因素水平。这些模型基于在3个高性能医院系统的19家医院进行的52,792例心脏手术后的手术结果,总医院死亡率为2.0%。通过先进的机器学习分析罕见的事件。
结果:对于个别外科医生,他们的病人,医院,和医院系统,外科医生案例研究表明,AQG提供了整个心脏手术的预期结果,从单组件主要操作到复杂的多组件再操作。为患者说明了基于虚拟双胞胎的质量改进的可行机会,外科医生,医院,风险简介组,操作,和其他医院的风险因素。
结论:使用最少的数据收集和使用高级机器学习开发的模型,本案例研究表明,在几乎所有成人心脏手术后,手术死亡率和主要发病率的概率都是存在的.它展示了21世纪因果推理(虚拟[数字]孪生)工具的实用性,用于评估外科医生问“我做得如何?”他们的患者问“我的机会是多少?”和职业问“我们如何变得更好?”
OBJECTIVE: To demonstrate applying American Association for Thoracic Surgery Quality Gateway (AQG) outcomes models to a Surgeon Case
Study of quality assurance in adult cardiac surgery.
METHODS: The case
study includes 6,989 cardiac and thoracic aorta operations performed in adults at Cleveland Clinic by one surgeon from 2001 to 2023. AQG models were used to predict expected probabilities for operative mortality and major morbidity, and to compare hospital outcomes, surgery type, risk profile, and individual risk-factor levels using virtual (digital) twin causal inference. These models were based on postoperative procedural outcomes after 52,792 cardiac operations performed in 19 hospitals of 3 high-performing hospital systems with overall hospital mortality of 2.0%, analyzed by advanced machine learning for rare events.
RESULTS: For individual surgeons, their patients, hospitals, and hospital systems, the Surgeon Case
Study demonstrated that AQG provides expected outcomes across the entire spectrum of cardiac surgery, from single-component primary operations to complex multi-component reoperations. Actionable opportunities for quality improvement based on virtual twins is illustrated for patients, surgeons, hospitals, risk profile groups, operations, and risk factors vis-à-vis other hospitals.
CONCLUSIONS: Using minimal data collection and models developed using advanced machine learning, this case
study shows that probabilities can be generated for operative mortality and major morbidity after virtually all adult cardiac operations. It demonstrates the utility of 21st century causal inference (virtual [digital] twin) tools for assessing quality for surgeons asking \"How am I doing?\" their patients asking \"What are my chances?\" and the profession asking \"How can we get better?\"