目的:为成人心脏手术后的手术结果建立全面的质量保证模型。
方法:基于在3个高性能医院系统的19家医院中进行的52,792例成人心脏手术,建立手术死亡率模型(n=1,271),中风(n=895),胸骨深部伤口感染(n=122),长时间插管(6,182),肾功能衰竭(1,265),术后住院时间延长(n=5,418),和再操作(n=1693)。随机森林分位数分类,一种为罕见事件的挑战量身定制的方法,和无模型变量优先级筛选用于确定事件的预测因子.
结果:一组术前变量足以模拟几乎所有心脏手术的手术结果,包括老年;晚期症状;左心室,肺,肾,和肝功能障碍;较低的白蛋白;较高的敏锐度;计划手术的复杂性更高。几何平均性能范围从.63到.76。校准覆盖了大范围的概率。持续的风险因素提供了很高的信息含量,它们与结果的关联用部分图可视化。这些风险因素在医院的强度和配置上有所不同,根据虚拟(数字)双胞胎框架内的反事实因果推断确定的患者风险,他们的风险调整结局也是如此.
结论:使用一小部分变量和现代机器学习方法,基于3个示例性医院系统的数据,开发了成人心脏手术后手术死亡率和主要发病率的综合模型.他们提供外科医生,他们的病人,与这些先进的医院系统相比,医院和医院系统具有21世纪的工具,用于评估其风险并提高心脏手术质量。
OBJECTIVE: The study objective was to develop comprehensive quality assurance models for procedural outcomes after adult cardiac surgery.
METHODS: Based on 52,792 cardiac operations in adults performed in 19 hospitals of 3 high-performing hospital systems, models were developed for operative mortality (n = 1271), stroke (n = 895), deep sternal wound infection (n = 122), prolonged intubation (6182), renal failure (1265), prolonged postoperative stay (n = 5418), and reoperations (n = 1693). Random forest quantile classification, a method tailored for challenges of rare events, and model-free variable priority screening were used to identify predictors of events.
RESULTS: A small set of preoperative variables was sufficient to model procedural outcomes for virtually all cardiac operations, including older age; advanced symptoms; left ventricular, pulmonary, renal, and hepatic dysfunction; lower albumin; higher acuity; and greater complexity of the planned operation. Geometric mean performance ranged from .63 to .76. Calibration covered large areas of probability. Continuous risk factors provided high information content, and their association with outcomes was visualized with partial plots. These risk factors differed in strength and configuration among hospitals, as did their risk-adjusted outcomes according to patient risk as determined by counterfactual causal inference within a framework of virtual (digital) twins.
CONCLUSIONS: By using a small set of variables and contemporary machine-learning methods, comprehensive models for procedural operative mortality and major morbidity after adult cardiac surgery were developed based on data from 3 exemplary hospital systems. They provide surgeons, their patients, and hospital systems with 21st century tools for assessing their risks compared with these advanced hospital systems and improving cardiac surgery quality.