Postoperative critical care management of congenital heart disease patients requires prompt intervention when the patient deviates significantly from clinician‐determined vital sign and hemodynamic goals. Current monitoring systems only allow for static thresholds to be set on individual variables, despite the expectations that these signals change as the patient recovers and that variables interact. To address this incongruency, we have employed statistical process monitoring (SPM) techniques originally developed to monitor batch industrial processes to monitor high‐frequency vital sign and hemodynamic data to establish multivariate trajectory maps for patients with d‐transposition of the great arteries following the arterial switch operation. In addition to providing multivariate trajectory maps, the multivariate control charts produced by the SPM framework allow for assessment of adherence to the desired trajectory at each time point as the data is collected. Control charts based on slow feature analysis were compared with those based on principal component analysis. Alarms generated by the multivariate control charts are discussed in the context of the available clinical documentation.

中文翻译：

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统计过程监测创建小儿心脏手术后的血流动力学轨迹图：动脉转换手术的案例研究


当患者明显偏离临床医生确定的生命体征和血流动力学目标时，先天性心脏病患者的术后重症监护管理需要及时干预。当前的监测系统只允许对单个变量设置静态阈值，尽管人们预期这些信号会随着患者的康复而变化并且变量会相互作用。为了解决这种不一致问题，我们采用了最初开发的统计过程监测 （SPM） 技术来监测批处理工业过程，以监测高频生命体征和血流动力学数据，从而为动脉转位手术后大动脉 d 转位患者建立多变量轨迹图。除了提供多元轨迹图外，SPM 框架生成的多元控制图还允许在收集数据时评估每个时间点对所需轨迹的遵守情况。将基于慢特征分析的控制图与基于主成分分析的控制图进行比较。多变量控制图生成的警报在可用临床文档的上下文中进行讨论。