背景:危重先天性心脏病(cCHD)-需要在生命的第一年进行心脏介入治疗才能生存-在全球范围内,每1000名活产儿中有2-3人发生。在关键的围手术期,儿科重症监护病房(PICU)的强化多模式监测是必要的,因为它们的器官-特别是大脑-可能由于血液动力学和呼吸事件而严重受伤。这些24/7临床数据流产生大量的高频数据,由于cCHD固有的变化和动态生理学,这在解释方面具有挑战性。通过先进的数据科学算法,这些动态数据可以浓缩为可理解的信息,减少医疗团队的认知负荷,并通过自动检测临床恶化来提供数据驱动的监测支持,这可能有助于及时干预。
目的:本研究旨在为PICUcCHD患者开发一种临床恶化检测算法。
方法:回顾,大脑区域血氧饱和度(rSO2)的同步每秒数据和4个重要参数(呼吸频率,心率,氧饱和度,和侵入性平均血压)在乌得勒支大学医学中心收治的cCHD新生儿中,荷兰,在2002年至2018年之间被提取。根据入院期间的平均血氧饱和度对患者进行分层,以说明无花性和紫红色cCHD之间的生理差异。每个子集用于训练我们的算法,将数据分类为稳定的,不稳定,或传感器功能障碍。该算法旨在检测分层亚群异常的参数组合以及与患者唯一基线的显著偏差,进一步分析以区分临床改善和恶化。新数据用于测试,可视化的细节,并由儿科重症医师内部验证。
结果:回顾性查询在78和10个新生儿中获得了每秒4600小时和209小时的数据,分别,培训和测试目的。在测试过程中,稳定发作发生153次,其中134人(88%)被正确检测到。在观察到的57次发作中,有46次(81%)正确记录了不稳定的发作。在测试中错过了12次专家确认的不稳定发作。时间百分比准确度分别为93%和77%,分别,稳定和不稳定的事件。共检测到138例感觉功能障碍,其中130人(94%)是正确的。
结论:在这项概念验证研究中,开发了一种临床恶化检测算法,并进行了回顾性评估,以对临床稳定性和不稳定性进行分类,考虑到新生儿cCHD的异质性,实现合理的表现。基线的组合分析(即,特定于患者的)偏差和同时的参数移位(即,针对特定人群)的证据在增强对异质危重儿科人群的适用性方面将是有希望的。经过前瞻性验证,当前和可比的模型可能,在未来,用于临床恶化的自动检测,并最终为医疗团队提供数据驱动的监测支持,允许及时干预。
BACKGROUND: Critical congenital heart disease (cCHD)-requiring cardiac intervention in the first year of life for survival-occurs globally in 2-3 of every 1000 live births. In the critical perioperative period, intensive multimodal monitoring at a pediatric intensive care unit (PICU) is warranted, as their organs-especially the brain-may be severely injured due to hemodynamic and respiratory events. These 24/7 clinical data streams yield large quantities of high-frequency data, which are challenging in terms of interpretation due to the varying and dynamic physiology innate to cCHD. Through advanced data science algorithms, these dynamic data can be condensed into comprehensible information, reducing the cognitive load on the medical team and providing data-driven monitoring support through automated detection of clinical deterioration, which may facilitate timely intervention.
OBJECTIVE: This study aimed to develop a clinical deterioration detection algorithm for PICU patients with cCHD.
METHODS: Retrospectively, synchronous per-second data of cerebral regional oxygen saturation (rSO2) and 4 vital parameters (respiratory rate, heart rate, oxygen saturation, and invasive mean blood pressure) in neonates with cCHD admitted to the University Medical Center Utrecht, the Netherlands, between 2002 and 2018 were extracted. Patients were stratified based on mean oxygen saturation during admission to account for physiological differences between acyanotic and cyanotic cCHD. Each subset was used to train our algorithm in classifying data as either stable, unstable, or sensor dysfunction. The algorithm was designed to detect combinations of parameters abnormal to the stratified subpopulation and significant deviations from the patient\'s unique baseline, which were further analyzed to distinguish clinical improvement from deterioration. Novel data were used for testing, visualized in detail, and internally validated by pediatric intensivists.
RESULTS: A retrospective query yielded 4600 hours and 209 hours of per-second data in 78 and 10 neonates for, respectively, training and testing purposes. During testing, stable episodes occurred 153 times, of which 134 (88%) were correctly detected. Unstable episodes were correctly noted in 46 of 57 (81%) observed episodes. Twelve expert-confirmed unstable episodes were missed in testing. Time-percentual accuracy was 93% and 77% for, respectively, stable and unstable episodes. A total of 138 sensorial dysfunctions were detected, of which 130 (94%) were correct.
CONCLUSIONS: In this proof-of-concept study, a clinical deterioration detection algorithm was developed and retrospectively evaluated to classify clinical stability and instability, achieving reasonable performance considering the heterogeneous population of neonates with cCHD. Combined analysis of baseline (ie, patient-specific) deviations and simultaneous parameter-shifting (ie, population-specific) proofs would be promising with respect to enhancing applicability to heterogeneous critically ill pediatric populations. After prospective validation, the current-and comparable-models may, in the future, be used in the automated detection of clinical deterioration and eventually provide data-driven monitoring support to the medical team, allowing for timely intervention.