Use of Predictive Analytics to Quantify Neonatal Hypothermia Burden After Cardiac Surgery

使用预测分析来量化心脏手术后新生儿体温过低的负担

• 批准号: 10650743
• 负责人: Stephanie M Helman
• 金额: $ 3.59万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10650743
• 关键词: Address; Arrhythmia; Award; Birth; Cardiac; Cardiac Surgery procedures; Cardiopulmonary Bypass; Care given by nurses; Caring; Cell physiology; Cessation of life; Characteristics; Childhood; Clinical; Clinical Research; Communication; Complex; Congenital Abnormality; Consensus; Cost of Illness; Critical Care; Critical Illness; Data; Data Analyses; Databases; Defect; Development; Electronic Health Record; Enrollment; Event; Exclusion; Failure; Future; Goals; Health; Healthcare; Hemorrhage; Hospitalization; Hour; Hypoxemia; Impairment; Infant; Institution; Integumentary system; Intensive Care Units; Intervention; Ischemia; Knowledge; Lead; Length of Stay; Link; Logistic Regressions; Longevity; Maps; Mediating; Metabolic acidosis; Modeling; Monitor; Morbidity - disease rate; Neonatal; Nurses; Operative Surgical Procedures; Outcome; Output; PC4 Gene; Patient-Focused Outcomes; Patients; Pattern; Pediatric Hospitals; Philadelphia; Physiological; Platelet aggregation; Population; Predictive Analytics; Prevalence; Prevention; Principal Component Analysis; Privatization; Productivity; Prospective, cohort study; Regulation; Reporting; Research; Research Personnel; Respiratory Failure; Respiratory distress; Risk; Risk Estimate; Safety; School Nursing; Subgroup; Surgical Wound Infection; Survival Analysis; System; Techniques; Temperature; Time; Time trend; Training; Universities; Vasoconstrictor Agents; Vulnerable Populations; Weight Gain; adverse outcome; career; clinical practice; clinically relevant; clinically significant; congenital heart disorder; cost; critical care nursing; database query; early onset; experience; hemodynamics; improved; inclusion criteria; indexing; mortality; multidimensional data; multilevel analysis; natural hypothermia; neonatal outcome; neonate; neutrophil; novel; patient safety; precision medicine; predictive modeling; preemptive intervention; prevent; repository; respiratory; trend; unsupervised learning

ABSTRACT Neonates (infants ≤ 28 days), especially those with congenital heart disease (CHD), are among the most vulnerable populations cared for by critical care nurses. Approximately, two out of three CHD neonates experience unintentional hypothermia after cardiopulmonary bypass (CPB). Unintentional hypothermia impairs cellular function, which can be linked to poor outcomes frequently reported in this population. To date, there are no studies examining the association between the burden of unintentional hypothermia and clinical outcomes in neonates with CHD. This knowledge would render future opportunities to improve nursing care and prevent avoidable safety events in these vulnerable neonates. To address this gap, we propose to use retrospective data from CardioAccess (database local to the Children’s Hospital of Philadelphia [CHOP]), which includes one of the largest multicenter repositories of neonatal cardiac surgery data available to date (Pediatric Cardiac Critical Care Consortium [PC4]), as well as, the electronic health record. Using data from at least 432 neonates who have undergone CPB between 2015 and 2019, we will quantify the time course of hourly temperature trajectories within the initial 24–48 hours after CPB and evaluate their relation to key clinical outcomes. We will specifically study the temporal trends of unintentional hypothermia burden (temperature depth and duration), which challenges current practice, where care is based on maintaining a single, preselected temperature threshold that is driven by consensus, rather than evidence. Single threshold values are not dynamic representations of the complexity that makes up temperature. A more robust output, such as an accumulative hypothermia burden index, is needed to assist clinicians with interpretation of this dynamic indicator of overall health. Our Specific Aims are: 1) Identify distinct temporal temperature patterns in CHD neonates after CPB using both: a multilevel model for intensive longitudinal data with group-based trajectory modeling; and an unsupervised machine learning technique using principal component analysis followed by k- means clustering of longitudinal data. 2) Determine the relationship between hypothermia burden subgroups / clusters and important clinical outcomes in this population. Our team has a demonstrated expertise in building clinically relevant and physiologically plausible markers of adverse outcomes in critically ill patients. This study aligns with the NINR’s priorities of promoting wellness and preventing illness across the lifespan, as well as, using recent advances in precision medicine. The research conducted under this award will take place at the University of Pittsburgh School of Nursing, a research-intensive institution (data analysis), and CHOP (data provision). The personalized training plan outlined in this application, supports the applicant’s career and academic development goals to become an independent nurse researcher.

摘要 新生儿（≤ 28天的婴儿），尤其是先天性心脏病（CHD）， 由重症监护护士照顾的弱势群体。大约三分之二的CHD新生儿 心肺转流术（CPB）后发生意外体温过低。无意的低温损害 细胞功能，这可能与该人群中经常报告的不良结局有关。迄今为止 目前还没有研究探讨无意低温负担与临床 CHD新生儿的结局。这些知识将使未来的机会，以改善护理 并防止这些脆弱的新生儿发生本可避免的安全事件。为了弥补这一差距，我们建议使用 回顾性数据来源于CIMAccess（费城儿童医院[CHOP]的本地数据库）， 其中包括迄今为止最大的新生儿心脏手术数据多中心库之一 （儿科心脏重症监护联盟[PC 4]）以及电子健康记录。使用来自AT的数据 在2015年至2019年期间接受CPB的至少432名新生儿中，我们将量化 CPB后最初24-48小时内的每小时温度轨迹，并评价其与关键临床 结果。我们将专门研究意外低体温负担（温度）的时间趋势 深度和持续时间），这对目前的做法提出了挑战，在目前的做法中，护理是基于维持一个单一的， 预先选定的温度阈值是由共识驱动的，而不是证据。单一阈值 并不是构成温度的复杂性的动态表现。更强大的输出，例如 需要一个累积的低温负荷指数来帮助临床医生解释这种动态 整体健康指标。我们的具体目标是：1）确定不同的时间温度模式在冠心病 CPB后新生儿使用这两种方法：一个基于组轨迹的密集纵向数据的多水平模型 建模;以及使用主成分分析的无监督机器学习技术，然后是k- 意味着纵向数据的聚类。2)确定体温过低负荷亚组/ 在这个人群中的集群和重要的临床结果。我们的团队在建筑领域拥有丰富的专业知识 危重患者不良结局的临床相关和生理学合理标志物。本研究 符合NINR的促进健康和预防疾病的优先事项，以及， 利用精准医疗的最新进展根据该奖项进行的研究将在 研究密集型机构匹兹堡大学护理学院（数据分析）和CHOP（数据 规定）。本申请中概述的个性化培训计划支持申请人的职业生涯， 学术发展目标是成为一名独立的护理研究者。

项目成果

Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence-Powered Graphical User Interface for Intensive Care Unit Instability Decision Support.

让多学科临床用户参与设计人工智能驱动的图形用户界面，以支持重症监护病房不稳定决策。

• DOI: 10.1055/s-0043-1775565
• 发表时间: 2023
• 期刊: Applied clinical informatics
• 影响因子: 2.9
• 作者: Helman,Stephanie;Terry,MarthaAnn;Pellathy,Tiffany;Hravnak,Marilyn;George,Elisabeth;Al-Zaiti,Salah;Clermont,Gilles
• 通讯作者: Clermont,Gilles