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: 10415862
• 负责人: Stephanie M Helman
• 金额: $ 4.93万
• 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 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10415862
• 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; 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; Play; 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; Role; 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; base; 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(婴儿≤28天)，特别是那些患有先天性心脏病的人，是最多的 由重症监护护士照顾的弱势人群。大约每三个新生儿中就有两个患有冠心病 在体外循环(CPB)后出现意外体温过低。无意的体温过低会损害 细胞功能，这可能与这一人群中经常报告的不良结局有关。到目前为止，有 是否没有研究研究非故意体温过低的负担与临床之间的关系？ 先天性心脏病新生儿的结局。这一知识将为改善护理提供未来的机会 并防止这些脆弱的新生儿发生可避免的安全事件。为了解决这一差距，我们建议使用 来自MedicoAccess(费城儿童医院本地数据库[CHOP])的回顾数据， 它包括迄今为止可用的最大的多中心新生儿心脏手术数据存储库之一 (儿科心脏危重护理联合会[PC4])，以及电子健康记录。使用来自以下位置的数据 2015年至2019年期间至少有432名新生儿接受了体外循环，我们将量化 体外循环后最初24-48小时内的每小时体温轨迹及其与关键临床的关系 结果。我们将具体研究非故意低温负担(温度)的时间趋势 深度和持续时间)，这对当前的做法提出了挑战，在目前的做法中，护理基于保持单一的、 预先选定的温度阈值是由共识驱动的，而不是证据。单阈值 并不是构成温度的复杂性的动态表示。更稳健的产出，例如 需要一个累积低温负荷指数来帮助临床医生解释这种动态。 整体健康状况的指标。我们的具体目标是：1)确定CHD的不同的时间温度模式 使用两者的CPB术后新生儿：基于群体轨迹的密集纵向数据的多水平模型 以及一种基于主成分分析的无监督机器学习技术。 指的是纵向数据的聚类。2)确定低温负荷亚组/之间的关系 在这一人群中的聚集性和重要的临床结果。我们的团队在建造建筑方面拥有公认的专业知识 危重病患者不良结局的临床相关和生理上可信的标记物。本研究 与NINR促进健康和预防终身疾病的优先事项一致，以及， 利用精准医学的最新进展。根据该奖项进行的研究将在 匹兹堡大学护理学院，一家研究密集型机构(数据分析)和CHOP(数据 条文)。此申请表中概述的个性化培训计划，支持申请者的职业生涯和 学术发展目标是成为一名独立的护士研究员。