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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

基本信息

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
项目类别：
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

项目摘要

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.

抽象的新生儿（≤ 28 天的婴儿），尤其是患有先天性心脏病 (CHD) 的婴儿，是最容易受到伤害的人群。由重症监护护士照顾的弱势群体。大约三分之二的 CHD 新生儿体外循环 (CPB) 后经历意外体温过低。无意的体温过低会损害细胞功能，这可能与该人群中经常报告的不良结果有关。迄今为止，有没有研究检查无意低体温的负担与临床症状之间的关联患有 CHD 的新生儿的结果。这些知识将为未来改善护理提供机会并防止这些脆弱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 CardioAccess 的回顾性数据（费城儿童医院 [CHOP] 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存储库之一（儿科心脏重症监护联盟 [PC4]），以及电子健康记录。使用来自 at 的数据 2015 年至 2019 年间至少有 432 名接受过 CPB 的新生儿，我们将量化其时间进程 CPB 后最初 24-48 小时内每小时的体温轨迹，并评估其与关键临床的关系结果。我们将专门研究无意低体温负担（温度深度和持续时间），这对当前的实践提出了挑战，目前的护理基于维持单一、由共识而非证据驱动的预选温度阈值。单一阈值不是构成温度的复杂性的动态表示。更稳健的输出，例如需要累积低温负担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) 识别 CHD 中不同的时间温度模式 CPB 后的新生儿使用两者：具有基于组的轨迹的密集纵向数据的多级模型造型;以及使用主成分分析和 k- 的无监督机器学习技术是指纵向数据的聚类。 2) 确定低温负担亚组之间的关系/ 该人群中的集群和重要的临床结果。我们的团队在建筑方面拥有丰富的专业知识危重患者不良后果的临床相关且生理上合理的标志物。这项研究符合 NINR 在一生中促进健康和预防疾病的优先事项，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，研究密集型机构（数据分析）和 CHOP（数据分析）条款）。本申请中概述的个性化培训计划支持申请人的职业生涯和学术发展目标成为一名独立的护士研究员。