Predicting Pediatric Risk of Death After Hospitalization

说明(由申请人提供)：为了改善临终关怀、临终关怀和丧亲(P-EOL-B)服务对垂死儿童及其家庭的有益影响，必须完成对垂死儿童的预期识别是一项基本任务，因为如果要及时采用有效的个人干预措施，这种预期识别是必不可少的。我们假设，使用行政卫生服务利用数据，可以合理准确地前瞻性地识别死于复杂慢性病的相当大比例的儿童，并且主动识别可以改善向这些儿童及其家庭提供P-EOL-B护理的情况。因此，我们的目标是：1.开发一种预测方法，该方法将使用管理卫生利用数据将患者分成低、中-低、中-高和高风险组，以计算随后1年的死亡率。2.发展合作关系，利用来自其他背景的数据规划一项更大规模的重复研究，以验证能够在当地和不断发展的儿科保健服务系统的背景下重建这一预测算法的技术。该算法将使用Logistic回归和递归分区来开发，它将对年龄为365天的儿童住院后死亡的概率进行建模。我们将利用宾夕法尼亚州卫生保健成本委员会数据库中包含的行政医院出院数据，这些数据与生命统计记录相关联。有了这一创新的死亡风险预测系统，我们假设医院和医疗保健项目中的P-EOL-B和牧民护理团队将能够识别适合进行护理质量评估和改进的个别患者或患者类别(例如，脑瘫患者和因吸入性肺炎而首次或第二次住院的患者)，并通过有针对性的重症监护协调，将P-EOL-B护理更早地引入这些患者和家庭的综合护理方案中。

DESCRIPTION (provided by applicant): The prospective identification of dying children is an essential task that must be accomplished in order to improve the beneficial impact of palliative, end-of-life, and bereavement (P-EOL-B) services on dying children and their families, since this prospective identification is essential if effective individual-level interventions are to be employed in a timely manner. We hypothesize that using administrative health service utilization data, one can prospectively identify with reasonable accuracy a substantial proportion of children who die from complex chronic conditions, and that proactive identification can improve the delivery of P-EOL-B care to these children and their families. We therefore aim to: 1. Develop a method of prognostication that would use administrative health utilization data to triage patients into low, medium-low, medium-high, and high risk groups for subsequent 1-year mortality. 2. Develop collaborative relationships and plan a larger replication study using data from other contexts, in order to validate techniques that would enable the recreation of this prognostication algorithm in the context of local and evolving systems of pediatric health care services. The algorithm will be developed using logistic regression and recursive partitioning, which will model the probability of dying 365 days after hospitalization among children age. We will utilize administrative hospital discharge data contained in the Pennsylvania Health Care Cost Commission database, linked to vital statistics records. With this innovative mortality risk prediction system in hand, we postulate that P-EOL-B and pastoral care teams in hospitals and health care programs would be able to identify individual patients or classes of patients (eg, those with cerebral palsy and the first or second hospitalization for an aspiration pneumonia) suitable for quality of care assessment and improvement, with targeted intensive care coordination to introduce P-EOL-B care earlier into these patients' and families' comprehensive package of care.