儿童死亡风险评分：2015年更新版
The Pediatric Risk of Mortality Score: Update 2015.
作者信息
Pollack Murray M, Holubkov Richard, Funai Tomohiko, Dean J Michael, Berger John T, Wessel David L, Meert Kathleen, Berg Robert A, Newth Christopher J L, Harrison Rick E, Carcillo Joseph, Dalton Heidi, Shanley Thomas, Jenkins Tammara L, Tamburro Robert
机构信息
1Department of Pediatrics, Children's National Medical Center and the George Washington University School of Medicine and Health Sciences, Washington DC. 2Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT. 3Department of Pediatrics, Children's National Medical Center, Washington DC. 4Department of Pediatrics, Children's Hospital of Michigan, Detroit, MI. 5Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA. 6Department of Anesthesiology and Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA. 7Department of Pediatrics, University of California at Los Angeles, Los Angeles, CA. 8Department of Critical Care Medicine, Children's Hospital of Pittsburgh, Pittsburgh, PA. 9Department of Child Health, Phoenix Children's Hospital and University of Arizona College of Medicine-Phoenix, Phoenix, AZ. 10Department of Pediatrics, University of Michigan, Ann Arbor, MI. 11Pediatric Trauma and Critical Illness Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), the National Institutes of Health (NIH), Bethesda, MD.
出版信息
Pediatr Crit Care Med. 2016 Jan;17(1):2-9. doi: 10.1097/PCC.0000000000000558.
OBJECTIVES
Severity of illness measures have long been used in pediatric critical care. The Pediatric Risk of Mortality is a physiologically based score used to quantify physiologic status, and when combined with other independent variables, it can compute expected mortality risk and expected morbidity risk. Although the physiologic ranges for the Pediatric Risk of Mortality variables have not changed, recent Pediatric Risk of Mortality data collection improvements have been made to adapt to new practice patterns, minimize bias, and reduce potential sources of error. These include changing the outcome to hospital survival/death for the first PICU admission only, shortening the data collection period and altering the Pediatric Risk of Mortality data collection period for patients admitted for "optimizing" care before cardiac surgery or interventional catheterization. This analysis incorporates those changes, assesses the potential for Pediatric Risk of Mortality physiologic variable subcategories to improve score performance, and recalibrates the Pediatric Risk of Mortality score, placing the algorithms (Pediatric Risk of Mortality IV) in the public domain.
DESIGN
Prospective cohort study from December 4, 2011, to April 7, 2013.
MEASUREMENTS AND MAIN RESULTS
Among 10,078 admissions, the unadjusted mortality rate was 2.7% (site range, 1.3-5.0%). Data were divided into derivation (75%) and validation (25%) sets. The new Pediatric Risk of Mortality prediction algorithm (Pediatric Risk of Mortality IV) includes the same Pediatric Risk of Mortality physiologic variable ranges with the subcategories of neurologic and nonneurologic Pediatric Risk of Mortality scores, age, admission source, cardiopulmonary arrest within 24 hours before admission, cancer, and low-risk systems of primary dysfunction. The area under the receiver operating characteristic curve for the development and validation sets was 0.88 ± 0.013 and 0.90 ± 0.018, respectively. The Hosmer-Lemeshow goodness of fit statistics indicated adequate model fit for both the development (p = 0.39) and validation (p = 0.50) sets.
CONCLUSIONS
The new Pediatric Risk of Mortality data collection methods include significant improvements that minimize the potential for bias and errors, and the new Pediatric Risk of Mortality IV algorithm for survival and death has excellent prediction performance.
目的
疾病严重程度评估指标长期以来一直用于儿科重症监护。儿科死亡风险（PRISM）是一种基于生理学的评分系统，用于量化生理状态，当与其他独立变量结合时，它可以计算预期死亡风险和预期发病风险。尽管PRISM变量的生理范围没有变化，但最近对PRISM数据收集进行了改进，以适应新的实践模式，尽量减少偏差，并减少潜在的误差来源。这些改进包括仅将首次入住儿科重症监护病房（PICU）的结局改为住院生存/死亡，缩短数据收集期，并改变因心脏手术或介入导管插入术前“优化”治疗而入院患者的PRISM数据收集期。本分析纳入了这些变化，评估了PRISM生理变量子类别改善评分性能的潜力，并重新校准了PRISM评分，将算法（PRISM IV）置于公共领域。
设计
2011年12月4日至2013年4月7日的前瞻性队列研究。
测量与主要结果
在10078例入院病例中，未调整的死亡率为2.7%（各研究点范围为1.3% - 5.0%）。数据分为推导集（75%）和验证集（25%）。新的PRISM预测算法（PRISM IV）包括相同的PRISM生理变量范围，以及神经学和非神经学PRISM评分的子类别、年龄、入院来源、入院前24小时内的心搏骤停、癌症和主要功能障碍的低风险系统。推导集和验证集的受试者工作特征曲线下面积分别为0.88±0.013和0.90±0.018。Hosmer-Lemeshow拟合优度统计表明，推导集（p = 0.39）和验证集（p = 0.50）的模型拟合良好。
结论
新的PRISM数据收集方法有显著改进，最大限度地减少了偏差和误差的可能性，新的PRISM IV生存和死亡算法具有出色的预测性能。
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