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恰当的:早期儿科重症监护病房再入院风险预测工具的开发。

PROPER: Development of an Early Pediatric Intensive Care Unit Readmission Risk Prediction Tool.

作者信息

Kaur Harsheen, Naessens James M, Hanson Andrew C, Fryer Karen, Nemergut Michael E, Tripathi Sandeep

机构信息

1 Department of Pediatric Critical Care, Mayo Clinic, Rochester, MN, USA.

2 Department of Health Science Research, Mayo Clinic, Rochester, MN, USA.

出版信息

J Intensive Care Med. 2018 Jan;33(1):29-36. doi: 10.1177/0885066616665806. Epub 2016 Sep 6.

Abstract

OBJECTIVE

No risk prediction model is currently available to measure patient's probability for readmission to the pediatric intensive care unit (PICU). This retrospective case-control study was designed to assess the applicability of an adult risk prediction score (Stability and Workload Index for Transfer [SWIFT]) and to create a pediatric version (PRediction Of PICU Early Readmissions [PROPER]).

DESIGN

Eighty-six unplanned early (<48 hours) PICU readmissions from January 07, 2007, to June 30, 2014, were compared with 170 random controls. Patient- and disease-specific data and PICU workload factors were compared across the 2 groups. Factors statistically significant on multivariate analysis were included in the creation of the risk prediction model. The SWIFT scores were calculated for cases and controls and compared for validation.

RESULTS

Readmitted patients were younger, weighed less, and were more likely to be admitted from the emergency department. There were no differences in gender, race, or admission Pediatric Index of Mortality scores. A higher proportion of patients in the readmission group had a Pediatric Cerebral Performance Category in the moderate to severe disability category. Cases and controls did not differ with respect to staff workload at discharge or discharge day of the week; there was a much higher proportion of patients on supplemental oxygen in the readmission group. Only 2 of 5 categories in the SWIFT model were significantly different, and although the median SWIFT score was significantly higher in the readmissions group, the model discriminated poorly between cases and controls (area under the curve: 0.613). A 7-category PROPER score was created based on a multiple logistic regression model. Sensitivity of this model (score ≥12) for the detection of readmission was 81% with a positive predictive value of 0.50.

CONCLUSION

We have created a preliminary model for predicting patients at risk of early readmissions to the PICU from the hospital floor. The SWIFT score is not applicable for predicting the risk for pediatric population.

摘要

目的

目前尚无风险预测模型可用于衡量患者再次入住儿科重症监护病房(PICU)的概率。这项回顾性病例对照研究旨在评估成人风险预测评分(转运稳定性和工作量指数[SWIFT])的适用性,并创建一个儿科版本(PICU早期再入院预测[PROPER])。

设计

将2007年1月7日至2014年6月30日期间86例非计划的早期(<48小时)PICU再入院患者与170例随机对照患者进行比较。对两组患者的个体及疾病特异性数据和PICU工作量因素进行比较。多因素分析中有统计学意义的因素被纳入风险预测模型的构建。计算病例组和对照组的SWIFT评分并进行比较以验证其有效性。

结果

再入院患者年龄更小、体重更轻,且更有可能从急诊科入院。在性别、种族或入院时的儿科死亡率指数评分方面无差异。再入院组中更高比例的患者的儿科脑功能表现类别为中度至重度残疾。病例组和对照组在出院时或出院当周的工作人员工作量方面无差异;再入院组中接受补充氧气的患者比例更高。SWIFT模型的5个类别中只有2个有显著差异,尽管再入院组的SWIFT评分中位数显著更高,但该模型对病例组和对照组的区分能力较差(曲线下面积:0.613)基于多因素逻辑回归模型创建了一个7分类的PROPER评分。该模型(评分≥12)检测再入院的敏感性为81%,阳性预测值为0.50。

结论

我们创建了一个初步模型,用于预测从医院病房有早期再入院风险的PICU患者。SWIFT评分不适用于预测儿科人群的风险。

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