Choi Mona, Lee JuHee, Ahn Mi Jung, Kim YoungAh
College of Nursing, Yonsei University, Seoul, South Korea.
Stud Health Technol Inform. 2013;192:1063.
To examine the Critical Patient Severity Classification System (CPSCS) recorded by nurses to predict ICU and hospital lengths of stay and mortality, data were drawn from patients admitted to 2 surgical intensive care units (SICUs) at a university hospital in Seoul, South Korea in 2010. This retrospective study used a large data set retrieved from the Clinical Data Repository System. Among 1432 patients, the mean grade of CPSCS was 4.9 out of 6, which indicated that the subjects had generally severe conditions. The CPSCS was a statistically significant predictor of ICU and hospital LOS and mortality when patients' demographic characteristics were adjusted. In the era of emphasis on using big data, analysis of nursing assessment data should be evaluated to show importance of nursing contribution to predict patients' clinical outcomes.
为了检验护士记录的危重症患者严重程度分类系统（CPSCS）对预测重症监护病房（ICU）住院时间、医院住院时间和死亡率的作用，数据取自2010年韩国首尔一家大学医院收治到两个外科重症监护病房（SICU）的患者。这项回顾性研究使用了从临床数据存储系统检索到的一个大型数据集。在1432名患者中，CPSCS的平均等级为6分中的4.9分，这表明受试者的病情总体较为严重。在对患者的人口统计学特征进行调整后，CPSCS是ICU住院时间、医院住院时间和死亡率的一个具有统计学意义的预测指标。在强调使用大数据的时代，应评估护理评估数据的分析，以显示护理对预测患者临床结局的贡献的重要性。
