Kim Susan W, Li Jordan Y, Hakendorf Paul, Teubner David Jo, Ben-Tovim David I, Thompson Campbell H
Flinders Centre for Epidemiology and Biostatistics, School of Medicine, Flinders University, Adelaide, South Australia, Australia.
Emerg Med Australas. 2014 Aug;26(4):361-7. doi: 10.1111/1742-6723.12252. Epub 2014 Jun 16.
The present study aims to determine the importance of certain factors in predicting the need of hospital admission for a patient in the ED.
This is a retrospective observational cohort study between January 2010 and March 2012. The characteristics, including blood test results, of 100,123 patients who presented to the ED of a tertiary referral urban hospital, were incorporated into models using logistic regression in an attempt to predict the likelihood of patients' disposition on leaving the ED. These models were compared with triage nurses' prediction of patient disposition.
Patient age, their initial presenting symptoms or diagnosis, Australasian Triage Scale category, mode of arrival, existence of any outside referral, triage time of day and day of the week were significant predictors of the patient's disposition (P < 0.001). The ordering of blood tests for any patient and the extent of abnormality of those tests increased the likelihood of admission. The accuracy of triage nurses' admission prediction was similar to that offered by a model that used the patients' presentation characteristics. The addition of blood tests to that model resulted in only 3% greater accuracy in prediction of patient disposition.
Certain characteristics of patients as they present to hospital predict their admission. The accuracy of the triage nurses' prediction for disposition of patients is the same as that afforded by a model constructed from these characteristics. Blood test results improve disposition accuracy only slightly so admission decisions should not always wait for these results.
本研究旨在确定某些因素在预测急诊科患者住院需求方面的重要性。
这是一项2010年1月至2012年3月间的回顾性观察队列研究。将一家城市三级转诊医院急诊科收治的100123例患者的特征（包括血液检测结果）纳入逻辑回归模型，以预测患者离开急诊科时的处置可能性。将这些模型与分诊护士对患者处置的预测进行比较。
患者年龄、初始症状或诊断、澳大利亚分诊量表类别、到达方式、是否有外部转诊、分诊时间和星期几是患者处置的显著预测因素（P < 0.001）。为任何患者安排血液检测以及检测结果的异常程度增加了住院的可能性。分诊护士预测住院的准确性与使用患者就诊特征的模型相似。在该模型中加入血液检测后，患者处置预测的准确性仅提高了3%。
患者就诊时的某些特征可预测其住院情况。分诊护士对患者处置的预测准确性与基于这些特征构建的模型相同。血液检测结果仅略微提高处置准确性，因此住院决策不应总是等待这些结果。
