Emergency Department, Rijnstate Hospital, Arnhem, The Netherlands.
Clinical Research Department, Rijnstate Hospital, Arnhem, The Netherlands.
Emerg Med J. 2018 Aug;35(8):464-470. doi: 10.1136/emermed-2017-206673. Epub 2018 Apr 7.
Early prediction of admission has the potential to reduce length of stay in the ED. The aim of this study is to create a computerised tool to predict admission probability.
The prediction rule was derived from data on all patients who visited the ED of the Rijnstate Hospital over two random weeks. Performing a multivariate logistic regression analysis factors associated with hospitalisation were explored. Using these data, a model was developed to predict admission probability. Prospective validation was performed at Rijnstate Hospital and in two regional hospitals with different baseline admission rates. The model was converted into a computerised tool that reported the admission probability for any patient at the time of triage.
Data from 1261 visits were included in the derivation of the rule. Four contributing factors for admission that could be determined at triage were identified: age, triage category, arrival mode and main symptom. Prospective validation showed that this model reliably predicts hospital admission in two community hospitals (area under the curve (AUC) 0.87, 95% CI 0.85 to 0.89) and in an academic hospital (AUC 0.76, 95% CI 0.72 to 0.80). In the community hospitals, using a cut-off of 80% for admission probability resulted in the highest number of true positives (actual admissions) with the greatest specificity (positive predictive value (PPV): 89.6, 95% CI 84.5 to 93.6; negative predictive value (NPV): 70.3, 95% CI 67.6 to 72.9). For the academic hospital, with a higher admission rate, a 90% probability was a better cut-off (PPV: 83.0, 95% CI 73.8 to 90.0; NPV: 59.3, 95% CI 54.2 to 64.2).
Admission probability for ED patients can be calculated using a prediction tool. Further research must show whether using this tool can improve patient flow in the ED.
提前预测患者是否需要住院可以减少急诊的住院时间。本研究的目的是创建一个可以预测住院概率的计算机工具。
从随机抽取的两周内到 Rijnstate 医院急诊科就诊的所有患者的数据中推导出预测规则。使用多元逻辑回归分析探索与住院相关的因素。利用这些数据，开发了一个预测住院概率的模型。在 Rijnstate 医院和两家具有不同基线住院率的地区医院进行了前瞻性验证。该模型被转化为一个计算机工具，可以在分诊时报告任何患者的住院概率。
该规则的推导纳入了 1261 次就诊的数据。确定了 4 个可在分诊时确定的入院因素：年龄、分诊类别、到达方式和主要症状。前瞻性验证表明，该模型可以可靠地预测两家社区医院（曲线下面积（AUC）0.87，95%置信区间（CI）0.85 至 0.89）和一家学术医院（AUC 0.76，95%CI 0.72 至 0.80）的住院情况。在社区医院中，使用 80%的住院概率作为切点可以获得最多的真阳性（实际住院）和最大的特异性（阳性预测值（PPV）：89.6，95%CI 84.5 至 93.6；阴性预测值（NPV）：70.3，95%CI 67.6 至 72.9）。对于学术医院，由于住院率较高，90%的概率是更好的切点（PPV：83.0，95%CI 73.8 至 90.0；NPV：59.3，95%CI 54.2 至 64.2）。
可以使用预测工具计算急诊科患者的住院概率。进一步的研究必须表明，使用该工具是否可以改善急诊科的患者流量。
