Development and validation of an admission prediction tool for emergency departments in the Netherlands. Issue 8 (7th April 2018)

• Record Type: Journal Article
• Authors: Kraaijvanger, Nicole; Rijpsma, Douwe; Roovers, Lian; van Leeuwen, Henk; Kaasjager, Karin; van den Brand, Lillian; Horstink, Laura; Edwards, Michael
• Is Part Of: Emergency medicine journal. Volume 35:Issue 8(2018)
• Page Start: 464
• Page End: 470
• Publication Date: 2018-04-07
• Subjects: crowding -- emergency department -- hospitalisations -- planning -- management; Emergency medicine -- Periodicals; 616.02505
• DOI: 10.1136/emermed-2017-206673
• Languages: English
• ISSNs: 1472-0205

Abstract

Abstract : Objective: 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. Methods: 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. Results: 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). Conclusion: 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.