Nicole Kraaijvanger 1 , Douwe Rijpsma 1 , Lian Roovers 2 , Henk van Leeuwen 3 , Karin Kaasjager 4 , Lillian van den Brand 5 , Laura Horstink 6 , Michael Edwards 7 Show Affiliations »
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. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
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. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
Entities: Species
Keywords:
crowding; emergency department; hospitalisations; management; planning
Mesh： See more »
Year: 2018
PMID： 29627769 DOI： 10.1136/emermed-2017-206673
Source DB: PubMed Journal: Emerg Med J ISSN： 1472-0205 Impact factor: 2.740