AIM: The aim of this study is to directly compare published prediction tools with triage nurse (TN) predictions within a defined paediatric population. METHOD: A prospective observational study carried out over a week in May 2010 in the Emergency Department (ED) at Princess Margaret Hospital for Children in Perth, Western Australia. TN predicted which patients would be admitted to hospital at the time of ED presentation. Data required for the other prediction tools (paediatric early warning score (PEWS); triage category and the Pediatric Risk of Admission Score (PRISA) and PRISA II were obtained from the notes following the patient's ED attendance. RESULTS: A total of 1223 patients presented during the study week, 91 patients were excluded and a total of 946 patients (83.6%) had TN predictions and were included in the analysis. TN predictions were compared against a PEWS ≥ 4, triage category 1, 2 and 3, PRISA ≥ 9 and PRISA II ≥ 2. TNs had the highest prediction accuracy (87.7%), followed by an elevated PEWS (82.9%), triage category of 1, 2, or 3 (82.9%). The PRISA and PRISA II score had an accuracy of 80.1% and 79.7%, respectively. CONCLUSION: When compared with validated prediction tools, the TN is the most accurate predictor of need to admit. This study provides valuable information in planning efficient flow of patients through the ED.
AIM: The aim of this study is to directly compare published prediction tools with triage nurse (TN) predictions within a defined paediatric population. METHOD: A prospective observational study carried out over a week in May 2010 in the Emergency Department (ED) at Princess Margaret Hospital for Children in Perth, Western Australia. TN predicted which patients would be admitted to hospital at the time of ED presentation. Data required for the other prediction tools (paediatric early warning score (PEWS); triage category and the Pediatric Risk of Admission Score (PRISA) and PRISA II were obtained from the notes following the patient's ED attendance. RESULTS: A total of 1223 patients presented during the study week, 91 patients were excluded and a total of 946 patients (83.6%) had TN predictions and were included in the analysis. TN predictions were compared against a PEWS ≥ 4, triage category 1, 2 and 3, PRISA ≥ 9 and PRISA II ≥ 2. TNs had the highest prediction accuracy (87.7%), followed by an elevated PEWS (82.9%), triage category of 1, 2, or 3 (82.9%). The PRISA and PRISA II score had an accuracy of 80.1% and 79.7%, respectively. CONCLUSION: When compared with validated prediction tools, the TN is the most accurate predictor of need to admit. This study provides valuable information in planning efficient flow of patients through the ED.
Authors: Juliana de Oliveira Freitas Miranda; Climene Laura de Camargo; Carlito Lopes Nascimento; Daniel Sales Portela; Alan Monaghan Journal: Rev Lat Am Enfermagem Date: 2017-07-10
Authors: Beata Mickiewicz; Graham C Thompson; Jaime Blackwood; Craig N Jenne; Brent W Winston; Hans J Vogel; Ari R Joffe Journal: Sci Rep Date: 2018-11-09 Impact factor: 4.379
Authors: Beata Mickiewicz; Graham C Thompson; Jaime Blackwood; Craig N Jenne; Brent W Winston; Hans J Vogel; Ari R Joffe Journal: Crit Care Date: 2015-09-09 Impact factor: 9.097
Authors: Sonya Crowe; Rachel Knowles; Jo Wray; Jenifer Tregay; Deborah A Ridout; Martin Utley; Rodney Franklin; Catherine L Bull; Katherine L Brown Journal: BMJ Open Date: 2016-06-06 Impact factor: 2.692