The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision-making.

OBJECTIVE: To further develop and refine an Emergency Department (ED) in-patient admission prediction model using machine learning techniques. METHODS: This was a retrospective analysis of state-wide ED data from New South Wales, Australia. Six classification algorithms (Bayesian networks, decision trees, logistic regression, naïve Bayes, neural networks and nearest neighbour) and five feature selection techniques (none, manual, correlation-based, information gain and wrapper) were examined. Presenting problem was categorised using broad (n = 20) and specific (n = 100) representations. Models were evaluated based on Area Under the Curve (AUC) and accuracy. The results were compared with the Sydney Triage to Admission Risk Tool (START), which uses logistic regression and six manually selected features. RESULTS: Sixty admission prediction models were trained and validated using data from 1 721 294 patients. Under the broad representation of presenting problem, the nearest neighbour algorithm with manual feature selection had the best AUC of 0.8206 (95% CI ±0.0006), while the decision tree with no feature selection had the best accuracy of 74.83% (95% CI ±0.065). Under the specific representation, almost all models improved; the nearest neighbour with information gain feature selection had the best AUC of 0.8267 (95% CI ±0.0006), while the decision tree with wrapper or no feature selection had the best accuracy of 75.24% (95% CI ±0.064). Eleven of the machine learning models had slightly better AUC than the START model. CONCLUSION: Machine learning methods demonstrate similar performance to logistic regression for ED disposition prediction models using basic triage information. This should be investigated further, especially for larger data sets with more complex clinical information.

The Sydney Triage to Admission Risk Tool (START): A prospective validation study.

OBJECTIVE: The present study aims to prospectively validate the Sydney Triage to Admission Risk Tool (START) to predict ED disposition. METHODS: This was a prospective validation study at two metropolitan EDs in Sydney, Australia. Consecutive triage encounters were observed by a trained researcher and START scores calculated. The primary outcome was patient disposition (discharge or inpatient admission) from the ED. Multivariable logistic regression was used to estimate area under curve of receiver operator characteristic (AUC ROC) for START scores as well as START score in combination with other variables such as frailty, general practitioner referral, overcrowding and major medical comorbidities. RESULTS: There were 894 patients analysed during the study period. The START score when applied to the data had AUC ROC of 0.80 (95% CI 0.77-0.83). The inclusion of other clinical variables identified at triage did not improve the overall performance of the model with an AUC ROC of 0.81 (95% CI 0.78-0.84) in the present study. CONCLUSION: The overall performance of the START tool with respect to model discrimination and accuracy has been prospectively validated. Further clinical trials are required to test the clinical effectiveness of the tool in improving patient flow and overall ED performance.