Triage is a process to accurately assess and classify symptoms to identify and provide rapid treatment to patients. The Korean Triage and Acuity Scale (KTAS) is used as a triage instrument in all emergency centers. The aim of this study was to train and compare machine learning models to predict KTAS levels. This was a cross-sectional study using data from a single emergency department of a tertiary university hospital. Information collected during triage was used in the analysis. Logistic regression, random forest, and XGBoost were used to predict the KTAS level. The models with the highest area under the receiver operating characteristic curve (AUROC) were the random forest and XGBoost models trained on the entire dataset (AUROC = 0.922, 95% confidence interval 0.917-0.925 and AUROC = 0.922, 95% confidence interval 0.918-0.925, respectively). The AUROC of the models trained on the clinical data was higher than that of models trained on text data only, but the models trained on all variables had the highest AUROC among similar machine learning models. Machine learning can robustly predict the KTAS level at triage, which may have many possibilities for use, and the addition of text data improves the predictive performance compared to that achieved by using structured data alone.

Background: There is a lack of studies investigating the heterogeneity of patients with aortic stenosis (AS). We explored whether cluster analysis identifies distinct subgroups with different prognostic significances in AS. Methods: Newly diagnosed patients with moderate or severe AS were prospectively enrolled between 2013 and 2016 (n=398, mean 71 years, 55% male). Among demographics, laboratory, and echocardiography parameters (n=32), 11 variables were selected through dimension reduction and used for unsupervised clustering. Phenotypes and causes of mortality were compared between the clusters. Results: Three clusters with markedly different features were identified. Cluster 1 (n=60) was predominantly associated with cardiac dysfunction, cluster 2 (n=86) consisted of elderly with comorbidities, especially end-stage renal disease, whereas cluster 3 (n=252) demonstrated neither cardiac dysfunction nor comorbidities. Although AS severity did not differ, there was a significant difference in adverse outcomes between the clusters during a median 2.4 years follow-up (mortality rate, 13.3% versus 19.8% versus 6.0% for cluster 1, 2, and 3, P <0.001). Particularly, compared with cluster 3, cluster 1 was associated with only cardiac mortality (adjusted hazard ratio, 7.37 [95% CI, 2.00–27.13]; P =0.003), whereas cluster 2 was associated with higher noncardiac mortality (adjusted hazard ratio, 3.35 [95% CI, 1.26–8.90]; P =0.015). Phenotypes and association of clusters with specific outcomes were reproduced in an independent validation cohort (n=262). Conclusions: Unsupervised cluster analysis of patients with AS revealed 3 distinct groups with different causes of death. This provides a new perspective in the categorization of patients with AS that takes into account comorbidities and extravalvular cardiac dysfunction.