Outputs (2)

2236 Emergency department attendance stratified by cause and frailty status: a national cohort study of over 155 million patients (2024)
Journal Article
Sokhal, B., Matetić, A., Protheroe, J., Helliwell, T., Myint, P., Paul, T., …Mamas, M. (2024). 2236 Emergency department attendance stratified by cause and frailty status: a national cohort study of over 155 million patients. Age and ageing, 53(Supplement_3), https://doi.org/10.1093/ageing/afae139.051

Introduction Data are limited on whether the causes of Emergency Department (ED) attendance and clinical outcomes vary by frailty status. Methods Using data from the Nationwide Emergency Department Sample, all ED attendances from 2016 to 2018 were st... Read More about 2236 Emergency department attendance stratified by cause and frailty status: a national cohort study of over 155 million patients.

Machine-learning clustering analysis identifies novel phenogroups in patients with ST-elevation acute myocardial infarction. (2024)
Journal Article
Matetic, A., Kyriacou, T., & Mamas, M. A. (2024). Machine-learning clustering analysis identifies novel phenogroups in patients with ST-elevation acute myocardial infarction. International Journal of Cardiology, 411, 1-9. https://doi.org/10.1016/j.ijcard.2024.132272

Machine learning clustering of patients with ST-elevation acute myocardial infarction (STEMI) may provide important insights into their risk profile, management and prognosis. All adult discharges for STEMI in the National Inpatient Sample (October 2... Read More about Machine-learning clustering analysis identifies novel phenogroups in patients with ST-elevation acute myocardial infarction..