A new risk stratification score for patients with suspected cardiac chest pain in emergency departments, based on machine learning

• Published in: Chinese medical journal 2020-04, Vol.133 (7), p.879-880
• Main Authors: Mao, Hai-Feng, Chen, Xiao-Hui, Li, Yun-Mei, Zhang, Si-Yuan, Mo, Jun-Rong, Li, Min, Lin, Pei-Yi, Rainer, Timothy H., Graham, Colin A., Jiang, Hui-Lin
• Format: Article
• Language: English
• Subjects: Algorithms; Angina Pectoris - pathology; Chest Pain - pathology; Chi-Square Distribution; Consent; Correspondence; Emergency Service, Hospital - statistics & numerical data; Heart attacks; Humans; Logistic Models; Machine Learning; Pain; Patients; Retrospective Studies; ROC Curve; Support vector machines
• ISSN: 0366-6999; 2542-5641
• DOI: 10.1097/CM9.0000000000000725

Data regarding subsequent visits to ED, hospital readmission for evaluation of chest pain and all cardiac procedures carried out were obtained from Clinical Management System (CMS) in PWH and Health Insurance Information Management System (HIIMS) in AHGZMU and confirmed by phone interviews at 7-day follow-up after the initial presentation. [...]the machine learning model XGBoost may be a better prognostic tool for predicting 7-day MACE following chest pain than SVM, LR, and HEART score. Declaration of patient consent Ethical approval was obtained from the joint Chinese University of Hong Kong - New Territories East Cluster Clinical Research Ethics Committee in Hong Kong as well as the Clinical Research Ethics Committee of the Second Affiliated Hospital of Guangzhou Medical University.