Utilizing machine learning dimensionality reduction for risk stratification of chest pain patients in the emergency department

BMC Medical Research Methodology

Zhi Xiong Koh¹  Su Li Leow²  Andrew Fu Wah Ho³  Marcus Eng Hock Ong⁴  Nan Liu⁵  Marcel Lucas Chee⁶  Dagang Guo⁷ 

[1] Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore;Duke-NUS Medical School, National University of Singapore, Singapore, Singapore;Duke-NUS Medical School, National University of Singapore, Singapore, Singapore;Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore;Duke-NUS Medical School, National University of Singapore, Singapore, Singapore;Health Services Research Centre, Singapore Health Services, Singapore, Singapore;Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore;Duke-NUS Medical School, National University of Singapore, Singapore, Singapore;Health Services Research Centre, Singapore Health Services, Singapore, Singapore;Institute of Data Science, National University of Singapore, Singapore, Singapore;Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia;SingHealth Duke-NUS Emergency Medicine Academic Clinical Programme, Singapore, Singapore;

关键词: Machine learning;    Dimensionality reduction;    Heart rate n-variability (HRnV);    Heart rate variability (HRV);    Chest pain;    Emergency department;   

DOI  :  10.1186/s12874-021-01265-2

来源: Springer

【 摘 要 】

BackgroundChest pain is among the most common presenting complaints in the emergency department (ED). Swift and accurate risk stratification of chest pain patients in the ED may improve patient outcomes and reduce unnecessary costs. Traditional logistic regression with stepwise variable selection has been used to build risk prediction models for ED chest pain patients. In this study, we aimed to investigate if machine learning dimensionality reduction methods can improve performance in deriving risk stratification models.MethodsA retrospective analysis was conducted on the data of patients > 20 years old who presented to the ED of Singapore General Hospital with chest pain between September 2010 and July 2015. Variables used included demographics, medical history, laboratory findings, heart rate variability (HRV), and heart rate n-variability (HRnV) parameters calculated from five to six-minute electrocardiograms (ECGs). The primary outcome was 30-day major adverse cardiac events (MACE), which included death, acute myocardial infarction, and revascularization within 30 days of ED presentation. We used eight machine learning dimensionality reduction methods and logistic regression to create different prediction models. We further excluded cardiac troponin from candidate variables and derived a separate set of models to evaluate the performance of models without using laboratory tests. Receiver operating characteristic (ROC) and calibration analysis was used to compare model performance.ResultsSeven hundred ninety-five patients were included in the analysis, of which 247 (31%) met the primary outcome of 30-day MACE. Patients with MACE were older and more likely to be male. All eight dimensionality reduction methods achieved comparable performance with the traditional stepwise variable selection; The multidimensional scaling algorithm performed the best with an area under the curve of 0.901. All prediction models generated in this study outperformed several existing clinical scores in ROC analysis.ConclusionsDimensionality reduction models showed marginal value in improving the prediction of 30-day MACE for ED chest pain patients. Moreover, they are black box models, making them difficult to explain and interpret in clinical practice.

【 授权许可】

CC BY