Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles

Background Numerous studies have revealed the relationship between lipid expression and increased cardiovascular risk in ST-segment elevation myocardial infarction (STEMI) patients. Nevertheless, few investigations have focused on the risk stratification of STEMI patients using machine learning algo...

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Published in:Lipids in health and disease Vol. 20; no. 1; pp. 48 - 11
Main Authors: Xue, Yuzhou, Shen, Jian, Hong, Weifeng, Zhou, Wei, Xiang, Zhenxian, Zhu, Yuansong, Huang, Chuiguo, Luo, Suxin
Format: Journal Article
Language:English
Published: London BioMed Central 06.05.2021
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Adverse events
Aged
Algorithms
Apolipoprotein A-I - blood
Apolipoprotein B-100 - blood
Apolipoproteins
Biomedical and Life Sciences
Blood lipids
Cardiovascular disease
Cardiovascular diseases
Cardiovascular statin-modified risk
Cholesterol
Cholesterol, HDL - blood
Cholesterol, LDL - blood
Clinical Nutrition
Cluster analysis
Clustering
Coronary heart disease
Creatinine
Female
Health aspects
Heart attack
Heart attacks
Heart failure
Hemoglobin
High density lipoprotein
Humans
Kaplan-Meier Estimate
Laboratories
Learning algorithms
Life Sciences
Lipid Metabolism
Lipidology
Lipids
Lipoprotein
Lipoprotein(a) - blood
Lipoproteins
Low density lipoprotein
Machine learning
Male
Measurement
Medical Biochemistry
Middle Aged
Mortality
Myocardial infarction
Patient Readmission - statistics & numerical data
Patient Selection
Percutaneous Coronary Intervention
Principal Component Analysis
Principal components analysis
Prognosis
Pulmonary arteries
Risk Assessment
Risk factors
ST Elevation Myocardial Infarction - blood
ST Elevation Myocardial Infarction - classification
ST Elevation Myocardial Infarction - diagnosis
ST Elevation Myocardial Infarction - mortality
ST-segment elevation myocardial infarction
Triglycerides - blood
Unsupervised Machine Learning
Variables
China
Prognosis
Lipoprotein
ST-segment elevation myocardial infarction
Cardiovascular statin-modified risk
Machine learning
ISSN:1476-511X, 1476-511X
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Summary:Background Numerous studies have revealed the relationship between lipid expression and increased cardiovascular risk in ST-segment elevation myocardial infarction (STEMI) patients. Nevertheless, few investigations have focused on the risk stratification of STEMI patients using machine learning algorithms. Methods A total of 1355 STEMI patients who underwent percutaneous coronary intervention were enrolled in this study during 2015–2018. Unsupervised machine learning (consensus clustering) was applied to the present cohort to classify patients into different lipid expression phenogroups, without the guidance of clinical outcomes. Kaplan-Meier curves were implemented to show prognosis during a 904-day median follow-up (interquartile range: 587–1316). In the adjusted Cox model, the association of cluster membership with all adverse events including all-cause mortality, all-cause rehospitalization, and cardiac rehospitalization was evaluated. Results All patients were classified into three phenogroups, 1, 2, and 3. Patients in phenogroup 1 with the highest Lp(a) and the lowest HDL-C and apoA1 were recognized as the statin-modified cardiovascular risk group. Patients in phenogroup 2 had the highest HDL-C and apoA1 and the lowest TG, TC, LDL-C and apoB. Conversely, patients in phenogroup 3 had the highest TG, TC, LDL-C and apoB and the lowest Lp(a). Additionally, phenogroup 1 had the worst prognosis. Furthermore, a multivariate Cox analysis revealed that patients in phenogroup 1 were at significantly higher risk for all adverse outcomes. Conclusion Machine learning-based cluster analysis indicated that STEMI patients with increased concentrations of Lp(a) and decreased concentrations of HDL-C and apoA1 are likely to have adverse clinical outcomes due to statin-modified cardiovascular risks. Trial registration ChiCTR1900028516 ( http://www.chictr.org.cn/index.aspx ).
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ISSN:1476-511X
1476-511X
DOI:10.1186/s12944-021-01475-z