AUTHOR=Liu Jie , Huang Lei , Shi Xinrong , Gu Chungang , Xu Hongmin , Liu Shuye 

TITLE=Clinical Parameters and Metabolomic Biomarkers That Predict Inhospital Outcomes in Patients With ST-Segment Elevated Myocardial Infarctions

JOURNAL=Frontiers in Physiology

VOLUME=Volume 12 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2021.820240

DOI=10.3389/fphys.2021.820240

ISSN=1664-042X

ABSTRACT=Background: Postoperative risk stratification is challenging in patients with ST-segment elevated myocardial infarction (STEMI) who undergo percutaneous coronary intervention. The study aimed to characterize the metabolic fingerprints of the STEMI patients with different in-hospital outcomes in the early stage of morbidity and to integrate the clinical baseline characteristics to develop a prognostic prediction model.
Methods: Plasma samples were collected retrospectively from two propensity score-matched STEMI cohorts from May 6th, 2020, to April 20th, 2021. Cohort one consisted of 48 survivors and 48 non-survivors. Cohort two included 48 patients with unstable angina pectoris, 48 STEMI patients, and 48 age-sex matched healthy controls. Metabolic profiling was generated based on ultra-performance liquid chromatography and a mass spectrometry platform. A comprehensive metabolomics data analysis was performed using MetaboAnalyst v.5.0. The hub metabolite biomarkers integrated into the model were tested using multivariate linear support vector machine (SVM) algorithms and a generalized estimating equation (GEE) model. Their predictive capabilities were evaluated using areas under the curve (AUCs) of receiver operating characteristic curves.
Results: Metabonomics analysis from the two cohorts showed that the STEMI patients with different outcomes had significantly different clusters. Seven differentially expressed metabolites were identified as potential candidates for predicting in-hospital outcomes based on the two cohorts, and their joint discriminative capabilities were robust using SVM (AUC 0.998, 95% confidence interval [CI] 0.983–1) and the univariate GEE model (AUC = 0.981, 95% CI 0.969–0.994). After integrating another six clinical variants, the predictive performance of the updated model improved further (AUC = 0.99, 95% CI 0.981–0.998).
Conclusions: A survival prediction model integrating seven metabolites from non-targeted metabonomics and six clinical indicators may generate a powerful early survival prediction model for STEMI patients. Validation of internal and external cohorts is required.