AUTHOR=Gao Yan , Bai Xueke , Lu Jiapeng , Zhang Lihua , Yan Xiaofang , Huang Xinghe , Dai Hao , Wang Yanping , Hou Libo , Wang Siming , Tian Aoxi , Li Jing 

TITLE=Prognostic Value of Multiple Circulating Biomarkers for 2-Year Death in Acute Heart Failure With Preserved Ejection Fraction

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 8 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2021.779282

DOI=10.3389/fcvm.2021.779282

ISSN=2297-055X

ABSTRACT=Background  Heart failure with preserved ejection fraction (HFpEF) is increasingly recognized as a major global public health burden and lacks effective risk stratification. We aimed to assess a multi-biomarker model in improving risk prediction in HFpEF.
Methods  We analyzed 18 biomarkers from the main pathophysiological domains of HF in 380 patients hospitalized for HFpEF from a prospective cohort. The association between these biomarkers and 2-year risk of all-cause death was assessed by Cox proportional hazards model. Support vector machine (SVM), a supervised machine learning method, was used to develop prediction model of 2-year all-cause and cardiovascular death using a combination of 18 biomarkers and clinical indicators. The improvement of this model was evaluated by c-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).
Results The median age of patients was 71 years, and 50.5% were female. Multiple biomarkers independently predicted the 2-year risk of death in Cox regression model, including N-terminal pro B-type brain-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-TnT), high-sensitivity C-reactive protein (hs-CRP), growth differentiation factor-15 (GDF-15), tumor necrosis factor-α (TNFα)], endoglin, and 4 biomarkers of extracellular matrix turnover [tissue inhibitor of metalloproteinases (TIMP)-1, matrix metalloproteinase (MMP)-2, MMP-8 and MMP-9). The SVM model effectively predicted the 2-year risk of all-cause death in patients with acute HFpEF in training set (AUC 0.834, 95% CI:0.771−0.895) and validation set (AUC 0.798, 95% CI: 0.719−0.877). The NRI and IDI indicated that the SVM model significantly improved patient classification compared to the reference model in both sets (p<0.05).
Conclusions  Multiple circulating biomarkers coupled with an appropriate machine-learning method effectively predicted the risk of long-term mortality in patients with acute HFpEF. It is a promising strategy for improving risk stratification in HFpEF.