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microRNA neural networks improve diagnosis of acute coronary syndrome (ACS).

Abstract：

BACKGROUND:Cardiac troponins are the preferred biomarkers of acute myocardial infarction. Despite superior sensitivity, serial testing of Troponins to identify patients suffering acute coronary syndromes is still required in many cases to overcome limited specificity. Moreover, unstable angina pectoris relies on reported symptoms in the troponin-negative group. In this study, we investigated genome-wide miRNA levels in a prospective cohort of patients with clinically suspected ACS and determined their diagnostic value by applying an in silico neural network. METHODS:PAXgene blood and serum samples were drawn and hsTnT was measured in patients at initial presentation to our Chest-Pain Unit. After clinical and diagnostic workup, patients were adjudicated by senior cardiologists in duty to their final diagnosis: STEMI, NSTEMI, unstable angina pectoris and non-ACS patients. ACS patients and a cohort of healthy controls underwent deep transcriptome sequencing. Machine learning was implemented to construct diagnostic miRNA classifiers. RESULTS:We developed a neural network model which incorporates 34 validated ACS miRNAs, showing excellent classification results. By further developing additional machine learning models and selecting the best miRNAs, we achieved an accuracy of 0.96 (95% CI 0.96-0.97), sensitivity of 0.95, specificity of 0.96 and AUC of 0.99. The one-point hsTnT value reached an accuracy of 0.89, sensitivity of 0.82, specificity of 0.96, and AUC of 0.96. CONCLUSIONS:Here we show the concept of neural network based biomarkers for ACS. This approach also opens the possibility to include multi-modal data points to further increase precision and perform classification of other ACS differential diagnoses.

journal_name

J Mol Cell Cardiol

journal_title

Journal of molecular and cellular cardiology

authors

Kayvanpour E,Gi WT,Sedaghat-Hamedani F,Lehmann DH,Frese KS,Haas J,Tappu R,Samani OS,Nietsch R,Kahraman M,Fehlmann T,Müller-Hennessen M,Weis T,Giannitsis E,Niederdränk T,Keller A,Katus HA,Meder B

doi

10.1016/j.yjmcc.2020.04.014

subject

Has Abstract

pub_date

2020-04-17 00:00:00

eissn

0022-2828

issn

1095-8584

pii

S0022-2828(20)30097-3

pub_type

杂志文章

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microRNA neural networks improve diagnosis of acute coronary syndrome (ACS).