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

• Verfasst von: Kayvanpour, Elham [VerfasserIn]
• Verfasst von: Gi, Weng-Tein [VerfasserIn]
• Verfasst von: Sedaghat-Hamedani, Farbod [VerfasserIn]
• Verfasst von: Lehmann, David Hermann [VerfasserIn]
• Verfasst von: Frese, Karen S. [VerfasserIn]
• Verfasst von: Haas, Jan [VerfasserIn]
• Verfasst von: Tappu, Rewati [VerfasserIn]
• Verfasst von: Shirvani-Samani, Omid [VerfasserIn]
• Verfasst von: Nietsch, Rouven [VerfasserIn]
• Verfasst von: Kahraman, Mustafa [VerfasserIn]
• Verfasst von: Fehlmann, Tobias [VerfasserIn]
• Verfasst von: Müller-Hennessen, Matthias [VerfasserIn]
• Verfasst von: Weis, Tanja [VerfasserIn]
• Verfasst von: Giannitsis, Evangelos [VerfasserIn]
• Verfasst von: Niederdränk, Torsten [VerfasserIn]
• Verfasst von: Keller, Andreas [VerfasserIn]
• Verfasst von: Katus, Hugo [VerfasserIn]
• Verfasst von: Meder, Benjamin [VerfasserIn]
• Titel: microRNA neural networks improve diagnosis of acute coronary syndrome (ACS)
• Verf.angabe: Elham Kayvanpour, Weng-Tein Gi, Farbod Sedaghat-Hamedani, David H. Lehmann, Karen S. Frese, Jan Haas, Rewati Tappu, Omid Shirvani Samani, Rouven Nietsch, Mustafa Kahraman, Tobias Fehlmann, Matthias Müller-Hennessen, Tanja Weis, Evangelos Giannitsis, Torsten Niederdränk, Andreas Keller, Hugo A. Katus, Benjamin Meder
• Jahr: 2021
• Jahr des Originals: 2020
• Umfang: 8 S.
• Fussnoten: Available online 17 April 2020 ; Gesehen am 06.04.2021
• Titel Quelle: Enthalten in: Journal of molecular and cellular cardiology
• Ort Quelle: New York, NY [u.a.] : Elsevier, 1970
• Jahr Quelle: 2021
• Band/Heft Quelle: 151(2021) vom: Feb., Seite 155-162
• ISSN Quelle: 1095-8584
• DOI: doi:10.1016/j.yjmcc.2020.04.014
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Acute coronary syndrome
• Sach-SW: Deep learning
• Sach-SW: High-sensitive troponin
• Sach-SW: microRNA
• K10plus-PPN: 1753215323

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.