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Devaux, Y.* ; Zhang, L.* ; Lumley, A.I.* ; Karaduzovic-Hadziabdic, K.* ; Mooser, V.* ; Rousseau, S.* ; Shoaib, M.* ; Satagopam, V.* ; Adilovic, M.* ; Srivastava, P.K.* ; Emanueli, C.* ; Martelli, F.* ; Greco, S.* ; Badimon, L.* ; Padró, T.* ; Lustrek, M.* ; Scholz, M.* ; Rosolowski, M.* ; Jordan, M.* ; Brandenburger, T.* ; Benczik, B.* ; Agg, B.* ; Ferdinandy, P.* ; Vehreschild, J.J.* ; Lorenz-Depiereux, B. ; Dörr, M.* ; Witzke, O.* ; Sánchez, G.* ; Kul, S.* ; Baker, A.H.* ; Fagherazzi, G.* ; Ollert, M.* ; Wereski, R.* ; Mills, N.L.* ; Firat, H.*

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.

Nat. Commun. 15:4259 (2024)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82-0.84) and a balanced accuracy of 0.78 (95% CI 0.77-0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40-0.74). Quantitative PCR validated LEF1-AS1's adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Zeitschrift Nature Communications
Quellenangaben Band: 15, Heft: 1, Seiten: , Artikelnummer: 4259 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology (EPI)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Genetics and Epidemiology
PSP-Element(e) G-504091-004
Förderungen NIHR Biomedical Research Center at Imperial College London
Liverpool Experimental Cancer Medicine Center
PHE
NIHR HPRU in Respiratory Infections at Imperial College London
Public Health England (PHE)
NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool
Medical Research Council (MRC)
National Institute for Health Research (NIHR)
German Center for Infection Research (DZIF)
Bavarian Ministry of Research and Art
German Federal Ministry of Education and Research (BMBF)
European Regional Development Fund (FEDER)
Andre Losch Foundation
Luxembourg National Research Fund (FNR) (Predi-COVID)
Fonds de recherche du Quebec - Sante
Genome Quebec

Italian Ministry of Health
Heart Foundation-Daniel Wagner of Luxembourg
Ministry of Higher Education and Research
-2021-00013]
National Research Fund
National Research, Development and Innovation Office (NKFIH) of Hungary
National Research Development and Innovation Fund
European Union
Ministry for Innovation and Technology
Ministere de la Sante et des Services Sociaux
Public Health Agency of Canada
EU Horizon 2020 project COVIRNA
DOI 10.1038/s41467-024-47557-1
WOS ID 001228176400030
Scopus ID 85193708350
PubMed ID 38769334
Erfassungsdatum 2024-06-03
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