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

Yvan Devaux (), Lu Zhang, Andrew I. Lumley, Kanita Karaduzovic-Hadziabdic, Vincent Mooser, Simon Rousseau, Muhammad Shoaib, Venkata Satagopam, Muhamed Adilovic, Prashant Kumar Srivastava, Costanza Emanueli, Fabio Martelli, Simona Greco, Lina Badimon, Teresa Padro, Mitja Lustrek, Markus Scholz, Maciej Rosolowski, Marko Jordan, Timo Brandenburger, Bettina Benczik, Bence Agg, Peter Ferdinandy, Jörg Janne Vehreschild, Bettina Lorenz-Depiereux, Marcus Dörr, Oliver Witzke, Gabriel Sanchez, Seval Kul, Andy H. Baker, Guy Fagherazzi, Markus Ollert, Ryan Wereski, Nicholas L. Mills and Hüseyin Firat
Additional contact information
Yvan Devaux: Luxembourg Institute of Health
Lu Zhang: Luxembourg Institute of Health
Andrew I. Lumley: Luxembourg Institute of Health
Kanita Karaduzovic-Hadziabdic: International University of Sarajevo
Vincent Mooser: McGill University
Simon Rousseau: McGill University
Muhammad Shoaib: University of Luxembourg
Venkata Satagopam: University of Luxembourg
Muhamed Adilovic: International University of Sarajevo
Prashant Kumar Srivastava: Imperial College London
Costanza Emanueli: Imperial College London
Fabio Martelli: IRCCS Policlinico San Donato
Simona Greco: IRCCS Policlinico San Donato
Lina Badimon: Autonomous University of Barcelona
Teresa Padro: Autonomous University of Barcelona
Mitja Lustrek: Jozef Stefan Institute
Markus Scholz: University of Leipzig
Maciej Rosolowski: University of Leipzig
Marko Jordan: Jozef Stefan Institute
Timo Brandenburger: Medical University of Dusseldorf
Bettina Benczik: Semmelweis University, Budapest, Hungary; Pharmahungary Group
Bence Agg: Semmelweis University, Budapest, Hungary; Pharmahungary Group
Peter Ferdinandy: Semmelweis University, Budapest, Hungary; Pharmahungary Group
Jörg Janne Vehreschild: Goethe University Frankfurt, University Hospital
Bettina Lorenz-Depiereux: Helmholtz Center Munich
Marcus Dörr: University Medicine Greifswald, Greifswald, Germany; German Centre of Cardiovascular Research (DZHK)
Oliver Witzke: University Hospital Essen, University of Duisburg-Essen
Gabriel Sanchez: Firalis SA
Seval Kul: Firalis SA
Andy H. Baker: University of Edinburgh
Guy Fagherazzi: Luxembourg Institute of Health
Markus Ollert: Luxembourg Institute of Health
Ryan Wereski: University of Edinburgh
Nicholas L. Mills: University of Edinburgh
Hüseyin Firat: Firalis SA

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract 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.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-47557-1 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47557-1

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-47557-1

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().