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

Authors :
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
Hüseyin Firat
Source :
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
15
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.54234423656547d89b595a58c9921da7
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-024-47557-1