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 BrandenburgerBettina 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

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Article number4259
Pages (from-to)4259
JournalNature Communications
Volume15
Issue number1
DOIs
Publication statusPublished - 20 May 2024

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