- Published
- 20 May 2024
- Journal article
Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality
- Authors
-
- Source
- Nature Communications
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.
Rights
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Cite as
Devaux, Y., Zhang, L., Lumley, A., Karaduzovic-Hadziabdic, K., Mooser, V., Rousseau, S., Shoaib, M., Satagopam, V., Adilovic, M., Srivastava, P., 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., Lorenz-Depiereux, B., Dörr, M., Witzke, O., Sanchez, G., Kul, S., Baker, A., Fagherazzi, G., Ollert, M., Wereski, R., Mills, N. & Firat, H. 2024, 'Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality', Nature Communications, 15, article no: 4259. https://doi.org/10.1038/s41467-024-47557-1