- 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
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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., Padro, 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