@article{Devaux_Y-2024_177956,
title = {Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality},
author = {Devaux, Y. and Zhang, L. and Lumley, A. and Karaduzovic-Hadziabdic, K. and Mooser, V. and Rousseau, S. and Shoaib, M. and Satagopam, V. and Adilovic, M. and Srivastava, P. and Emanueli, C. and Martelli, F. and Greco, S. and Badimon, L. and Padró, T. and Lustrek, M. and Scholz, M. and Rosolowski, M. and Jordan, M. and Brandenburger, T. and Benczik, B. and Agg, B. and Ferdinandy, P. and Vehreschild, J. and Lorenz-Depiereux, B. and Dörr, M. and Witzke, O. and Sanchez, G. and Kul, S. and Baker, A. and Fagherazzi, G. and Ollert, M. and Wereski, R. and Mills, N. and Firat, H.},
month = {may},
year = {2024},
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.},
volume = {15},
journal = {Nature Communications},
publisher = {Springer Nature},
url = {https://doi.org/10.1038/s41467-024-47557-1},
}
