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
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...
May 20, 2024: Nature Communications