Nature Communications (May 2024)
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 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
Affiliations
- Yvan Devaux
- Cardiovascular Research Unit, Department of Precision Health, Luxembourg Institute of Health
- Lu Zhang
- Bioinformatics Platform, Luxembourg Institute of Health
- Andrew I. Lumley
- Cardiovascular Research Unit, Department of Precision Health, Luxembourg Institute of Health
- Kanita Karaduzovic-Hadziabdic
- Faculty of Engineering and Natural Sciences, International University of Sarajevo
- Vincent Mooser
- Department of Human Genetics, McGill University
- Simon Rousseau
- The Meakins-Christie Laboratories at the Research Institute of the McGill University Heath Centre Research Institute, & Department of Medicine, Faculty of Medicine, McGill University
- Muhammad Shoaib
- Luxembourg Center for Systems Biomedicine, University of Luxembourg
- Venkata Satagopam
- Luxembourg Center for Systems Biomedicine, University of Luxembourg
- Muhamed Adilovic
- Faculty of Engineering and Natural Sciences, International University of Sarajevo
- Prashant Kumar Srivastava
- National Heart and Lung Institute, Imperial College London
- Costanza Emanueli
- National Heart and Lung Institute, Imperial College London
- Fabio Martelli
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato
- Simona Greco
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato
- Lina Badimon
- Cardiovascular Program-ICCC, Institut d’Investigació Biomèdica Sant Pau (IIB SANT PAU); CIBERCV, Autonomous University of Barcelona
- Teresa Padro
- Cardiovascular Program-ICCC, Institut d’Investigació Biomèdica Sant Pau (IIB SANT PAU); CIBERCV, Autonomous University of Barcelona
- Mitja Lustrek
- Department of Intelligent Systems, Jozef Stefan Institute
- Markus Scholz
- Group Genetical Statistics and Biomathematical Modelling, Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig
- Maciej Rosolowski
- Group Genetical Statistics and Biomathematical Modelling, Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig
- Marko Jordan
- Department of Intelligent Systems, Jozef Stefan Institute
- Timo Brandenburger
- Medical University of Dusseldorf
- Bettina Benczik
- HUN-REN–SU System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary; Pharmahungary Group
- Bence Agg
- HUN-REN–SU System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary; Pharmahungary Group
- Peter Ferdinandy
- HUN-REN–SU System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary; Pharmahungary Group
- Jörg Janne Vehreschild
- Medical Department 2 (Hematology/Oncology and Infectious Diseases), Center for Internal Medicine, Goethe University Frankfurt, University Hospital
- Bettina Lorenz-Depiereux
- Institute of Epidemiology, Helmholtz Center Munich
- Marcus Dörr
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany; German Centre of Cardiovascular Research (DZHK)
- Oliver Witzke
- Department of Infectious Diseases, West German Centre of Infectious Diseases, University Hospital Essen, University of Duisburg-Essen
- Gabriel Sanchez
- Firalis SA
- Seval Kul
- Firalis SA
- Andy H. Baker
- Centre for Cardiovascular Science, The Queen’s Medical Research Institute, University of Edinburgh
- Guy Fagherazzi
- Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health
- Markus Ollert
- Department of Infection and Immunity, Luxembourg Institute of Health
- Ryan Wereski
- Centre for Cardiovascular Science, University of Edinburgh
- Nicholas L. Mills
- Centre for Cardiovascular Science, University of Edinburgh
- Hüseyin Firat
- Firalis SA
- DOI
- https://doi.org/10.1038/s41467-024-47557-1
- Journal volume & issue
-
Vol. 15,
no. 1
pp. 1 – 12
Abstract
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.
