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Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality

Author

Listed:
  • Yvan Devaux

    (Luxembourg Institute of Health)

  • Lu Zhang

    (Luxembourg Institute of Health)

  • Andrew I. Lumley

    (Luxembourg Institute of Health)

  • Kanita Karaduzovic-Hadziabdic

    (International University of Sarajevo)

  • Vincent Mooser

    (McGill University)

  • Simon Rousseau

    (McGill University)

  • Muhammad Shoaib

    (University of Luxembourg)

  • Venkata Satagopam

    (University of Luxembourg)

  • Muhamed Adilovic

    (International University of Sarajevo)

  • Prashant Kumar Srivastava

    (Imperial College London)

  • Costanza Emanueli

    (Imperial College London)

  • Fabio Martelli

    (IRCCS Policlinico San Donato)

  • Simona Greco

    (IRCCS Policlinico San Donato)

  • Lina Badimon

    (Autonomous University of Barcelona)

  • Teresa Padro

    (Autonomous University of Barcelona)

  • Mitja Lustrek

    (Jozef Stefan Institute)

  • Markus Scholz

    (University of Leipzig)

  • Maciej Rosolowski

    (University of Leipzig)

  • Marko Jordan

    (Jozef Stefan Institute)

  • Timo Brandenburger

    (Medical University of Dusseldorf)

  • Bettina Benczik

    (Semmelweis University, Budapest, Hungary; Pharmahungary Group)

  • Bence Agg

    (Semmelweis University, Budapest, Hungary; Pharmahungary Group)

  • Peter Ferdinandy

    (Semmelweis University, Budapest, Hungary; Pharmahungary Group)

  • Jörg Janne Vehreschild

    (Goethe University Frankfurt, University Hospital
    Faculty of Medicine and University Hospital Cologne
    Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf
    partner site Bonn-Cologne)

  • Bettina Lorenz-Depiereux

    (Helmholtz Center Munich)

  • Marcus Dörr

    (University Medicine Greifswald, Greifswald, Germany; German Centre of Cardiovascular Research (DZHK))

  • Oliver Witzke

    (University Hospital Essen, University of Duisburg-Essen)

  • Gabriel Sanchez

    (Firalis SA)

  • Seval Kul

    (Firalis SA)

  • Andy H. Baker

    (University of Edinburgh
    University of Maastricht)

  • Guy Fagherazzi

    (Luxembourg Institute of Health)

  • Markus Ollert

    (Luxembourg Institute of Health
    University of Southern Denmark)

  • Ryan Wereski

    (University of Edinburgh)

  • Nicholas L. Mills

    (University of Edinburgh
    University of Edinburgh)

  • Hüseyin Firat

    (Firalis SA)

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.

Suggested Citation

  • Yvan Devaux & Lu Zhang & Andrew I. Lumley & Kanita Karaduzovic-Hadziabdic & Vincent Mooser & Simon Rousseau & Muhammad Shoaib & Venkata Satagopam & Muhamed Adilovic & Prashant Kumar Srivastava & Costa, 2024. "Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47557-1
    DOI: 10.1038/s41467-024-47557-1
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    References listed on IDEAS

    as
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    2. Xinlei Mi & Baiming Zou & Fei Zou & Jianhua Hu, 2021. "Permutation-based identification of important biomarkers for complex diseases via machine learning models," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    3. Elie Dolgin, 2023. "Why rings of RNA could be the next blockbuster drug," Nature, Nature, vol. 622(7981), pages 22-24, October.
    4. B-A. Reme & J. Gjesvik & K. Magnusson, 2023. "Predictors of the post-COVID condition following mild SARS-CoV-2 infection," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
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