IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-47557-1.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-47557-1
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-47557-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tong, Jianfeng & Liu, Zhenxing & Zhang, Yong & Zheng, Xiujuan & Jin, Junyang, 2023. "Improved multi-gate mixture-of-experts framework for multi-step prediction of gas load," Energy, Elsevier, vol. 282(C).
    2. Asma Shaheen & Javed Iqbal, 2018. "Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm," Sustainability, MDPI, vol. 10(3), pages 1-20, March.
    3. Ramón Ferri-García & María del Mar Rueda, 2022. "Variable selection in Propensity Score Adjustment to mitigate selection bias in online surveys," Statistical Papers, Springer, vol. 63(6), pages 1829-1881, December.
    4. Ghosh, Indranil & Chaudhuri, Tamal Datta & Alfaro-Cortés, Esteban & Gámez, Matías & García, Noelia, 2022. "A hybrid approach to forecasting futures prices with simultaneous consideration of optimality in ensemble feature selection and advanced artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    5. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    6. Sangjin Kim & Jong-Min Kim, 2019. "Two-Stage Classification with SIS Using a New Filter Ranking Method in High Throughput Data," Mathematics, MDPI, vol. 7(6), pages 1-16, May.
    7. Arjan S. Gosal & Janine A. McMahon & Katharine M. Bowgen & Catherine H. Hoppe & Guy Ziv, 2021. "Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness," Land, MDPI, vol. 10(6), pages 1-14, May.
    8. Zhao-Yue Chen & Hervé Petetin & Raúl Fernando Méndez Turrubiates & Hicham Achebak & Carlos Pérez García-Pando & Joan Ballester, 2024. "Population exposure to multiple air pollutants and its compound episodes in Europe," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    9. Schrader, Silja & Graham, Sonia & Campbell, Rebecca & Height, Kaitlyn & Hawkes, Gina, 2024. "Grower attitudes and practices toward area-wide management of cropping weeds in Australia," Land Use Policy, Elsevier, vol. 137(C).
    10. Bram Janssens & Matthias Bogaert & Mathijs Maton, 2023. "Predicting the next Pogačar: a data analytical approach to detect young professional cycling talents," Annals of Operations Research, Springer, vol. 325(1), pages 557-588, June.
    11. Cooray, Upul & Watt, Richard G. & Tsakos, Georgios & Heilmann, Anja & Hariyama, Masanori & Yamamoto, Takafumi & Kuruppuarachchige, Isuruni & Kondo, Katsunori & Osaka, Ken & Aida, Jun, 2021. "Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis," Social Science & Medicine, Elsevier, vol. 291(C).
    12. Simon Besnard & Nuno Carvalhais & M Altaf Arain & Andrew Black & Benjamin Brede & Nina Buchmann & Jiquan Chen & Jan G P W Clevers & Loïc P Dutrieux & Fabian Gans & Martin Herold & Martin Jung & Yoshik, 2019. "Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-22, February.
    13. Francesco Sartor & Jonathan P. Moore & Hans-Peter Kubis, 2021. "Plasma Interleukin-10 and Cholesterol Levels May Inform about Interdependences between Fitness and Fatness in Healthy Individuals," IJERPH, MDPI, vol. 18(4), pages 1-19, February.
    14. Nawin Raj, 2022. "Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning," Mathematics, MDPI, vol. 10(23), pages 1-23, November.
    15. Piotr Pomorski & Denise Gorse, 2023. "Improving Portfolio Performance Using a Novel Method for Predicting Financial Regimes," Papers 2310.04536, arXiv.org.
    16. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2022. "A babel of web-searches: Googling unemployment during the pandemic," Labour Economics, Elsevier, vol. 74(C).
    17. Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.
    18. Abolfazl Mollalo & Kiara M. Rivera & Behzad Vahedi, 2020. "Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States," IJERPH, MDPI, vol. 17(12), pages 1-13, June.
    19. Marc Deffland & Claudia Spies & Bjoern Weiss & Niklas Keller & Mirjam Jenny & Jochen Kruppa & Felix Balzer, 2020. "Effects of pain, sedation and delirium monitoring on clinical and economic outcome: A retrospective study," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-14, September.
    20. Faisal Alsayegh & Moh A Alkhamis & Fatima Ali & Sreeja Attur & Nicholas M Fountain-Jones & Mohammad Zubaid, 2022. "Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-15, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47557-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.