Humans; Male; Female; Aged; Middle Aged; Europe/epidemiology; Canada/epidemiology; Cohort Studies; Aged, 80 and over; Adult; COVID-19/mortality; COVID-19/virology; COVID-19/genetics; Machine Learning; RNA, Long Noncoding/genetics; Hospital Mortality; SARS-CoV-2/genetics; SARS-CoV-2/isolation & purification; Canada; COVID-19; Europe; RNA, Long Noncoding; SARS-CoV-2; Chemistry (all); Biochemistry, Genetics and Molecular Biology (all); Multidisciplinary; Physics and Astronomy (all)
Abstract :
[en] 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.
Disciplines :
Cardiovascular & respiratory systems
Author, co-author :
Devaux, Yvan ; Cardiovascular Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg. yvan.devaux@lih.lu
Zhang, Lu; Bioinformatics Platform, Luxembourg Institute of Health, Strassen, Luxembourg
Lumley, Andrew I ; Cardiovascular Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
Karaduzovic-Hadziabdic, Kanita; Faculty of Engineering and Natural Sciences, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina
Mooser, Vincent ; Department of Human Genetics, McGill University, Montréal, QC, Canada
Rousseau, Simon ; The Meakins-Christie Laboratories at the Research Institute of the McGill University Heath Centre Research Institute, & Department of Medicine, Faculty of Medicine, McGill University, Montréal, QC, Canada
Badimon, Lina ; Cardiovascular Program-ICCC, Institut d'Investigació Biomèdica Sant Pau (IIB SANT PAU), CIBERCV, Autonomous University of Barcelona, Barcelona, Spain
Padro, Teresa ; Cardiovascular Program-ICCC, Institut d'Investigació Biomèdica Sant Pau (IIB SANT PAU), CIBERCV, Autonomous University of Barcelona, Barcelona, Spain
Lustrek, Mitja ; Department of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia
Scholz, Markus ; Group Genetical Statistics and Biomathematical Modelling, Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
Rosolowski, Maciej; Group Genetical Statistics and Biomathematical Modelling, Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
Jordan, Marko; Department of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia
Brandenburger, Timo; Medical University of Dusseldorf, Dusseldorf, Germany
Benczik, Bettina ; HUN-REN-SU System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary, Pharmahungary Group, Szeged, Hungary
Agg, Bence ; HUN-REN-SU System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary, Pharmahungary Group, Szeged, Hungary
Ferdinandy, Peter; HUN-REN-SU System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary, Pharmahungary Group, Szeged, Hungary
Vehreschild, Jörg Janne ; Medical Department 2 (Hematology/Oncology and Infectious Diseases), Center for Internal Medicine, Goethe University Frankfurt, University Hospital, Frankfurt, Germany ; University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany ; Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Cologne, Germany ; German Centre for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
Lorenz-Depiereux, Bettina; Institute of Epidemiology, Helmholtz Center Munich, Munich, Germany
Dörr, Marcus; Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany, German Centre of Cardiovascular Research (DZHK), Greifswald, Germany
Witzke, Oliver; Department of Infectious Diseases, West German Centre of Infectious Diseases, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
Sanchez, Gabriel; Firalis SA, Huningue, France
Kul, Seval; Firalis SA, Huningue, France
Baker, Andy H ; Centre for Cardiovascular Science, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, Scotland ; CARIM Institute and Department of Pathology, University of Maastricht, Maastricht, The Netherlands
Fagherazzi, Guy; Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
Ollert, Markus ; Department of Infection and Immunity, Luxembourg Institute of Health, Esch-Sur-Alzette, Luxembourg ; Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis (ORCA), University of Southern Denmark, Odense, Denmark
Wereski, Ryan; Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
Mills, Nicholas L ; Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK ; Usher Institute, University of Edinburgh, Edinburgh, UK
European Commission Fonds National de la Recherche Luxembourg Italian Ministry of Health Projects
Funding text :
The authors thank all members of COVIRNA project for their contribution: Claude Pelletier, Petr Nazarov, Adriana Voicu, Irina Carpusca, Eric Schordan, Rodwell Mkhwananzi, Stephanie Boutillier, Louis Chauviere, Joanna Michel, Florent Tessier, Reinhard Schneider, Irina Belaur, Wei Gu, Enrico Petretto, Michaela Noseda, Verena Zuber, Pranay Shah, Leonardo Bottolo, Leon de Windt, Emma Robinson, George Valiotis, Tina Hadzic, Federica Margheri, Chiara Gonzi, Detlef Kindgen-Milles, Christian Vollmer, Thomas Dimski, Emin Tahirovic. Further information on the COVIRNA project can be found at https://covirna.eu/. We dedicate this paper to Claude Pelletier who passed away during the timeframe of the COVIRNA project. His invaluable contribution to data analysis is highly recognized and acknowledged. We are thankful to all the participants of the Predi-COVID study. We also acknowledge the involvement of the interdisciplinary and inter-institutional study team that contributed to Predi-COVID. The full list of the Predi-COVID team can be found here: https://sites.lih.lu/the-predi-covid-study/about-us/project-team/. We would like to thank University of Edinburgh DataLoch (https://dataloch.org) and NHS Lothian Bioresource for their support and assistance with this study. This work uses data provided by patients and collected by the NHS as part of their care and support #DataSavesLives. We are extremely grateful to the 2,648 frontline NHS clinical and research staff and volunteer medical students, who collected data in challenging circumstances; and the generosity of the participants and their families for their individual contributions in these difficult times. We also acknowledge the support of Jeremy J Farrar and Nahoko Shindo. The study was carried out using the clinical-scientific infrastructure of NAPKON (Nationales Pandemie Kohorten Netz, German National Pandemic Cohort Network) and NUKLEUS (NUM Klinische Epidemiologie- und Studienplattform, NUM Clinical Epidemiology and Study Platform) of the Network University Medicine (NUM). We gratefully thank all NAPKON sites who contributed patient data and/or biosamples for this analysis.
2023, N. P. O. A. The Nobel Prize in Physiology or Medicine 2023, https://www.nobelprize.org/prizes/medicine/2023/summary/ (2023).
M. Winkle S.M. El-Daly M. Fabbri G.A. Calin Noncoding RNA therapeutics—challenges and potential solutions Nat. Rev. Drug Discov. 2021 20 629 651 1:CAS:528:DC%2BB3MXhtlKmtbbI 10.1038/s41573-021-00219-z 34145432 8212082
L. Badimon Y. Devaux Transcriptomics research to improve cardiovascular healthcare Eur. Heart J. 2020 41 3296 3298 10.1093/eurheartj/ehaa237 33033839
C.P.C. Gomes et al. Catalyzing transcriptomics research in cardiovascular disease: the CardioRNA COST action CA17129 Noncoding RNA 2019 5 31 30934986 6630366
L. Badimon et al. Cardiovascular RNA markers and artificial intelligence may improve COVID-19 outcome: a position paper from the EU-CardioRNA COST Action CA17129 Cardiovasc. Res. 2021 117 1823 1840 1:CAS:528:DC%2BB3MXisFanu77L 10.1093/cvr/cvab094 33839767 8083253
E.L. Robinson C. Emanueli F. Martelli Y. Devaux Leveraging non-coding RNAs to fight cardiovascular disease: the EU-CardioRNA network Eur. Heart J. 2021 42 4881 4883 1:CAS:528:DC%2BB38XosVCjsr0%3D 10.1093/eurheartj/ehab326 34109376
H. Firat et al. FIMICS: a panel of long noncoding RNAs for cardiovascular conditions Heliyon 2023 9 1:CAS:528:DC%2BB3sXhslKqurY%3D 10.1016/j.heliyon.2023.e13087 36747920 9898641
L. Chauviere et al. Firalink: a bioinformatics pipeline for long non-coding RNA data analysis Noncoding RNA Res. 2023 8 602 604 1:CAS:528:DC%2BB3sXhvFynsLnN 10.1016/j.ncrna.2023.09.002 37771472 10523182
H.A. Mohamed et al. MicroRNAs and cytokines as potential predictive biomarkers for COVID-19 disease progression Sci. Rep. 2023 13 2023NatSR.13.3531M 1:CAS:528:DC%2BB3sXksFalu7w%3D 10.1038/s41598-023-30474-6 36864077 9979137
M. Gelzo et al. Matrix metalloproteinases (MMP) 3 and 9 as biomarkers of severity in COVID-19 patients Sci. Rep. 2022 12 2022NatSR.12.1212G 1:CAS:528:DC%2BB38XhvVeru7g%3D 10.1038/s41598-021-04677-8 35075175 8786927
Z. Fu et al. A virus-derived microRNA-like small RNA serves as a serum biomarker to prioritize the COVID-19 patients at high risk of developing severe disease Cell Discov. 2021 7 48 1:CAS:528:DC%2BB3MXhsVyltL7P 10.1038/s41421-021-00289-8 34226530 8257610
S. Greco et al. HCG18, LEF1AS1 and lncCEACAM21 as biomarkers of disease severity in the peripheral blood mononuclear cells of COVID-19 patients J. Transl. Med. 2023 21 1:CAS:528:DC%2BB3sXit1art7jE 10.1186/s12967-023-04497-6 37884975 10605335
L. Wynants et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal BMJ 2020 369 m1328 10.1136/bmj.m1328 32265220 7222643
M.A. Hausburg K.L. Banton M. Roshon D. Bar-Or Clinically distinct COVID-19 cases share notably similar immune response progression: a follow-up analysis Heliyon 2021 7 1:CAS:528:DC%2BB38XitVerurbI 10.1016/j.heliyon.2020.e05877 33437888
M. Yang et al. Lef1 is transcriptionally activated by Klf4 and suppresses hyperoxia-induced alveolar epithelial cell injury Exp. Lung Res. 2022 48 213 223 1:CAS:528:DC%2BB38Xitl2gu7zI 10.1080/01902148.2022.2108945 35950640
I.S. Forrest et al. Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts Lancet 2023 401 215 225 10.1016/S0140-6736(22)02079-7 36563696
S. Ng S. Masarone D. Watson M.R. Barnes The benefits and pitfalls of machine learning for biomarker discovery Cell Tissue Res. 2023 394 17 31 10.1007/s00441-023-03816-z 37498390 10558383
S.J. Sammut et al. Multi-omic machine learning predictor of breast cancer therapy response Nature 2022 601 623 629 2022Natur.601.623S 1:CAS:528:DC%2BB38Xht1Kqtbs%3D 10.1038/s41586-021-04278-5 34875674
C. Wang W. Lue R. Kaalia P. Kumar J.C. Rajapakse Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma Sci. Rep. 2022 12 2022NatSR.1215425W 1:CAS:528:DC%2BB38Xitlyru7fP 10.1038/s41598-022-19019-5 36104347 9475034
X. Mi B. Zou F. Zou J. Hu Permutation-based identification of important biomarkers for complex diseases via machine learning models Nat. Commun. 2021 12 2021NatCo.12.3008M 10.1038/s41467-021-22756-2 34021151 8140109
E. Dolgin Why rings of RNA could be the next blockbuster drug Nature 2023 622 22 24 2023Natur.622..22D 1:CAS:528:DC%2BB3sXitVKhs73J 10.1038/d41586-023-03058-7 37794271
B.A. Reme J. Gjesvik K. Magnusson Predictors of the post-COVID condition following mild SARS-CoV-2 infection Nat. Commun. 2023 14 2023NatCo.14.5839R 1:CAS:528:DC%2BB3sXhvFKksb3I 10.1038/s41467-023-41541-x 37730740 10511472
T. Thaweethai et al. Development of a definition of postacute sequelae of SARS-CoV-2 infection JAMA 2023 329 1934 1946 1:CAS:528:DC%2BB3sXht1KqtL%2FL 10.1001/jama.2023.8823 37278994 10214179
Global Burden of Disease Long, C. C. et al. Estimated global proportions of individuals with persistent fatigue, cognitive, and respiratory symptom clusters following symptomatic COVID-19 in 2020 and 2021. JAMA 328, 1604–1615. https://doi.org/10.1001/jama.2022.18931 (2022).
P. Wilmes et al. SARS-CoV-2 transmission risk from asymptomatic carriers: Results from a mass screening programme in Luxembourg Lancet Reg. Health Eur. 2021 4 10.1016/j.lanepe.2021.100056 33997830 7912359
G. Fagherazzi et al. Protocol for a prospective, longitudinal cohort of people with COVID-19 and their household members to study factors associated with disease severity: the Predi-COVID study BMJ Open 2020 10 e041834 10.1136/bmjopen-2020-041834 33234656 7684799
M. Schons et al. The German National Pandemic Cohort Network (NAPKON): rationale, study design and baseline characteristics Eur. J. Epidemiol. 2022 37 849 870 10.1007/s10654-022-00896-z 35904671 9336157
K. Tremblay et al. The Biobanque quebecoise de la COVID-19 (BQC19)-A cohort to prospectively study the clinical and biological determinants of COVID-19 clinical trajectories PLoS One 2021 16 e0245031 1:CAS:528:DC%2BB3MXhtFGisLvJ 10.1371/journal.pone.0245031 34010280 8133500
D.J. Stekhoven P. Bühlmann MissForest—non-parametric missing value imputation for mixed-type data Bioinformatics 2011 28 112 118 10.1093/bioinformatics/btr597 22039212
C.W. Law Y. Chen W. Shi G.K. Smyth voom: precision weights unlock linear model analysis tools for RNA-seq read counts Genome Biol. 2014 15 10.1186/gb-2014-15-2-r29 24485249 4053721
M.I. Love W. Huber S. Anders Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biol. 2014 15 10.1186/s13059-014-0550-8 25516281 4302049
M.B. Kursa W.R. Rudnicki Feature selection with the Boruta package J. Stat. Softw. 2010 36 1 13 10.18637/jss.v036.i11
Code accompanying the paper “Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality”. https://doi.org/10.24433/CO.6166592.v1
