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

doi:10.1038/s41467-024-47557-1

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 BrandenburgerBettina 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

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Article number	4259
Number of pages	12
Journal	Nature Communications
Volume	15
Issue number	1
DOIs	https://doi.org/10.1038/s41467-024-47557-1
Publication status	Published - 1 Dec 2024

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Devaux, Y., Zhang, L., Lumley, A. I., Karaduzovic-Hadziabdic, K., Mooser, V., Rousseau, S., Shoaib, M., Satagopam, V., Adilovic, M., Srivastava, P. K., Emanueli, C., Martelli, F., Greco, S., Badimon, L., Padro, T., Lustrek, M., Scholz, M., Rosolowski, M., Jordan, M., ... Firat, H. (2024). Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality. Nature Communications, 15(1), Article 4259. https://doi.org/10.1038/s41467-024-47557-1

@article{74943cd350a748c29dc1c2136fa237ec,

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

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{\textquoteright}s adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.",

author = "Yvan Devaux and Lu Zhang and Lumley, {Andrew I.} and Kanita Karaduzovic-Hadziabdic and Vincent Mooser and Simon Rousseau and Muhammad Shoaib and Venkata Satagopam and Muhamed Adilovic and Srivastava, {Prashant Kumar} and Costanza Emanueli and Fabio Martelli and Simona Greco and Lina Badimon and Teresa Padro and Mitja Lustrek and Markus Scholz and Maciej Rosolowski and Marko Jordan and Timo Brandenburger and Bettina Benczik and Bence Agg and Peter Ferdinandy and Vehreschild, {J{\"o}rg Janne} and Bettina Lorenz-Depiereux and Marcus D{\"o}rr and Oliver Witzke and Gabriel Sanchez and Seval Kul and Baker, {Andy H.} and Guy Fagherazzi and Markus Ollert and Ryan Wereski and Mills, {Nicholas L.} and H{\"u}seyin Firat",

note = "Funding Information: 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. Publisher Copyright: {\textcopyright} The Author(s) 2024.",

year = "2024",

month = dec,

day = "1",

doi = "10.1038/s41467-024-47557-1",

language = "English",

volume = "15",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Publishing Group",

number = "1",

}

Devaux, Y, Zhang, L, Lumley, AI, Karaduzovic-Hadziabdic, K, Mooser, V, Rousseau, S, Shoaib, M, Satagopam, V, Adilovic, M, Srivastava, PK, Emanueli, C, Martelli, F, Greco, S, Badimon, L, Padro, T, Lustrek, M, Scholz, M, Rosolowski, M, Jordan, M, Brandenburger, T, Benczik, B, Agg, B, Ferdinandy, P, Vehreschild, JJ, Lorenz-Depiereux, B, Dörr, M, Witzke, O, Sanchez, G, Kul, S, Baker, AH, Fagherazzi, G, Ollert, M, Wereski, R, Mills, NL & Firat, H 2024, 'Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality', Nature Communications, vol. 15, no. 1, 4259. https://doi.org/10.1038/s41467-024-47557-1

TY - JOUR

T1 - Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality

AU - Devaux, Yvan

AU - Zhang, Lu

AU - Lumley, Andrew I.

AU - Karaduzovic-Hadziabdic, Kanita

AU - Mooser, Vincent

AU - Rousseau, Simon

AU - Shoaib, Muhammad

AU - Satagopam, Venkata

AU - Adilovic, Muhamed

AU - Srivastava, Prashant Kumar

AU - Emanueli, Costanza

AU - Martelli, Fabio

AU - Greco, Simona

AU - Badimon, Lina

AU - Padro, Teresa

AU - Lustrek, Mitja

AU - Scholz, Markus

AU - Rosolowski, Maciej

AU - Jordan, Marko

AU - Brandenburger, Timo

AU - Benczik, Bettina

AU - Agg, Bence

AU - Ferdinandy, Peter

AU - Vehreschild, Jörg Janne

AU - Lorenz-Depiereux, Bettina

AU - Dörr, Marcus

AU - Witzke, Oliver

AU - Sanchez, Gabriel

AU - Kul, Seval

AU - Baker, Andy H.

AU - Fagherazzi, Guy

AU - Ollert, Markus

AU - Wereski, Ryan

AU - Mills, Nicholas L.

AU - Firat, Hüseyin

N1 - Funding Information: 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. Publisher Copyright: © The Author(s) 2024.

PY - 2024/12/1

Y1 - 2024/12/1

N2 - 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.

AB - 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.

U2 - 10.1038/s41467-024-47557-1

DO - 10.1038/s41467-024-47557-1

M3 - Article

SN - 2041-1723

VL - 15

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 4259

ER -

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

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