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Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality [Elektronski vir]Devaux, Yvan ...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.Source: Nature communications [Elektronski vir]. - ISSN 2041-1723 (Vol. 15, article no. 4259, 20 May 2024, str. 1-12)Type of material - e-article ; adult, seriousPublish date - 2024Language - englishCOBISS.SI-ID - 196251651
Author
Devaux, Yvan |
Zhang, Lu |
Lumley, Andrew I. |
Luštrek, Mitja |
Firat, Hüsseyin
Topics
Covid-19 |
Strojno učenje |
Smrtnost |
RNK |
napovedni modeli |
long noncoding RNA |
predictive model
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Database name | Field | Year |
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Devaux, Yvan | ![]() |
Zhang, Lu | ![]() |
Lumley, Andrew I. | ![]() |
Luštrek, Mitja | 23581 |
Firat, Hüsseyin | ![]() |
Source: Personal bibliographies
and: SICRIS
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