ArticleNature communications2024
Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- The Janus face of host LncRNA in viral infections: Defender or collaborator?Communications biology · 2026Review
- Inflammation and iron metabolism dysregulation as hallmarks of COVID-19 severity.Frontiers in immunology · 2026Observational
- From molecules to meaning: non-coding RNAs as biomarkers in obstructive sleep apnea.Biomarkers in medicine · 2025Article
- Advances in Wastewater-Based Epidemiology for Pandemic Surveillance: Methodological Frameworks and Future Perspectives.Microorganisms · 2025Review
- LEF1-AS1 Deregulation in the Peripheral Blood of Patients with Persistent Post-COVID Symptoms.International journal of molecular sciences · 2025Article
- Association of LEF1-AS1 with cardiovascular and neurological complications of COVID-19.Journal of molecular and cellular cardiology plus · 2025Article
- Prediction of COVID-19 severity using machine learning.Clinical and translational medicine · 2024Article
- Multiomic biomarkers after cardiac arrest.Intensive care medicine experimental · 2024Review
- Machine learning for catalysing the integration of noncoding RNA in research and clinical practice.EBioMedicine · 2024Review
- Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.Nature communications · 2024Article
- Computational Biology in the Discovery of Biomarkers in the Diagnosis, Treatment and Management of Cardiovascular Diseases.Cardiology and cardiovascular medicine · 2024Article
- A multi-omics strategy to understand PASC through the RECOVER cohorts: a paradigm for a systems biology approach to the study of chronic conditions.Frontiers in systems biology · 2024Review
Corrections and comments
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Authors and funding
35 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.