Prediction of COVID‐19 severity using machine learning

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Dear Editor,Prediction of COVID-19 severity is a critical task in the decision-making process during the initial stages of the disease, enabling personalised surveillance and care of COVID-19 patients.To develop a machine learning (ML) model for the prediction of COVID-19 severity, a consortium of 15 institutions from 12 European countries analysed expression data of 2906 blood long noncoding RNAs (lncRNAs) and clinical data collected from four independent cohorts, totalling 564 patients with COVID-19.This predictive model based on age and five lncRNAs predicted disease severity with an area under the receiver operating characteristic curve (AUC) of .875 [.868-.881] and an accuracy of .783[.775-.791].The sudden onset of the COVID-19 pandemic caught the world unprepared, leading to more than 774 million confirmed cases and over 7 million reported deaths worldwide (over a period from January 2020 to March 2024), according to the World Health Organization (WHO). 1 Other than having an impact on the respiratory system, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can also infect nonpulmonary cells such as cardiac and brain cells leading to cardiovascular or neurological symptoms.2 With the recent advances in high throughput sequencing, a large number of RNA signatures have emerged as promising biomarkers involved in the progression of various diseases, including cardiovascular diseases.3 As a response to the COVID-19 pandemic, partners of the EU-CardioRNA COST Action network 4-6 joined forces in the H2020-funded COVIRNA project to develop an RNA-based diagnostic test using artificial intelligence (AI) that can help predict clinical outcomes after COVID-19.7 We chose to implement a targeted sequencing approach using the FIMICS panel of 2906 cardiac-enriched or heart failure-associated lncRNAs previously characterised by our consortium.8 In the present study, we aimed to apply the FIMICS panel to identify lncRNAs that will predict disease severity of COVID-19 patients.We used an approach based on ML to conduct the predictive analysis, as ML

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