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.
