Integration of metabolomics methodologies for the development of predictive models for mortality risk in patients with severe COVID-19.

Background The global spread of Coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has prompted the investigation of a predictive model for early mortality risk estimation in critical-type COVID-19 patients through the integration of metabolomics and clinical data using machine learning techniques in this study. Methods One hundred patients with severe COVID-19 infection, comprising 46 survivors and 53 non-survivors, were enrolled from the Second Hospital affiliated with Harbin Medical University. A predictive model was developed within 24 hours of admission utilizing blood metabolomics and clinical data. Differential metabolite analysis and other techniques were employed to identify relevant features. The performance of the models was evaluated by comparing the area under the receiver operating characteristic curve (AUROC). The ultimate predictive model underwent external validation with a cohort of 50 critical COVID-19 patients from the First Hospital affiliated with Harbin Medical University. Results Significant disparities in blood metabolomics and laboratory parameters were noted between individuals who survived and those who did not. Two metabolite indicators, Itaconic acid and 3-Oxalomalate, along with four laboratory tests (LYM, IL-6, PCT, and CRP), were identified as the six variables in all four models. The external validation set demonstrated that the KNN model exhibited the highest AUC of 0.935 among the four models. When considering a 50% risk of mortality threshold, the validation set displayed a sensitivity of 0.926 and a specificity of 0.934. Conclusions The prognostic outcome of COVID-19 patients is significantly influenced by the levels of Itaconic acid, 3-Oxalomalate, LYM, IL-6, PCT, and CRP upon admission. These six indicators can be utilized to assess the mortality risk in affected individuals.