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

Abstract

Background: The rapid evolution of the COVID-19 pandemic and subsequent global immunization efforts have rendered early metabolomics studies potentially outdated, as they primarily involved non-exposed, non-vaccinated populations. This paper presents a predictive model developed from up-to-date metabolomics data integrated with clinical data to estimate early mortality risk in critically ill COVID-19 patients. Our study addresses the critical gap in current research by utilizing current patient samples, providing fresh insights into the pathophysiology of the disease in a partially immunized global population.

Methods: One hundred elderly patients with severe COVID-19 infection, including 46 survivors and 54 non-survivors, were recruited in January-February 2023 at the Second Hospital affiliated with Harbin Medical University. A predictive model within 24 h of admission was developed using blood metabolomics and clinical data. Differential metabolite analysis and other techniques were used to identify relevant characteristics. Model performance was assessed by comparing the area under the receiver operating characteristic curve (AUROC). The final prediction model was externally validated in a cohort of 50 COVID-19 elderly critically ill patients at the First Hospital affiliated with Harbin Medical University during the same period.

Results: Significant disparities in blood metabolomics and laboratory parameters were noted between individuals who survived and those who did not. One metabolite indicator, Itaconic acid, and four laboratory tests (LYM, IL-6, PCT, and CRP), were identified as the five variables in all four models. The external validation set demonstrated that the KNN model exhibited the highest AUC of 0.952 among the four models. When considering a 50% risk of mortality threshold, the validation set displayed a sensitivity of 0.963 and a specificity of 0.957.

Conclusions: The prognostic outcome of COVID-19 elderly patients is significantly influenced by the levels of Itaconic acid, LYM, IL-6, PCT, and CRP upon admission. These five indicators can be utilized to assess the mortality risk in affected individuals.

Keywords: COVID-19; Machine learning; Metabolomics; Mortality; Predictive model.

Fig. 1

The study flow chart

Fig. 2

(A) Detected metabolites analysis of serum samples from survivors and non-survivors. (B) Volcano plots show the change in transformed P-value (− log10) against the log2 (survival/mortality). Gray dashed lines: cut-off values (ratio > 1.5 and p-value < 0.05). Orange dots: High expression of metabolites (23). Blue dots: Low expression of metabolites (55). The size of the points varies according to the absolute value of [Log2(survival/mortality)]. (C) Principal Component Analysis (PCA). The green and red points are survival and mortality. (D) Variable important in projection (VIP). (E) shows the AUC values of Itaconic acid

Fig. 3

(A) Violin plots: The median is denoted by a white dot, the interquartile range is represented by a thick grey bar, and the remaining distribution. (B) The S-shaped correlation pattern between risk score and mortality probability shows that when the risk score is greater than 3, the mortality risk exceeds 50%. (C) The risk scores for both surviving and deceased patients are approximately normally distributed. (D) Use Python 3.7 to calculate the performance curves of each model