Multivariate Prognostic Model for Predicting the Outcome of Critically Ill Patients Using the Aromatic Metabolites Detected by Gas Chromatography-Mass Spectrometry

Abstract

A number of aromatic metabolites of tyrosine and phenylalanine have been investigated as new perspective markers of infectious complications in the critically ill patients of intensive care units (ICUs). The goal of our research was to build a multivariate model for predicting the outcome of critically ill patients regardless of the main pathology on the day of admission to the ICU. Eight aromatic metabolites were detected in serum using gas chromatography-mass spectrometry. The samples were obtained from the critically ill patients (n = 79), including survivors (n = 44) and non-survivors (n = 35), and healthy volunteers (n = 52). The concentrations of aromatic metabolites were statistically different in the critically ill patients and healthy volunteers. A univariate model for predicting the outcome of the critically ill patients was based on 3-(4-hydroxyphenyl)lactic acid (p-HPhLA). Two multivariate classification models were built based on aromatic metabolites using SIMCA method. The predictive models were compared with the clinical APACHE II scale using ROC analysis. For all of the predictive models the areas under the ROC curve were close to one. The aromatic metabolites (one or a number of them) can be used in clinical practice for the prognosis of the outcome of critically ill patients on the day of admission to the ICU.

Promising Markers of Inflammatory and Gut Dysbiosis in Patients with Post-COVID-19 Syndrome

“…We hypothesized that some microbial metabolites, which were previously elevated in different groups of patients with infectious complications in the intensive care units, could be elevated in comparison with healthy volunteers [ 23 , 24 ]. These metabolites, i.e., phenyllactic, 4-hydroxyphenylacetic, and 4-hydroxyphenyllactic acids, were not statistically elevated in the post-COVID-19 patients.…”

Analysis of 4-Hydroxyphenyllactic Acid and Other Diagnostically Important Metabolites of α-Amino Acids in Human Blood Serum Using a Validated and Sensitive Ultra-High-Pressure Liquid Chromatography-Tandem Mass Spectrometry Method

“…According to the data obtained, we can state that the GC-MS and UPLC-MS/MS data are completely comparable. Moreover, two methods were validated in similar concentration ranges, and we can assume that the clinical results that described the prognostic significance of p-HPhLA in critically ill patients and were previously reported using GC-MS [5] will be applicable for UPLC-MS/MS. Considering the excellent validation characteristics of the developed UPLC-MS/MS method in comparison with GC-MS, we believe that we are as close as possible to introducing our method into the routine clinical practice of medical centers that have the appropriate equipment.…”

“…We have previously demonstrated that a number of metabolites of aromatic α-amino acids are ubiquitous, and their low serum concentrations reflect the integration of the human metabolism with its microbiota [4]. Some of the metabolites can be used as prognostic markers of infectious complications and outcomes in severe patients with various pathologies [5] and in patients with post-COVID-19 syndrome [6]. These metabolites include phenyl-containing microbial and endogenous derivatives of phenylalanine (phenyllactic (PhLA) and phenylpropionic (PhPA) acids) and tyrosine (4-hydroxyphenyllactic (p-HPhLA), 4-hydroxyphenylacetic (p-HPhAA), 4-hydroxybenzoic (p-HBA), and 4-hydroxyphenylpropionic (p-HPhPA) acids).…”

“…These metabolites include phenyl-containing microbial and endogenous derivatives of phenylalanine (phenyllactic (PhLA) and phenylpropionic (PhPA) acids) and tyrosine (4-hydroxyphenyllactic (p-HPhLA), 4-hydroxyphenylacetic (p-HPhAA), 4-hydroxybenzoic (p-HBA), and 4-hydroxyphenylpropionic (p-HPhPA) acids). These compounds, both together and separately (in particular, p-HPhLA), were used to build predictive models in blood serum and cerebrospinal fluid using liquid-liquid extraction or microextraction via packed sorbent coupled with gas chromatography-mass spectrometry (GC-MS) [4][5][6]. The study that has described the results of a univariate model using p-HPhLA and two multivariate classification models is of particular interest, because it has demonstrated that aromatic metabolites (one or a number of them) can be used in clinical practice for the prognosis of the outcomes of critically ill patients on the day of admission to the intensive care unit [5].…”

“…These compounds, both together and separately (in particular, p-HPhLA), were used to build predictive models in blood serum and cerebrospinal fluid using liquid-liquid extraction or microextraction via packed sorbent coupled with gas chromatography-mass spectrometry (GC-MS) [4][5][6]. The study that has described the results of a univariate model using p-HPhLA and two multivariate classification models is of particular interest, because it has demonstrated that aromatic metabolites (one or a number of them) can be used in clinical practice for the prognosis of the outcomes of critically ill patients on the day of admission to the intensive care unit [5]. Thus, the development of a reliable method for the determination of aromatic metabolites is the next step towards their active clinical use.…”