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![Partial least squares discriminant analysis (PLS-DA) for the 2–17-year-old cohorts based on the preprocessed original data. a Metabolomic profiling data. b Inflammatory protein mediator profiling data. c Combined biomarker profiling data. The three-dimensional PLS-DA score scatterplots show the distribution of observations (red dots, pediatric intensive care unit [PICU] sepsis patients; blue dots, emergency department [ED] sepsis patients; green dots, ED controls) in the three-dimensional space formed by PLS components (PLS1, PLS2, and PLS3). During the model construction, a discriminant plane (PLS component) was found in which the projected observations were well separated according to the class (PICU sepsis, ED sepsis, ED controls). The sphere describes the 95 % confidence interval of the Hotelling’s T 2 distribution](profile/Hans-Vogel/publication/281637033/figure/fig3/AS:281512948846612@1444129325541/Partial-least-squares-discriminant-analysis-PLS-DA-for-the-2-17-year-old-cohorts-based.png)
Partial least squares discriminant analysis (PLS-DA) for the 2–17-year-old cohorts based on the preprocessed original data. a Metabolomic profiling data. b Inflammatory protein mediator profiling data. c Combined biomarker profiling data. The three-dimensional PLS-DA score scatterplots show the distribution of observations (red dots, pediatric intensive care unit [PICU] sepsis patients; blue dots, emergency department [ED] sepsis patients; green dots, ED controls) in the three-dimensional space formed by PLS components (PLS1, PLS2, and PLS3). During the model construction, a discriminant plane (PLS component) was found in which the projected observations were well separated according to the class (PICU sepsis, ED sepsis, ED controls). The sphere describes the 95 % confidence interval of the Hotelling’s T 2 distribution
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Introduction:
The first steps in goal-directed therapy for sepsis are early diagnosis followed by appropriate triage. These steps are usually left to the physician's judgment, as there is no accepted biomarker available. We aimed to determine biomarker phenotypes that differentiate children with sepsis who require intensive care from those who do...
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... cohort and 3 (4 %) in the ED sepsis cohort. These outliers were ex- cluded from subsequent analyses. A supervised PLS-DA showed that the three different cohorts were well clus- tered, with specific metabolic profiles for each. The model showed excellent goodness of fit (cumulative R 2 Y =0.56) and goodness of prediction (cumulative Q 2 =0.47) (Fig. 2a). The OPLS-DA method was applied to compare metabolic variance in patient groups consisting of only two classes. The score scatterplots for each statistical analysis show clear separation of groups, with high values for the R 2 Y and Q 2 parameters (Fig. 3a and Table 2). The predictive accuracy statistics for differentiating PICU sepsis ...
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... E7b). A total of 20 outliers comprising 13 (14 %) in the PICU sepsis cohort, 4 (5 %) in the ED sepsis cohort, and 3 (5 %) in the ED control cohort were excluded from further ana- lyses. The PLS-DA model shows that the three different cohorts are reasonably well clustered, with an R 2 Y cumu- lative score of 0.54 and a Q 2 cumulative score of 0.47 (Fig. 2b). The score scatterplots for each OPLS-DA statis- tical analysis show clear separation of groups, with high values for the R 2 Y and Q 2 parameters (Fig. 3b and Table 2). The predictive accuracy statistics for differentiating PICU sepsis from ED sepsis ( Table 2) showed accuracy of 0.78 and AUROC of 0.88 (SD 0.03), which were not as ...
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... E7c). There were eight outliers comprising seven (7 %) in the PICU sepsis cohort and one (1 %) in the ED sepsis co- hort. These outliers were excluded from subsequent ana- lyses. A supervised PLS-DA shows that the three different cohorts are well clustered, with excellent model descrip- tive values: cumulative R 2 Y 0.63 and cumulative Q 2 0.56 (Fig. 2c). The score scatterplots for each OPLS-DA statis- tical analysis show clear separation of the groups, with high values for the R 2 Y and Q 2 parameters ( Fig. 3c and Table 2). The predictive accuracy statistics for differentiat- ing PICU sepsis from ED sepsis ( Table 2) show accuracy of 0.87 and AUROC 0.95 (SD 0.01). This model had very ...
Citations
... Most of concentrates on original biomarkers of appendicitis have focused on single biomarkers and to date have not brought about adequate sensitivity and specificity for appendicitis diagnosis [19,20]. The possibility to utilize precision medicine strategies, for example, metabolomics, proteomics, transcriptomics and genomics, altogether known as 'omics', has been displayed to build the strength of diagnosis in pediatric appendicitis and other inflammatory circumstances [21][22][23]. With ongoing headways in molecular and biochemical innovations, it is feasible to distinguish various biomarkers all the while in a single blood or urine test [16]. ...
A ruptured appendix is quite possibly of the most regular careful crisis in pediatric medical procedure. Complicated appendicitis can develop with attached peritonitis portrayed by the dispersion of the pathological interaction to the peritoneal cavity, subsequently creating general or restricted inflammation of the peritoneum. The ability to expect the chance of perforation in acute appendicitis can coordinate brief management and lower morbidity. There are no particular symptoms that could be utilized to expect complicated infected appendix, and diagnostic hints incorporate a longer time of symptoms, diffuse peritoneal signs, high fever, raised leukocytosis , CRP, hyponatremia, and high ESR. Imagistic strategies, especially US and CT, are helpful yet not adequate. There are no traditional inflammation biomarkers ready to foresee the advancement of simple to complicated appendicitis alone, yet the prescient limit of novel biomarkers is being researched. The point of this review is to lay out the predictors that might help physicians in convenient recognizing pediatric patients determined to have intense appendicitis in risk of creating a ruptured appendix with development to attached peritonitis.
... These changes in metabolites which were involved in the processes of fatty acid decomposition enhancement, ketoacidosis and amino acid metabolism disorders, hepatic glycogen catabolism, and the destruction of glycerol phospholipid and sulfur metabolism, have been found to contribute to pediatric sepsis. Furthermore, Mickiewicz et al. 86,87 also studied the metabolomics of septic children aged 2-17 years and 1-23 months via targeted metabolomics and found seven metabolites (dimethylamine, mannose, 3-methyl-2-oxovalerate, 3hydroxyisovalerate, alanine, O-Acetylcholine, and acetate) could distinguish the acute and critical severity of the disease in children with severe sepsis. However, there is no pathogen information on sepsis patients in these two studies. ...
Severe sepsis causes organ dysfunction and continues to be the leading reason for pediatric death worldwide. Early recognition of sepsis could substantially promote precision treatment and reduce the risk of pediatric death. The host cellular response to infection during sepsis between adults and pediatrics could be significantly different. A growing body of studies focused on finding markers in pediatric sepsis in recent years using multi‐omics approaches. This narrative review summarized the progress in studying pediatric sepsis biomarkers from genome, transcript, protein, and metabolite levels according to the omics technique that has been applied for biomarker screening. It is most likely not a single biomarker could work for precision diagnosis of sepsis, but a panel of markers and probably a combination of markers detected at multi‐levels. Importantly, we emphasize the importance of group distinction of infectious agents in sepsis patients for biomarker identification, because the host response to infection of bacteria, virus, or fungus could be substantially different and thus the results of biomarker screening. Further studies on the investigation of sepsis biomarkers that were caused by a specific group of infectious agents should be encouraged in the future, which will better improve the clinical execution of personalized medicine for pediatric sepsis.
... Reported AUC values ranged from 0.88-0.96 [44]. Multiomic-based profiles that can be used to predict severity of disease and necessity for certain treatments are valuable, especially when certain expertise is not available. ...
This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2023. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2023 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .
... Studies from a group in Calgary, Canada, focused on predicting which patients will require care in a pediatric intensive care unit (PICU), as compared to those that can be managed in the pediatric emergency department (PED). 66,67 The final analysis based on seven metabolites differentiated between PICU-sepsis and PEDsepsis for 1-month-17-year-old patients with an AUROC of 0.93. The seven metabolites were dimethylamine, mannose, 3-methyl-2-oxovalerate, 3-hydroxyisovalerate, alanine, O-acetylcholine, and acetate. ...
... 69 While only a single case study has looked exclusively at sepsis caused by pneumonia, 65 almost half of the children included in the other sepsis studies initially had pneumonia leading to sepsis. 66 Given the significant proportion of pediatric sepsis patients that have an underlying LRTI, it is important to be able to predict which patients with LRTIs will develop sepsis and how severe their course will be. Future metabolomic studies could aim to identify metabolic patterns that can distinguish between children with LRTIs that develop sepsis and those that do not. ...
Lower respiratory tract infections (LRTIs) are a leading cause of morbidity and mortality in children. The ability of healthcare providers to diagnose and prognose LRTIs in the pediatric population remains a challenge, as children can present with similar clinical features regardless of the underlying pathogen or ultimate severity. Metabolomics, the large-scale analysis of metabolites and metabolic pathways offers new tools and insights that may aid in diagnosing and predicting the outcomes of LRTIs in children. This review highlights the latest literature on the clinical utility of metabolomics in providing care for children with bronchiolitis, pneumonia, COVID-19, and sepsis. This article summarizes current metabolomics approaches to diagnosing and predicting the course of pediatric lower respiratory infections.This article highlights the limitations to current metabolomics research and highlights future directions for the field. This article summarizes current metabolomics approaches to diagnosing and predicting the course of pediatric lower respiratory infections. This article highlights the limitations to current metabolomics research and highlights future directions for the field.
... The purpose of this pilot study was to compare results obtained by naïve operators who used these two automated, open-source programs (BATMAN and rDolphin) to carry out identification and quantification of 1D 1 H-NMR metabolomics spectral data with the results obtained by manual profiling by an experienced spectroscopist. A targeted approach was needed for clinical diagnostic purposes in the original study [18], so this was also adopted here. ...
... Data utilized in this study were from a previous report that developed a biomarker panel based on metabolomics and inflammatory markers for the purposes of an early diagnosis and then triage of suspected pediatric sepsis cases [18]. In brief, children 2-17 years of age presenting with a diagnosis of sepsis at a pediatric intensive care unit (PICU cohort, i.e., required PICU care) or a pediatric emergency department (ED cohort, i.e., met sepsis definitions while in the ED but did not require PICU care) provided a blood sample for this study. ...
Automated programs that carry out targeted metabolite identification and quantification using proton nuclear magnetic resonance spectra can overcome time and cost barriers that limit metabolomics use. However, their performance needs to be comparable to that of an experienced spectroscopist. A previously analyzed pediatric sepsis data set of serum samples was used to compare results generated by the automated programs rDolphin and BATMAN with the results obtained by manual profiling for 58 identified metabolites. Metabolites were selected using Student’s t-tests and evaluated with several performance metrics. The manual profiling results had the highest performance metrics values, especially for sensitivity (76.9%), area under the receiver operating characteristic curve (0.90), precision (62.5%), and testing accuracy based on a neural net (88.6%). All three approaches had high specificity values (77.7–86.7%). Manual profiling by an expert spectroscopist outperformed two open-source automated programs, indicating that further development is needed to achieve acceptable performance levels.
... It also suggests the potential to use reinforcement learning in this post-sepsis diagnosis period. Other approaches to illness trajectories have used longitudinal methods for evaluating change over time, which allows for apportioning of variance as well as phenotyping or clustering approaches (15,33). In contrast, Markov decision processes can model the sequence of interactions between clinician interventions and illness states (34). ...
Pediatric sepsis is a heterogeneous disease with varying physiological dynamics associated with recovery, disability, and mortality. Using risk scores generated from a sepsis prediction model to define illness states, we used Markov chain modeling to describe disease dynamics over time by describing how children transition among illness states. We analyzed 18,666 illness state transitions over 157 pediatric intensive care unit admissions in the 3 days following blood cultures for suspected sepsis. We used Shannon entropy to quantify the differences in transition matrices stratified by clinical characteristics. The population-based transition matrix based on the sepsis illness severity scores in the days following a sepsis diagnosis can describe a sepsis illness trajectory. Using the entropy based on Markov chain transition matrices, we found a different structure of dynamic transitions based on ventilator use but not age group. Stochastic modeling of transitions in sepsis illness severity scores can be useful in describing the variation in transitions made by patient and clinical characteristics.
... We performed a post hoc sub-group analysis of children aged 0-17 years who had been prospectively enrolled in the Alberta Sepsis Network (ASN) study and who had pathologically proven appendicitis. The ASN study was investigating the metabolic and inflammatory processes in children managed in the Emergency Department (ED) or the Pediatric Intensive Care Unit (PICU) for an infectious illness [44]. Children were eligible for enrolment into the ASN study if they met criteria for systemic inflammatory response syndrome, had a blood culture performed and antibiotics ordered. ...
... Samples were prepared for the acquisition of 1 H NMR spectra following a procedure described in earlier studies [44,45]. Briefly, serum samples were filtered using 3 kDa Na-noSep microcentrifuge filters to remove large molecules. ...
Background: While children with appendicitis often have excellent clinical outcomes, some develop life-threatening complications including sepsis and organ dysfunction requiring pediatric intensive care unit (PICU) support. Our study applied a metabolomics and inflammatory protein mediator (IPM) profiling approach to determine the bio-profiles of children who developed severe appendicitis compared with those that did not. Methods: We performed a prospective case-control study of children aged 0–17 years with a diagnosis of appendicitis. Cases had severe disease resulting in PICU admission. Primary controls had moderate appendicitis (perforation without PICU); secondary controls had mild appendicitis (non-perforated). Serum samples were analyzed using Proton Nuclear Magnetic Resonance (1H NMR) Spectroscopy and Gas Chromatography-Mass Spectrometry (GC-MS); IPM analysis was performed using plasma bead-based multiplex profiling. Comparisons were made using multivariate data statistical analysis. Results: Fifty-three children were included (15 severe, 38 non-severe). Separation between severe and moderate appendicitis demonstrated excellent sensitivity and specificity (100%, 88%; 14 compounds), separation between severe and mild appendicitis also showed excellent sensitivity and specificity (91%, 90%; 16 compounds). Conclusions: Biomarker patterns derived from metabolomics and IPM profiling are capable of distinguishing children with severe appendicitis from those with less severe disease. These findings provide an important first step towards developing non-invasive diagnostic tools for clinicians in early identification of children who are at a high risk of developing severe appendicitis.
... Metabolic signatures showing alterations in plasma levels of kynurenine, phenylalanine, lysophosphatidylcholines species and acylcarnitines have already been reported in different settings of septic shock patients [37,[55][56][57] suggesting an overall derangement of energy circuits and lipid homeostasis as indicators of disease severity, as summarized in Fig 5. An elevated concentration of phenylalanine appears to be the result of an accelerated rate of protein breakdown often caused by infections and inflammatory states [31,58]. The metabolites referred here have been altered in sepsis patients [59], and are believed to have prognostic value for sepsis, especially amino acids and derivatives, lipids and lipid-like molecules, and organic acids and derivatives [40]. ...
... Metabolic signatures showing alterations in plasma levels of kynurenine, phenylalanine, lysophosphatidylcholines species and acylcarnitines have already been reported in different settings of septic shock patients [37,[55][56][57] suggesting an overall derangement of energy circuits and lipid homeostasis as indicators of disease severity, as summarized in Fig 5. An elevated concentration of phenylalanine appears to be the result of an accelerated rate of protein breakdown often caused by infections and inflammatory states [31,58]. The metabolites referred here have been altered in sepsis patients [59], and are believed to have prognostic value for sepsis, especially amino acids and derivatives, lipids and lipid-like molecules, and organic acids and derivatives [40]. ...
Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C18:2, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986–0.995), with sensitivity of 0.978 (0.963–0.992) and specificity of 0.920 (0.890–0.949). The panel that included C10:2, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952–0.977), with sensitivity of 0.993(0.984–1.000) and specificity of 0.851 (0.815–0.887). The panel with citric acid, LysoPC a C28:1, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800–0.858), with sensitivity of 0.738 (0.695–0.781) and specificity of 0.781 (0.735–0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788–0.874), with sensitivity of 0.765 (0.697–0.832) and specificity of 0.817 (0.770–0.865).
... Similarly, in a different study it was found that a biopattern comprising of nine metabolites (lysophosphatidylcholine, phenylalanine, proline and six types of phosphatidylcholine) and seven inflammatory compounds (CRP, SAA, IL-6, ferritin, haptoglobin, HGF and TNF-related apoptosis-inducing ligand) could be used to accurately separate between children without appendicitis and appendicitis groups (AUROC = 0.96) [25]. Overall, with the advancement of technology and the increased interest in 'omics' techniques, research has demonstrated that precision medicine can improve accuracy of diagnosis in pediatric appendicitis and other inflammatory conditions [16,23]. Further studies are needed to confirm the validity of precision medicine techniques for diagnostic purposes. ...
Reliable and efficient diagnosis of pediatric appendicitis is essential for the establishment of a clinical management plan and improvement of patient outcomes. Current strategies used to diagnose a child presenting with a suspected appendicitis include laboratory studies, clinical scores and diagnostic imaging. Although these modalities work in conjunction with each other, one optimal diagnostic strategy has yet to be agreed upon. The recent introduction of precision medicine techniques such as genomics, transcriptomics, proteomics and metabolomics has increased both the diagnostic sensitivity and specificity of appendicitis. Using these novel strategies, the integration of precision medicine into clinical practice via point-of-care technologies is a plausible future. These technologies would assist in the screening, diagnosis and prognosis of pediatric appendicitis.
