Dataset Information

Potential Lipid Signatures for Diagnosis and Prognosis of Sepsis and Systemic Inflammatory Response Syndrome.

ABSTRACT: Systemic inflammatory response syndrome (SIRS) and sepsis are two conditions which are difficult to differentiate clinically and which are strongly impacted for prompt intervention. This study identified potential lipid signatures that are able to differentiate SIRS from sepsis and to predict prognosis. Forty-two patients, including 21 patients with sepsis and 21 patients with SIRS, were involved in the study. Liquid chromatography coupled to mass spectrometry and multivariate statistical methods were used to determine lipids present in patient plasma. The obtained lipid signatures revealed 355 features for the negative ion mode and 297 for the positive ion mode, which were relevant for differential diagnosis of sepsis and SIRS. These lipids were also tested as prognosis predictors. Lastly, L-octanoylcarnitine was found to be the most promising lipid signature for both the diagnosis and prognosis of critically ill patients, with accuracies of 75% for both purposes. In short, we presented the determination of lipid signatures as a potential tool for differential diagnosis of sepsis and SIRS and prognosis of these patients.

SUBMITTER: Mecatti GC

PROVIDER: S-EPMC7570015 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Potential Lipid Signatures for Diagnosis and Prognosis of Sepsis and Systemic Inflammatory Response Syndrome.

Mecatti Giovana Colozza GC Sánchez-Vinces Salvador S Fernandes Anna Maria A P AMAP Messias Marcia C F MCF de Santis Gabrielle K D GKD Porcari Andreia M AM Marson Fernando A L FAL Carvalho Patrícia de Oliveira PO

Metabolites 20200901 9

Systemic inflammatory response syndrome (SIRS) and sepsis are two conditions which are difficult to differentiate clinically and which are strongly impacted for prompt intervention. This study identified potential lipid signatures that are able to differentiate SIRS from sepsis and to predict prognosis. Forty-two patients, including 21 patients with sepsis and 21 patients with SIRS, were involved in the study. Liquid chromatography coupled to mass spectrometry and multivariate statistical method ...[more]

PMID: 32882869

Similar Datasets

Project description:ObjectivesTo advance our biological understanding of pediatric septic shock, we measured the genome-level expression profiles of critically ill children representing the systemic inflammatory response syndrome (SIRS), sepsis, and septic shock spectrum.DesignProspective observational study involving microarray-based bioinformatics.SettingMultiple pediatric intensive care units in the United States.PatientsChildren <or=10 years of age: 18 normal controls, 22 meeting criteria for SIRS, 32 meeting criteria for sepsis, and 67 meeting criteria for septic shock on day 1. The available day 3 samples included 20 patients still meeting sepsis criteria, 39 patients still meeting septic shock criteria, and 24 patients meeting the exclusive day 3 category, SIRS resolved.InterventionsNone other than standard care.Measurements and main resultsLongitudinal analyses were focused on gene expression relative to control samples and patients having paired day 1 and day 3 samples. The longitudinal analysis focused on up-regulated genes revealed common patterns of up-regulated gene expression, primarily corresponding to inflammation and innate immunity, across all patient groups on day 1. These patterns of up-regulated gene expression persisted on day 3 in patients with septic shock, but not to the same degree in the other patient classes. The longitudinal analysis focused on down-regulated genes demonstrated gene repression corresponding to adaptive immunity-specific signaling pathways and was most prominent in patients with septic shock on days 1 and 3. Gene network analyses based on direct comparisons across the SIRS, sepsis, and septic shock spectrum, and all available patients in the database, demonstrated unique repression of gene networks in patients with septic shock corresponding to major histocompatibility complex antigen presentation. Finally, analyses focused on repression of genes corresponding to zinc-related biology demonstrated that this pattern of gene repression is unique to patients with septic shock.ConclusionsAlthough some common patterns of gene expression exist across the pediatric SIRS, sepsis, and septic shock spectrum, septic shock is particularly characterized by repression of genes corresponding to adaptive immunity and zinc-related biology.

Project description:BackgroundSepsis is the leading cause of death in intensive care units (ICUs), and its timely detection and treatment improve clinical outcome and survival. Systemic inflammatory response syndrome (SIRS) refers to the concurrent fulfillment of at least two out of the following four clinical criteria: tachycardia, tachypnea, abnormal body temperature, and abnormal leukocyte count. While SIRS was controversially abandoned from the current sepsis definition, a dynamic SIRS representation still has potential for sepsis prediction and diagnosis.ObjectiveWe retrospectively elucidate the individual contributions of the SIRS criteria in a polytrauma cohort from the post-surgical ICU of University Medical Center Mannheim (Germany).MethodsWe used a dynamic and prospective SIRS algorithm tailored to the ICU setting by accounting for catecholamine therapy and mechanical ventilation. Two clinically relevant tasks are considered: (i) sepsis prediction using the first 24 h after admission to our ICU, and (ii) sepsis diagnosis using the last 24 h before sepsis onset and a time point of comparable ICU treatment duration for controls, respectively. We determine the importance of individual SIRS criteria by systematically varying criteria weights when summarizing the SIRS algorithm output with SIRS descriptors and assessing the classification performance of the resulting logistic regression models using a specifically developed ranking score.ResultsOur models perform better for the diagnosis than the prediction task (maximum AUROC 0.816 vs. 0.693). Risk models containing only the SIRS level average mostly show reasonable performance across criteria weights, with prediction and diagnosis AUROCs ranging from 0.455 (weight on leukocyte criterion only) to 0.693 and 0.619 to 0.800, respectively. For sepsis prediction, temperature and tachypnea are the most important SIRS criteria, whereas the leukocytes criterion is least important and potentially even counterproductive. For sepsis diagnosis, all SIRS criteria are relevant, with the temperature criterion being most influential.ConclusionSIRS is relevant for sepsis prediction and diagnosis in polytrauma, and no criterion should a priori be omitted. Hence, the original expert-defined SIRS criteria are valid, capturing important sepsis risk determinants. Our prospective SIRS algorithm provides dynamic determination of SIRS criteria and descriptors, allowing their integration in sepsis risk models also in other settings.

Dataset Information

Potential Lipid Signatures for Diagnosis and Prognosis of Sepsis and Systemic Inflammatory Response Syndrome.

Publications

Potential Lipid Signatures for Diagnosis and Prognosis of Sepsis and Systemic Inflammatory Response Syndrome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets