Metabolomic profiling of amines in sepsis predicts changes in NOS canonical pathways

Rationale Nitric oxide synthase (NOS) is a biomarker/target in sepsis. NOS activity is driven by amino acids, which cycle to regulate the substrate L-arginine in parallel with cycles which regulate the endogenous inhibitors ADMA and L-NMMA. The relationship between amines and the consequence of plasma changes on iNOS activity in early sepsis is not known. Objective Our objective was to apply a metabolomics approach to determine the influence of sepsis on a full array of amines and what consequence these changes may have on predicted iNOS activity. Methods and measurements 34 amino acids were measured using ultra purification mass spectrometry in the plasma of septic patients (n = 38) taken at the time of diagnosis and 24–72 hours post diagnosis and of healthy volunteers (n = 21). L-arginine and methylarginines were measured using liquid-chromatography mass spectrometry and ELISA. A top down approach was also taken to examine the most changed metabolic pathways by Ingenuity Pathway Analysis. The iNOS supporting capacity of plasma was determined using a mouse macrophage cell-based bioassay. Main results Of all the amines measured 22, including L-arginine and ADMA, displayed significant differences in samples from patients with sepsis. The functional consequence of increased ADMA and decreased L-arginine in context of all cumulative metabolic changes in plasma resulted in reduced iNOS supporting activity associated with sepsis. Conclusions In early sepsis profound changes in amine levels were defined by dominant changes in the iNOS canonical pathway resulting in functionally meaningful changes in the ability of plasma to regulate iNOS activity ex vivo

Critical Care Health Informatics Collaborative (CCHIC): Data, tools and methods for reproducible research: A multi-centre UK intensive care database.

OBJECTIVE: To build and curate a linkable multi-centre database of high resolution longitudinal electronic health records (EHR) from adult Intensive Care Units (ICU). To develop a set of open-source tools to make these data 'research ready' while protecting patient's privacy with a particular focus on anonymisation. MATERIALS AND METHODS: We developed a scalable EHR processing pipeline for extracting, linking, normalising and curating and anonymising EHR data. Patient and public involvement was sought from the outset, and approval to hold these data was granted by the NHS Health Research Authority's Confidentiality Advisory Group (CAG). The data are held in a certified Data Safe Haven. We followed sustainable software development principles throughout, and defined and populated a common data model that links to other clinical areas. RESULTS: Longitudinal EHR data were loaded into the CCHIC database from eleven adult ICUs at 5 UK teaching hospitals. From January 2014 to January 2017, this amounted to 21,930 and admissions (18,074 unique patients). Typical admissions have 70 data-items pertaining to admission and discharge, and a median of 1030 (IQR 481-2335) time-varying measures. Training datasets were made available through virtual machine images emulating the data processing environment. An open source R package, cleanEHR, was developed and released that transforms the data into a square table readily analysable by most statistical packages. A simple language agnostic configuration file will allow the user to select and clean variables, and impute missing data. An audit trail makes clear the provenance of the data at all times. DISCUSSION: Making health care data available for research is problematic. CCHIC is a unique multi-centre longitudinal and linkable resource that prioritises patient privacy through the highest standards of data security, but also provides tools to clean, organise, and anonymise the data. We believe the development of such tools are essential if we are to meet the twin requirements of respecting patient privacy and working for patient benefit. CONCLUSION: The CCHIC database is now in use by health care researchers from academia and industry. The 'research ready' suite of data preparation tools have facilitated access, and linkage to national databases of secondary care is underway.NIH

Increased plasma thioredoxin levels in patients with sepsis: positive association with macrophage migration inhibitory factor.

PURPOSE: To establish the relationship between plasma levels of thioredoxin (Trx) and macrophage migration inhibitory factor (MIF) in systemic inflammatory stress syndrome (SIRS)/sepsis. METHODS: Enzyme-linked immunosorbent assay measurements of Trx, MIF, IL-6, -8, and -10 and enzyme-linked fluorescent assay determination of procalcitonin (PCT) in plasma from patients with SIRS/sepsis, neutropenic sepsis, healthy volunteers and pre-oesophagectomy patients. RESULTS: Thioredoxin was significantly higher in SIRS/sepsis patients [101.3 ng ml(−1), interquartile range (IQR) 68.7–155.6, n = 32] compared with that in healthy controls (49.5 ng ml(−1), IQR 31.4–71.1, P < 0.001, n = 17) or pre-oesophagectomy patients (40.5 ng ml(−1), IQR 36.9–63.2, P < 0.01, n = 7), but was not raised in neutropenics (n = 5). MIF levels were also significantly higher in SIRS/sepsis patients (12.1 ng ml(−1), IQR 9.5–15.5, n = 35), but not in the neutropenic group, when compared with healthy controls (9.3 ng ml(−1), IQR 7.3–10.7, P < 0.01, n = 20). Trx levels correlated, positively, with MIF levels and APACHE II scores. Plasma levels of IL-6, -8 and -10 and PCT increased significantly in patients with SIRS/sepsis (P < 0.001) and with neutropenic sepsis, but did not correlate with Trx or MIF levels. CONCLUSION: Plasma levels of Trx, MIF, IL-6, -8, -10 and PCT were raised in patients with SIRS/sepsis. Comparisons between mediators suggest a unique correlation of Trx with MIF. Moreover, Trx and MIF differed from cytokines and PCT in that levels were significantly lower in patients with neutropenia compared with the main SIRS/sepsis group. By contrast, IL-8 and PCT levels were significantly greater in the neutropenic patient group. The link between MIF and Trx highlighted in this study has implications for future investigations into the pathogenesis of SIRS/sepsis

Extracellular myeloperoxidase and markers of inflammation in the sepsis syndromes

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Optimal intensive care outcome prediction over time using machine learning.

BACKGROUND:Prognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to data from admission or the first 24 hours. Most ICU admissions last longer than this, decisions are made throughout an admission, and some admissions are explicitly intended as time-limited prognostic trials. Despite this, temporal changes in prognostic ability during ICU admission has received little attention to date. Current predictive models, in the form of prognostic clinical tools, are typically derived from linear models and do not explicitly handle incremental information from trends. Machine learning (ML) allows predictive models to be developed which use non-linear predictors and complex interactions between variables, thus allowing incorporation of trends in measured variables over time; this has made it possible to investigate prognosis throughout an admission. METHODS AND FINDINGS:This study uses ML to assess the predictability of ICU mortality as a function of time. Logistic regression against physiological data alone outperformed APACHE-II and demonstrated several important interactions including between lactate & noradrenaline dose, between lactate & MAP, and between age & MAP consistent with the current sepsis definitions. ML models consistently outperformed logistic regression with Deep Learning giving the best results. Predictive power was maximal on the second day and was further improved by incorporating trend data. Using a limited range of physiological and demographic variables, the best machine learning model on the first day showed an area under the receiver-operator characteristic curve (AUC) of 0.883 (σ = 0.008), compared to 0.846 (σ = 0.010) for a logistic regression from the same predictors and 0.836 (σ = 0.007) for a logistic regression based on the APACHE-II score. Adding information gathered on the second day of admission improved the maximum AUC to 0.895 (σ = 0.008). Beyond the second day, predictive ability declined. CONCLUSION:This has implications for decision making in intensive care and provides a justification for time-limited trials of ICU therapy; the assessment of prognosis over more than one day may be a valuable strategy as new information on the second day helps to differentiate outcomes. New ML models based on trend data beyond the first day could greatly improve upon current risk stratification tools

Optimal intensive care outcome prediction over time using machine learning.

BACKGROUND: Prognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to data from admission or the first 24 hours. Most ICU admissions last longer than this, decisions are made throughout an admission, and some admissions are explicitly intended as time-limited prognostic trials. Despite this, temporal changes in prognostic ability during ICU admission has received little attention to date. Current predictive models, in the form of prognostic clinical tools, are typically derived from linear models and do not explicitly handle incremental information from trends. Machine learning (ML) allows predictive models to be developed which use non-linear predictors and complex interactions between variables, thus allowing incorporation of trends in measured variables over time; this has made it possible to investigate prognosis throughout an admission. METHODS AND FINDINGS: This study uses ML to assess the predictability of ICU mortality as a function of time. Logistic regression against physiological data alone outperformed APACHE-II and demonstrated several important interactions including between lactate & noradrenaline dose, between lactate & MAP, and between age & MAP consistent with the current sepsis definitions. ML models consistently outperformed logistic regression with Deep Learning giving the best results. Predictive power was maximal on the second day and was further improved by incorporating trend data. Using a limited range of physiological and demographic variables, the best machine learning model on the first day showed an area under the receiver-operator characteristic curve (AUC) of 0.883 (σ = 0.008), compared to 0.846 (σ = 0.010) for a logistic regression from the same predictors and 0.836 (σ = 0.007) for a logistic regression based on the APACHE-II score. Adding information gathered on the second day of admission improved the maximum AUC to 0.895 (σ = 0.008). Beyond the second day, predictive ability declined. CONCLUSION: This has implications for decision making in intensive care and provides a justification for time-limited trials of ICU therapy; the assessment of prognosis over more than one day may be a valuable strategy as new information on the second day helps to differentiate outcomes. New ML models based on trend data beyond the first day could greatly improve upon current risk stratification tools.NIH