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OC-030 Investigation of faecal volatile organic compounds as biomarkers for the diagnosis of necrotising entercolitis
  1. A Mayor1,
  2. N Ellaby2,
  3. S Reade1,
  4. R Aggio1,
  5. R Greenwood3,
  6. R Jackson4,
  7. E Simcox4,
  8. CS Probert1,
  9. AK Ewer4,5
  1. 1Department of Physiology
  2. 2Institute of Integrative Biology, University of Liverpool, Liverpool
  3. 3University Hospitals Bristol NHS Foundation Trust, Bristol
  4. 4Birmingham Women’s Hospital
  5. 5School of Clinical and Experimental Medicine, University of Birmingham, Birmingham, UK

Abstract

Introduction Necrotising enterocolitis (NEC) is the most common serious gastrointestinal complication of preterm birth. Clinical signs and symptoms are often non-specific and may only become apparent once disease is established. A biomarker to indicate early disease would allow earlier treatment and may improve outcome. We have reported a pilot study showing changes in faecal volatile organic compounds (VOCs) profile in the prodromal period of NEC. Here, we report a much larger series with more comprehensive clinical and laboratory data.

Method Daily faecal samples were collected prospectively from 1375 apparently healthy preterm babies, born between 23 and 34 weeks gestation, in 8 NICUs over a 2-year period. Over 13,000 samples were catalogued. Samples from 70 healthy preterm babies and 34 preterm babies who developed NEC (Bell’s stage II and III) were analysed. In short, 50–100 mg of faeces underwent headspace gas extraction using solid phase micro-extraction followed by gas chromatography–mass spectrometry using our optimised method. Each NEC patient was matched with 1 to 3 healthy control patients. Samples from NEC patients between 1 to 6 days prior to diagnosis and matched samples from controls were selected. Compounds were identified using an in-house developed pipeline involving the AMDIS software, the NIST database and the R packages Metab and XCMS. Univariate and multivariate analysis were performed on the clinical and VOC data. The features best describing the differences between NEC and control samples were used to build a classifier for the diagnosis of NEC.

Results Six VOCs and birthweight were significant in multivariable analysis. Birthweight was the strongest predictor. Of the VOCs, 3 were positively associated with NEC and 3 negatively associated.

A logistic regression model was developed to diagnose NEC overall, then for each of the 6 days that preceded the clinical diagnosis. Overall the model had an area under the ROC (AUROC) curve of 0.83. When estimated for each day, the AUROC ranged from 0.78 to 0.9.

Conclusion To the best of our knowledge, this is the first large scale metabolomics study of faecal VOCs aiming at classifying NEC patients. The data indicate the VOCs may be used to predict NEC up to 6 days before the condition is diagnosed using current methods. This may have an important impact on reducing the mortality of this devastating condition. Further investigation of VOCs may also elucidate the pathogenesis of NEC.

Disclosure of interest None Declared.

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