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Gut doi:10.1136/gutjnl-2013-305130
  • Intestinal inflammation
  • Original article

A novel urine peptide biomarker-based algorithm for the prognosis of necrotising enterocolitis in human infants

  1. R Lawrence Moss7,8
  1. 1Division of Pediatric Surgery, Lucile Packard Children's Hospital, Stanford, USA
  2. 2Department of Surgery, Stanford University School of Medicine, Stanford, USA
  3. 3Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, USA
  4. 4Department of Surgery, Texas Children's Hospital, Baylor College of Medicine, Houston, USA
  5. 5Department of Pediatrics, Yale University School of Medicine, New Haven, USA
  6. 6Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, USA
  7. 7Division of Pediatric Surgery, Nationwide Children's Hospital, Columbus, USA
  8. 8Department of Surgery, Ohio State College of Medicine, Columbus, USA
  1. Correspondence to Dr Karl G Sylvester, Stanford Department of Surgery, Stanford University School of Medicine, Lucile Packard Children's Hospital, 777 Welch Rd Ste J, Palo Alto, CA 94305, USA; kgsylvester{at}gmail.com
  • Received 22 April 2013
  • Revised 22 August 2013
  • Accepted 23 August 2013
  • Published Online First 18 September 2013

Abstract

Objective Necrotising enterocolitis (NEC) is a major source of neonatal morbidity and mortality. The management of infants with NEC is currently complicated by our inability to accurately identify those at risk for progression of disease prior to the development of irreversible intestinal necrosis. We hypothesised that integrated analysis of clinical parameters in combination with urine peptide biomarkers would lead to improved prognostic accuracy in the NEC population.

Design Infants under suspicion of having NEC (n=550) were prospectively enrolled from a consortium consisting of eight university-based paediatric teaching hospitals. Twenty-seven clinical parameters were used to construct a multivariate predictor of NEC progression. Liquid chromatography/mass spectrometry was used to profile the urine peptidomes from a subset of this population (n=65) to discover novel biomarkers of NEC progression. An ensemble model for the prediction of disease progression was then created using clinical and biomarker data.

Results The use of clinical parameters alone resulted in a receiver-operator characteristic curve with an area under the curve of 0.817 and left 40.1% of all patients in an ‘indeterminate’ risk group. Three validated urine peptide biomarkers (fibrinogen peptides: FGA1826, FGA1883 and FGA2659) produced a receiver-operator characteristic area under the curve of 0.856. The integration of clinical parameters with urine biomarkers in an ensemble model resulted in the correct prediction of NEC outcomes in all cases tested.

Conclusions Ensemble modelling combining clinical parameters with biomarker analysis dramatically improves our ability to identify the population at risk for developing progressive NEC.