PT  - JOURNAL ARTICLE
AU  - Karl G Sylvester
AU  - Xuefeng B Ling
AU  - G Y Liu
AU  - Zachary J Kastenberg
AU  - Jun Ji
AU  - Zhongkai Hu
AU  - Sihua Peng
AU  - Ken Lau
AU  - Fizan Abdullah
AU  - Mary L Brandt
AU  - Richard A Ehrenkranz
AU  - Mary Catherine Harris
AU  - Timothy C Lee
AU  - Joyce Simpson
AU  - Corinna Bowers
AU  - R Lawrence Moss
TI  - A novel urine peptide biomarker-based algorithm for the prognosis of necrotising enterocolitis in human infants
AID  - 10.1136/gutjnl-2013-305130
DP  - 2014 Aug 01
TA  - Gut
PG  - 1284--1292
VI  - 63
IP  - 8
4099  - http://gut.bmj.com/content/63/8/1284.short
4100  - http://gut.bmj.com/content/63/8/1284.full
SO  - Gut2014 Aug 01; 63
AB  - 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.