Format

Send to

Choose Destination
PLoS One. 2014 Feb 28;9(2):e89860. doi: 10.1371/journal.pone.0089860. eCollection 2014.

A data-driven algorithm integrating clinical and laboratory features for the diagnosis and prognosis of necrotizing enterocolitis.

Author information

1
State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, Zhejiang, China ; Department of Surgery, Stanford University, Stanford, California, United States of America.
2
Department of Surgery, Stanford University, Stanford, California, United States of America.
3
School of Health Management, Hangzhou Normal University, Hangzhou, Zhejiang, China ; Department of Surgery, Stanford University, Stanford, California, United States of America.
4
Department of Surgery, Stanford University, Stanford, California, United States of America ; College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China.
5
State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, Zhejiang, China.
6
Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
7
Department of Surgery, Texas Children's Hospital, Baylor College of Medicine, Houston, Texas, United States of America.
8
Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut, United States of America.
9
Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.
10
Division of Pediatric Surgery, Nationwide Children's Hospital, Columbus, Ohio, United States of America ; Department of Surgery, Ohio State College of Medicine, Columbus, Ohio, United States of America.

Abstract

BACKGROUND:

Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting.

STUDY DESIGN:

A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data.

RESULTS:

Machine learning using clinical and laboratory results at the time of clinical presentation led to two nec models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner.

ALGORITHM AVAILABILITY:

http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request.

PMID:
24587080
PMCID:
PMC3938509
DOI:
10.1371/journal.pone.0089860
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center