A data-driven algorithm integrating clinical and laboratory features for the diagnosis and prognosis of necrotizing enterocolitis.
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 va...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3938509?pdf=render |
| _version_ | 1819243056321265664 |
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| author | Jun Ji Xuefeng B Ling Yingzhen Zhao Zhongkai Hu Xiaolin Zheng Zhening Xu Qiaojun Wen Zachary J Kastenberg Ping Li Fizan Abdullah Mary L Brandt Richard A Ehrenkranz Mary Catherine Harris Timothy C Lee B Joyce Simpson Corinna Bowers R Lawrence Moss Karl G Sylvester |
| author_facet | Jun Ji Xuefeng B Ling Yingzhen Zhao Zhongkai Hu Xiaolin Zheng Zhening Xu Qiaojun Wen Zachary J Kastenberg Ping Li Fizan Abdullah Mary L Brandt Richard A Ehrenkranz Mary Catherine Harris Timothy C Lee B Joyce Simpson Corinna Bowers R Lawrence Moss Karl G Sylvester |
| author_sort | Jun Ji |
| collection | DOAJ |
| description | 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.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.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.http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request. |
| first_indexed | 2024-12-23T14:49:38Z |
| format | Article |
| id | doaj.art-6a92c635a8904533a5acb1dba95cb7ef |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-23T14:49:38Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-6a92c635a8904533a5acb1dba95cb7ef2022-12-21T17:42:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0192e8986010.1371/journal.pone.0089860A data-driven algorithm integrating clinical and laboratory features for the diagnosis and prognosis of necrotizing enterocolitis.Jun JiXuefeng B LingYingzhen ZhaoZhongkai HuXiaolin ZhengZhening XuQiaojun WenZachary J KastenbergPing LiFizan AbdullahMary L BrandtRichard A EhrenkranzMary Catherine HarrisTimothy C LeeB Joyce SimpsonCorinna BowersR Lawrence MossKarl G SylvesterNecrotizing 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.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.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.http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request.http://europepmc.org/articles/PMC3938509?pdf=render |
| spellingShingle | Jun Ji Xuefeng B Ling Yingzhen Zhao Zhongkai Hu Xiaolin Zheng Zhening Xu Qiaojun Wen Zachary J Kastenberg Ping Li Fizan Abdullah Mary L Brandt Richard A Ehrenkranz Mary Catherine Harris Timothy C Lee B Joyce Simpson Corinna Bowers R Lawrence Moss Karl G Sylvester A data-driven algorithm integrating clinical and laboratory features for the diagnosis and prognosis of necrotizing enterocolitis. PLoS ONE |
| title | A data-driven algorithm integrating clinical and laboratory features for the diagnosis and prognosis of necrotizing enterocolitis. |
| title_full | A data-driven algorithm integrating clinical and laboratory features for the diagnosis and prognosis of necrotizing enterocolitis. |
| title_fullStr | A data-driven algorithm integrating clinical and laboratory features for the diagnosis and prognosis of necrotizing enterocolitis. |
| title_full_unstemmed | A data-driven algorithm integrating clinical and laboratory features for the diagnosis and prognosis of necrotizing enterocolitis. |
| title_short | A data-driven algorithm integrating clinical and laboratory features for the diagnosis and prognosis of necrotizing enterocolitis. |
| title_sort | data driven algorithm integrating clinical and laboratory features for the diagnosis and prognosis of necrotizing enterocolitis |
| url | http://europepmc.org/articles/PMC3938509?pdf=render |
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