Evidence map›Paper›PMID 40640768›Full record

ArticleBMC pediatrics2025

Fulminant necrotizing enterocolitis: clinical features and a predictive model.

Xiaoyu Chen, Yuqiao Li, Yuhan Liu, Tianjing Liu, Yongyan Shi

Abstract readValidation Study
In one paragraph

Article in BMC pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xiaoyu ChenDepartment of Hematology/Oncology, Children's Hospital of Soochow University, Suzhou, China.
Yuqiao LiDepartment of Pediatrics, Shengjing Hospital of China Medical University, NO. 36 Sanhao Street, Shenyang, 110004, China.
Yuhan LiuDepartment of Pediatrics, Shengjing Hospital of China Medical University, NO. 36 Sanhao Street, Shenyang, 110004, China.
Tianjing LiuDepartment of Pediatrics, Shengjing Hospital of China Medical University, NO. 36 Sanhao Street, Shenyang, 110004, China. tjliu@cmu.edu.cn.
Yongyan ShiDepartment of Pediatrics, Shengjing Hospital of China Medical University, NO. 36 Sanhao Street, Shenyang, 110004, China. yyshi@cmu.edu.cn.

Funding

National Natural Science Foundation of China 82171709
6 · The paper itself

Abstract

backgroundTo develop and validate a nomogram model for predicting the risk of fulminant necrotizing enterocolitis (fNEC) in infants with NEC and to summarize the clinical features of fNEC.

methodsNeonates admitted to Shengjing Hospital from 1st January 2013 to 31st December 2022 with the diagnosis of NEC were randomly divided into a training set or a validation set. Independent risk factors identified by univariate and multivariate logistic regression analyses were used to conduct a nomogram model for fNEC. The model was evaluated by the area under the curve (AUC), calibration curves and decision curve analysis (DCA) in both sets.

resultsA total of 315 neonates were included, comprising 70 cases of fNEC and 245 cases of non-fulminant NEC. Neonates with fNEC exhibited more severe symptoms, including acidosis, thrombocytopenia, absent bowel sounds, increased need for surgical intervention and significantly higher mortality. The nomogram was developed using four predictors: bloody stool, absence of bowel sounds, elevated lactate levels and pH values. The AUC of the training and validation sets were 0.939 and 0.975, respectively. Calibration curves and the DCA showed satisfactory performance.

conclusionsNeonates with fNEC exhibited more severe symptoms. Our nomogram model shows promise in assisting clinicians in predicting fNEC.

Indexed as

Enterocolitis, NecrotizingNomogramsFemaleHumansInfant, NewbornMaleRetrospective StudiesRisk FactorsFulminant necrotizing EnterocolitisNomogramPredictive modelPreterm infants

Identifiers

PMID40640768
PMCPMC12243289

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.