Literature DB >> 25285400

A clinical scoring system to predict the development of bronchopulmonary dysplasia.

Tugba Gursoy1, Mutlu Hayran2, Hatice Derin3, Fahri Ovali3.   

Abstract

OBJECTIVE: This study aims to develop a scoring system for the prediction of bronchopulmonary dysplasia (BPD).
METHODS: Medical records of 652 infants whose gestational age and birth weight were below 32 weeks and 1,500 g, respectively, and who survived beyond 28th postnatal day were reviewed retrospectively. Logistic regression methods were used to determine the clinical and demographic risk factors within the first 72 hours of life associated with BPD, as well as the weights of these factors on developing BPD. Predictive accuracy of the scoring system was tested prospectively at the same unit.
RESULTS: Birth weight, gestational age, gender, presence of respiratory distress syndrome, patent ductus arteriosus, intraventricular hemorrhage, hypotension were the most important risk factors for BPD. Therefore, a scoring system (BPD-TM score) ranging from 0 to 13 and grouped in four tiers (0-3: low, 4-6: low intermediate, 7-9: high intermediate, and 10-13: high risk) was developed based on these factors. Below the score of 4, 4.1% of infants (18/436), above the score of 9, 100% (29/29) of the infants developed BPD. The score was validated successfully in 172 infants.
CONCLUSION: With this easy to use scoring system, one can predict the neonate at risk for BPD at 72 hours of life and direct preventive measures toward these infants. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

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Mesh:

Year:  2014        PMID: 25285400     DOI: 10.1055/s-0034-1393935

Source DB:  PubMed          Journal:  Am J Perinatol        ISSN: 0735-1631            Impact factor:   1.862


  9 in total

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Authors:  Yesim Coskun; Semra Isik; Tevfik Bayram; Kamran Urgun; Sibel Sakarya; Ipek Akman
Journal:  Childs Nerv Syst       Date:  2017-10-12       Impact factor: 1.475

2.  A risk factor analysis on disease severity in 47 premature infants with bronchopulmonary dysplasia.

Authors:  Yan Li; Yazhou Cui; Chao Wang; Xiao Liu; Jinxiang Han
Journal:  Intractable Rare Dis Res       Date:  2015-05

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4.  Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review.

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Journal:  Front Pediatr       Date:  2022-05-12       Impact factor: 3.569

5.  The influence of gender on respiratory outcomes in children with bronchopulmonary dysplasia during the first 3 years of life.

Authors:  Joseph M Collaco; Angela D Aherrera; Sharon A McGrath-Morrow
Journal:  Pediatr Pulmonol       Date:  2016-06-30

6.  Exploring clinical, echocardiographic and molecular biomarkers to predict bronchopulmonary dysplasia.

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Journal:  PLoS One       Date:  2019-03-06       Impact factor: 3.240

7.  Bronchopulmonary dysplasia predicted at birth by artificial intelligence.

Authors:  Henrik Verder; Christian Heiring; Rangasamy Ramanathan; Nikolaos Scoutaris; Povl Verder; Torben E Jessen; Agnar Höskuldsson; Lars Bender; Marianne Dahl; Christian Eschen; Jesper Fenger-Grøn; Jes Reinholdt; Heidi Smedegaard; Peter Schousboe
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8.  Development and verification of a risk prediction model for bronchopulmonary dysplasia in very low birth weight infants.

Authors:  Huiwen Cai; Ling Jiang; Yongshu Liu; Ting Shen; Zuming Yang; Sannan Wang; Yuelan Ma
Journal:  Transl Pediatr       Date:  2021-10

9.  Bronchopulmonary Dysplasia Predicted by Developing a Machine Learning Model of Genetic and Clinical Information.

Authors:  Dan Dai; Huiyao Chen; Xinran Dong; Jinglong Chen; Mei Mei; Yulan Lu; Lin Yang; Bingbing Wu; Yun Cao; Jin Wang; Wenhao Zhou; Liling Qian
Journal:  Front Genet       Date:  2021-07-02       Impact factor: 4.599

  9 in total

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