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Post a Comment Early prediction of bronchopulmonary dysplasia in extremely premature infants: a cohort study
- Authors: Permyakova A.V.1, Bakhmetyeva O.B.2, Mamunts M.A.1, Kuchumov A.G.3, Koshechkin K.A.4
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Affiliations:
- E.A. Vagner Perm State Medical University
- Perm Regional Perinatal Center
- Perm National Research Polytechnic University
- I.M. Sechenov First Moscow State Medical University (Sechenov University)
- Issue: Vol 41, No 3 (2024)
- Pages: 120-128
- Section: Methods of diagnosis and technologies
- URL: https://journals.rcsi.science/PMJ/article/view/260591
- DOI: https://doi.org/10.17816/pmj413120-128
- ID: 260591
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Abstract
Objective. To develop the model for early prediction of clinically significant bronchopulmonary dysplasia in extremely premature infants.
Materials and methods. 226 premature infants with gestational age less than 31 weeks, birth weight from 490 to 999 g, age from 0 to 7 days, and respiratory failure requiring ventilatory support (ventilator support) were included into a retrospective study conducted in the Perm Regional Perinatal Center. Machine learning algorithms such as logistic regression, support vector machine, random forest method, and gradient boosting method were used for the prognostic model building. Five variables were used: birth weight, Apgar score in the 5th minute of life, Silverman score, number of days of invasive ventilatory support, median oxygen fraction in the inhaled air measured daily during the first seven days of life.
Results. In the 36th week of postconceptional age 148 out of 182 infants (81.3 %) in the study cohort developed bronchopulmonary dysplasia (BPD), among them 15.4 % had a mild form, 29.7 % a moderate one, and in 36.3 % of patient it was severe. Among the four studied prediction algorithms, logistic regression model was chosen as the final model with metrics: AUC = 0.840, accuracy 0.818, sensitivity 0.972, specificity 0.666. The practical application of the modeling results was implemented in the form of a probability calculator.
Conclusions. In the early neonatal period of extremely premature infants, a combination of clinical predictors such as birth weight, Apgar score in the 5th minute of life, Silverman score, number of days of invasive ventilatory support, median oxygen fraction in the inhaled air measured during the first seven days of life can be used to predict the development of bronchopulmonary dysplasia. The logistic regression model shows high sensitivity that minimizes the probability of an error of second kind. Thus, its application is useful in the early prediction of bronchopulmonary dysplasia in premature infants.
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##article.viewOnOriginalSite##About the authors
A. V. Permyakova
E.A. Vagner Perm State Medical University
Author for correspondence.
Email: derucheva@mail.ru
ORCID iD: 0000-0001-5189-0347
DSc (Medicine), Head of the Department of Childhood Infectious Diseases
Russian Federation, PermO. B. Bakhmetyeva
Perm Regional Perinatal Center
Email: derucheva@mail.ru
ORCID iD: 0000-0003-2343-3602
Assistant of the Department of Anesthesiology, Resuscitation and Emergency Medical Aid, Resuscitation Anaesthetist
Russian Federation, PermM. A. Mamunts
E.A. Vagner Perm State Medical University
Email: derucheva@mail.ru
ORCID iD: 0000-0001-5326-6740
PhD (Medicine), Associate Professor of the Department of Pediatrics with Polyclinic Pediatrics Course
Russian Federation, PermA. G. Kuchumov
Perm National Research Polytechnic University
Email: derucheva@mail.ru
ORCID iD: 0000-0002-0466-175X
DSc (Physics and Mathematics), Associate Professor, Professor of the Department of Computational Mathematics, Mechanics and Biomechanics
Russian Federation, PermK. A. Koshechkin
I.M. Sechenov First Moscow State Medical University (Sechenov University)
Email: derucheva@mail.ru
ORCID iD: 0000-0001-7309-2215
DSc (Pharmaceutics), Associate Professor, Professor of the Department of Information and Internet Technologies
Russian Federation, MoscowReferences
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