Predictive model of ibuprofen treatment failure in very preterm infants with patent ductus arteriosus using machine learning techniques Articles uri icon

publication date

  • July 2025

start page

  • 944

end page

  • 950

issue

  • 45

International Standard Serial Number (ISSN)

  • 0743-8346

Electronic International Standard Serial Number (EISSN)

  • 1476-5543

abstract

  • Background
    The approach to patent ductus arteriosus (PDA) remains controversial. We aim to develop an algorithm to predict ibuprofen treatment failure (TF) using machine learning (ML) techniques.

    Methods
    Secondary analysis of a trial of very preterm infants receiving intravenous ibuprofen to treat PDA. A predictive model on TF was developed with ML. The impact of TF on outcomes was analyzed.

    Results
    One hundred forty-six infants were included. ML techniques showed that a logistic regression model predicted TF with an AUC 0.65. A multiple regression model found that bronchopulmonary dysplasia (BPD) was associated with TF, p = 0.03. Other neonatal outcomes did not differ between the study groups.

    Conclusions
    It is feasible to build a predictive model of ibuprofen TF with ML that could assist clinicians during the PDA treatment decision-making process. The identification of responders prior to intervention would mitigate adverse effects in non-responders, providing them with an alternative approach.

subjects

  • Telecommunications

keywords

  • biomarkers; diseases; medical research