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

Journal of Perinatology volume 45, pages 944–950 (2025)Cite this article

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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.

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Fig. 1: ROC (Receiver Operating Characteristic) curves of Machine Learning Models.
Fig. 2: Relevance of each variable in the predictive model (LR model L1 C = 5000).

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Data availability

Data for this study are not publicly available as they contain information that could compromise the privacy of the research participants; however, they may be requested upon signing a data access agreement.

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Acknowledgements

The corresponding author acknowledges the scientific support of the SAMID Network (Maternal and Child Health and Development) (RD08/0072/0018 and RD12/0026/0004) and “Primary Care Interventions to Prevent Maternal and Child Chronic Diseases of Perinatal and Developmental Origin (RICORS, RD21/0012/0014)”, Instituto de Salud Carlos III, Madrid.

Funding

The original multicenter clinical trial received a grant from the Instituto Salud Carlos III (PI16/0644) and the Fundación Mutua Madrileña (AP163272016). This study was supported by the Spanish Health Ministry (grant PI22/00567).

Author information

Authors and Affiliations

  1. Department of Neonatology, La Paz University Hospital, Madrid, Spain

    María Carmen Bravo & Adelina Pellicer

  2. Department of Signal Processing and Communications, Carlos III University, Madrid, Spain

    Emilio Parrado-Hernández

  3. Department of Pediatrics, University of Iowa, Iowa City, IA, USA

    Patrick J. McNamara

  4. Department of Internal Medicine, University of Iowa, Iowa City, IA, USA

    Patrick J. McNamara

Authors
  1. María Carmen Bravo
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  2. Emilio Parrado-Hernández
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  3. Patrick J. McNamara
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  4. Adelina Pellicer
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Contributions

Maria Carmen Bravo conceptualized and designed the study, drafted the initial manuscript and approved the final manuscript as submitted. Emilio Parrado-Hernández performed the machine learning analysis, the statistical analysis and approved the final manuscript as submitted. Patrick J McNamara reviewed the draft of the manuscript and contributed to writing the final version. Adelina Pellicer conceptualized and designed the study, drafted the initial manuscript and approved the final manuscript as submitted. We thank the families, nurses, doctors, and research assistants who took part in the trial and especially the local investigators Rebeca Sánchez, Ana Isabel Blanco, Laura Sánchez, Marta Ybarra, Paloma López, Maria Teresa Moral-Pumarega, Manuela López-Azorín, Rocío Mosqueda-Peña, Izaskun Dorronsoro, Fernando Cabañas, Noelia Ureta, María Soriano, Leticia Albert de la Torre, Belén Toral, Esther Cabañes, Lidia García, and Carmen Fé Peña.

Corresponding author

Correspondence to María Carmen Bravo.

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Competing interests

The authors declare no competing interests.

Informed consent

A written informed consent was obtained from the parents or guardians of the children who served as subjects of the investigation before enrollment in the randomized clinical trial. This project received ethical approval from the La Paz University Hospital Ethics Committee. Because this is a secondary analysis of the previously recruited cohort, additional informed consent was not required.

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Bravo, M.C., Parrado-Hernández, E., McNamara, P.J. et al. Predictive model of ibuprofen treatment failure in very preterm infants with patent ductus arteriosus using machine learning techniques. J Perinatol 45, 944–950 (2025). https://doi.org/10.1038/s41372-025-02346-6

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