Predictive model of ibuprofen treatment failure in very preterm infants with patent ductus arteriosus using machine
María Carmen Bravo1, Emilio Parrado-Hernández2, Patrick J McNamara3,4
1Department of Neonatology, La Paz University Hospital, Madrid, Spain. mcarmen.bravo@salud.madrid.org.
Insights
Machine learning can predict ibuprofen treatment failure in premature infants with patent ductus arteriosus. Identifying non-responders early may improve treatment decisions and outcomes.
Area of Science:
- Neonatal Medicine
- Pediatric Cardiology
- Medical Informatics
Background:
- The management of patent ductus arteriosus (PDA) in preterm infants is a subject of ongoing debate.
- Ibuprofen is a common treatment, but predicting treatment failure (TF) is challenging.
- Developing predictive tools can aid clinical decision-making.
Purpose of the Study:
- To develop and evaluate a machine learning (ML) algorithm for predicting ibuprofen TF in very preterm infants.
- To assess the association between TF and other neonatal outcomes.
Main Methods:
- Secondary analysis of a clinical trial involving very preterm infants treated with intravenous ibuprofen for PDA.
- Development of a predictive model using ML techniques to identify TF.
- Statistical analysis of the impact of TF on neonatal outcomes.
Main Results:
- The study included 146 infants.
- A logistic regression model utilizing ML predicted TF with an area under the curve (AUC) of 0.65.
- Bronchopulmonary dysplasia (BPD) was significantly associated with TF (p=0.03).
Conclusions:
- Machine learning models can feasibly predict ibuprofen TF in PDA.
- Such predictive models can assist clinicians in optimizing PDA treatment strategies.
- Early identification of non-responders could lead to alternative treatments and potentially reduce adverse effects.
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.
