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An Early Prediction Model for Estimating Bronchopulmonary Dysplasia in Preterm Infants
Yasemin Ezgi Kostekci1, Batuhan Bakırarar2, Emel Okulu1
1Division of Neonatology, Department of Pediatrics, Ankara University Faculty of Medicine, Ankara, Turkey.
Insights
The day 7 model accurately predicts bronchopulmonary dysplasia (BPD) or death in preterm infants, aiding in identifying those needing preventive therapies. This machine learning approach improves prognostic accuracy for high-risk neonates.
Area of Science:
- Neonatal Medicine
- Computational Biology
- Pediatric Pulmonology
Background:
- Accurate risk assessment for bronchopulmonary dysplasia (BPD) is crucial for prognosis and guiding preventive therapies in preterm infants.
- Clinical prediction models can aid in identifying high-risk neonates requiring early intervention.
- Machine learning offers potential for developing robust predictive tools for BPD outcomes.
Purpose of the Study:
- To investigate risk factors associated with BPD in preterm infants.
- To compare machine learning models for predicting BPD or death at various postnatal time points.
- To develop and evaluate a local BPD prediction estimator.
Main Methods:
- Analysis of data from 124 preterm infants.
- Evaluation of the composite outcome of BPD/death at 36 weeks postmenstrual age.
- Utilized SPSS and Weka software for data analysis and machine learning model development.
Main Results:
- Identified key risk factors including gestational age, birth weight, respiratory support, intraventricular hemorrhage, necrotizing enterocolitis, surfactant use, and late-onset sepsis.
- The prediction model developed for postnatal day 7 demonstrated the highest accuracy (89.5%) in predicting BPD or death.
- Compared prediction models across four postnatal time points (days 1, 7, 14, 28), with day 7 showing superior performance.
Conclusions:
- The postnatal day 7 prediction model is the most effective for identifying preterm infants at risk of BPD or death.
- This model can assist in identifying infants who would benefit from targeted preventive therapies.
- Further validation studies are recommended to refine individualized care strategies for preterm infants.
Introduction:
Accurate assessment of the risk for bronchopulmonary dysplasia (BPD) is critical to determine the prognosis and identify infants who will benefit from preventive therapies. Clinical prediction models can support the identification of high-risk patients. In this study, we investigated the potential risk factors for BPD and compared machine learning models for predicting the outcome of BPD/death on days 1, 7, 14, and 28 in preterm infants. We also developed a local BPD estimator.
Methods:
This study involved 124 infants. We evaluated the composite outcome of BPD/death at a postmenstrual age of 36 weeks and identified risk factors that would improve BPD/death prediction. SPSS for Windows Version 11.5 and Weka 3.9 software were used for the data analysis.
Results:
To evaluate the combined effect of all variables, all risk factors were taken into consideration. Gestational age, birth weight, mode of respiratory support, intraventricular hemorrhage, necrotizing enterocolitis, surfactant requirement, and late-onset sepsis were risk factors on postnatal days 7, 14, and 28. In a comparison of four different time points (postnatal days 1, 7, 14, and 28), the day 7 model provided the best prediction. According to this model, when a patient was diagnosed with BPD/death, the accuracy rate was 89.5%.
Conclusion:
The postnatal day 7 model was the best predictor of BPD or death. Future validation studies will help identify infants who may benefit from preventive therapies and develop individualized care.
