Machine learning for prediction of bronchopulmonary dysplasia-free survival among very preterm infants

Abstract

Background: Bronchopulmonary dysplasia (BPD) is one of the most common and serious sequelae of prematurity. Prompt diagnosis using prediction tools is crucial for early intervention and prevention of further adverse effects. This study aims to develop a BPD-free survival prediction tool based on the concept of the developmental origin of BPD with machine learning.

Methods: Datasets comprising perinatal factors and early postnatal respiratory support were used for initial model development, followed by combining the two models into a final ensemble model using logistic regression. Simulation of clinical scenarios was performed.

Results: Data from 689 infants were included in the study. We randomly selected data from 80% of infants for model development and used the remaining 20% for validation. The performance of the final model was assessed by receiver operating characteristics which showed 0.921 (95% CI: 0.899-0.943) and 0.899 (95% CI: 0.848-0.949) for the training and the validation datasets, respectively. Simulation data suggests that extubating to CPAP is superior to NIPPV in BPD-free survival. Additionally, successful extubation may be defined as no reintubation for 9 days following initial extubation.

Conclusions: Machine learning-based BPD prediction based on perinatal features and respiratory data may have clinical applicability to promote early targeted intervention in high-risk infants.

Keywords: Bronchopulmonary dysplasia; Machine learning; Predictive modeling; Preterm infants.

Fig. 1

A flow chart depicting the selection of study participants

Fig. 2

Feature importance scores for A Model 1 – five perinatal features, B Model 2—respiratory model data for day of life 1, C Model 3 – respiratory model data for day of life 1–7, and D Model 4 – respiratory model data for day of life 1–14. Feature importance scores were calculated based on permuting the values of the indicated feature followed by re-building the model and calculating the decrease in prediction accuracy. The scores were normalized between 0 and 100, with 0 being least important, and 100 being most important. The scores were obtained by running the varImp() function from the caret package

Fig. 3

BPD-free survival probabilities of female, appropriate for gestational age, white, antenatally non-smoking exposed infants born at 23, 26, and 29 weeks of gestation intubated at birth for indicated periods. The error bars indicate standard errors of the probabilities. This plot depicts the simulated results from Scenario 1 (see text)

Fig. 4

BPD-free survival probabilities of female, appropriate for gestational age, white, antenatally non-smoking exposed infants born at 26 weeks of gestation intubated at birth for the indicated periods followed by extubating to either continuous positive airway pressure (CPAP)/high-flow nasal cannula (HFNC) or to non-invasive positive pressure ventilation (NIPPV)/non-invasive neurally adjusted ventilatory assist (nNAVA). The error bars indicate standard errors of the probabilities. This plot depicts the simulated results from Scenario 2 (see text)

Fig. 5

BPD-free survival probabilities of female, appropriate for gestational age, white, antenatally non-smoking exposed infants born at 26 weeks of gestation intubated at birth for one full day, followed by extubation between day of life 1 and 2, and reintubation following the indicated periods of time. In the control infant, there was no reintubation. Statistical comparison of the probability of BPD-free survival was made between the control infant and each of the infants who were reintubated individually using Student’s t-test. The asterisk sign (*) indicates p-value < 0.05. The error bars indicate standard errors of the probabilities. This plot depicts the simulated results from Scenario 3 (see text)