Welcome to the 9th EAPS Congress Programme Scheduling

The congress will officially run on Barcelona Time (GMT+2)
To convert the congress times to your local time Click Here

Displaying One Session

Session Type
Pre-Recorded Oral Session
Date
10/06/2022
Session Time
08:00 AM - 11:59 PM
Room
Pre-Recorded Oral

MACHINE LEARNING OF RESPIRATORY OUTCOME PREDICTIONS IN PREMATURE INFANTS BORN AT < 30 WEEKS GESTATION BASED ON DEVELOPMENTAL ORIGINS OF BRONCHOPULMONARY DYSPLASIA

Presenter
  • Srinandini S. Rao (United States of America)
Date
10/06/2022
Session Time
08:00 AM - 11:59 PM
Session Type
Pre-Recorded Oral Session
Presentation Type
Abstract Submission
Lecture Time
08:00 AM - 08:10 AM
Duration
10 Minutes

Abstract

Background and Aims

Bronchopulmonary dysplasia (BPD) remains a significant challenge. As a multifactorial disease, BPD has been shown to be associated with various maternal and antenatal factors, which are then reflected as the severity of illness in the preterm infants’ immediate postnatal life. Current understanding of BPD pathophysiology points towards a developmental origin of BPD, which may be proportionate to the degree of irreversible insult during fetal lung development. We developed random forest prediction models based on this concept by combining perinatal and respiratory support data in the first 14 days of life.

Methods

This is a single-center retrospective study from 2013 to 2019 with subjects born at less than 30 weeks’ gestation randomly split into training (80%) and testing (20%) datasets. Perinatal features and respiratory mode at each 24-hour interval for the first 14 intervals were extracted from electronic medical records. A random forest (RF) algorithm was used to train perinatal and respiratory data separately, followed by developing a final ensemble model. Model performance was assessed by receiver’s operating characteristics (ROC).

Results

Table 1 shows demographic summarization. Figure 1 shows model performance for each RF model developed using perinatal data and respiratory data. Figure 2 demonstrates the final ensemble models with ROC area under the curve between 0.87-0.91 for the training dataset, and between 0.83-0.867 for the testing dataset, respectively.

Table 1

bpd - ml_demographics.png

Figures 1

bpd-ml.png

Figure 2

 bpd-ml train,test.png

Conclusions

Model performance was adequate, with perinatal data playing a significant role, reaffirming the developmental origins of BPD. This model may be further developed into a prediction tool for BPD.

Hide