Related Experiment Video
Updated: Jun 29, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Immediate postnatal prediction of death or bronchopulmonary dysplasia among very preterm and very low birth weight
Kota Yoneda1, Tomohisa Seki2, Yoshimasa Kawazoe2,3
1Department of Pediatrics, The University of Tokyo Hospital, Tokyo, Japan.
Insights
New models predict bronchopulmonary dysplasia (BPD) and mortality in preterm infants using early postnatal data. These gradient boosting decision tree (GBDT) models offer improved accuracy for timely intervention.
Area of Science:
- Neonatal Medicine
- Computational Biology
- Pediatric Pulmonology
Background:
- Bronchopulmonary dysplasia (BPD) is a significant global health challenge.
- Early prediction of BPD is crucial for mitigating preterm birth complications.
- Existing predictive models lack early postnatal applicability.
Purpose of the Study:
- To develop predictive models for BPD and mortality.
- To utilize immediate postnatal clinical data for prediction.
- To improve upon existing predictive models' early applicability.
Main Methods:
- Extracted clinical data from a Japanese nationwide database (2008-2018).
- Employed gradient boosting decision trees (GBDT) algorithm for prediction within 6 hours postpartum.
- Assessed model validity and adequacy using Shapley additive explanations (SHAP) values.
Main Results:
- Developed three models predicting "death or BPD," "death or severe BPD," and "death before discharge" with high accuracy (AUCs 0.828-0.887).
- GBDT models outperformed multivariable logistic regression.
- Identified key predictors: gestational age, size at birth, male sex, and persistent pulmonary hypertension.
Conclusions:
- GBDT models demonstrate superior prognostic performance for early postnatal prediction of BPD and mortality.
- SHAP value-based clustering reveals clinically relevant infant strata.
- The GBDT algorithm shows efficacy for early BPD prediction in neonates.
Introduction:
Bronchopulmonary dysplasia (BPD) poses a substantial global health burden. Individualized treatment strategies based on early prediction of the development of BPD can mitigate preterm birth complications; however, previously suggested predictive models lack early postnatal applicability. We aimed to develop predictive models for BPD and mortality based on immediate postnatal clinical data.
Methods:
Clinical information on very preterm and very low birth weight infants born between 2008 and 2018 was extracted from a nationwide Japanese database. The gradient boosting decision trees (GBDT) algorithm was adopted to predict BPD and mortality, using predictors within the first 6 h postpartum. We assessed the temporal validity and evaluated model adequacy using Shapley additive explanations (SHAP) values.
Results:
We developed three predictive models using data from 39,488, 39,096, and 40,291 infants to predict "death or BPD," "death or severe BPD," and "death before discharge," respectively. These well-calibrated models achieved areas under the receiver operating characteristic curve of 0.828 (95% CI: 0.828-0.828), 0.873 (0.873-0.873), and 0.887 (0.887-0.888), respectively, outperforming the multivariable logistic regression models. SHAP value analysis identified predictors of BPD, including gestational age, size at birth, male sex, and persistent pulmonary hypertension. In SHAP value-based case clustering, the "death or BPD" prediction model stratified infants by gestational age and persistent pulmonary hypertension, whereas the other models for "death or severe BPD" and "death before discharge" commonly formed clusters of low mortality, extreme prematurity, low Apgar scores, and persistent pulmonary hypertension of the newborn.
Conclusions:
GBDT models for predicting BPD and mortality, designed for use within 6 h postpartum, demonstrated superior prognostic performance. SHAP value-based clustering, a data-driven approach, formed clusters of clinical relevance. These findings suggest the efficacy of a GBDT algorithm for the early postnatal prediction of BPD.
Related Concept Videos
01:03Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
01:21Cancer Survival Analysis
01:07End Point Prediction: Gran Plot
For potentiometric titration, the Gran plot is created by plotting...
01:27Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies
Medical History
01:26COPD: Management Using Bronchodilators and Corticosteroids
01:17Classification of Illness
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...