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Early prediction of mortality and morbidities in VLBW preterm neonates using machine learning
Chi-Hung Shu1, Rema Zebda2, Camilo Espinosa1
1Department of Anesthesiology, Pain, and Perioperative Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Insights
Machine learning models accurately predict mortality and morbidities in very low birth weight preterm infants. Early risk stratification enables timely interventions to improve infant health trajectories.
Area of Science:
- Neonatal medicine
- Computational biology
- Precision medicine
Background:
- Predicting mortality and morbidities in preterm infants is crucial for timely interventions.
- Early identification of health risks can improve infant outcomes.
Purpose of the Study:
- To develop machine learning (ML) algorithms for predicting mortality and specific morbidities in very low birth weight (VLBW) preterm infants.
- To integrate maternal and infant variables within the first two weeks of life for accurate risk prediction.
Main Methods:
- Developed ML algorithms using 47 features to predict mortality, bronchopulmonary dysplasia (BPD), neonatal sepsis, necrotizing enterocolitis (NEC), intraventricular hemorrhage (IVH), cystic periventricular leukomalacia (PVL), and retinopathy of prematurity (ROP).
- Utilized a retrospective cohort of 3341 infants for training and validation with 10-fold cross-validation.
- Tested models on a separate cohort of 447 infants to assess performance.
Main Results:
- Tree-based ensemble models, Random Forest (RF) and XGBoost, demonstrated superior performance.
- The area under the receiver operating characteristic curve (AUROC) for sepsis, NEC, BPD, and mortality exceeded 0.7.
- The area under the Precision-Recall curve (AUPRC) for all outcomes surpassed prevalence, indicating effective risk stratification.
Conclusions:
- Predictive analytics using ML show significant potential for advancing precision medicine in neonatology.
- Reliable prediction of adverse outcomes enables proactive interventions, potentially improving health trajectories for VLBW preterm infants.
- Individualized outcome prediction and interventions represent a significant advancement in neonatal care.
Background:
Predicting mortality and specific morbidities before they occur may allow for interventions that may improve health trajectories.
Hypothesis:
Integrating key maternal and postnatal infant variables in the first 2 weeks of age into machine learning (ML) algorithms will reliably predict survival and specific morbidities in VLBW preterm infants.
Methods:
ML algorithms were developed to integrate 47 features for predicting mortality, bronchopulmonary dysplasia (BPD), neonatal sepsis, necrotizing enterocolitis (NEC), intraventricular hemorrhage (IVH), cystic periventricular leukomalacia (PVL), and retinopathy of prematurity (ROP). A retrospective cohort (n = 3341) was used to train and validate the models with a repeated 10-fold cross-validation strategy. These models were then tested on a separate cohort (n = 447) to evaluate the final model performance.
Results:
Among the seven ML algorithms employed, tree-based ensemble models, specifically Random Forest (RF) and XGBoost, had the best performance metrics. The area under the receiver operating characteristic curve (AUROC) of sepsis with or without meningitis (0.73), NEC (0.73), BPD (0.71), and mortality (0.74) exceeded 0.7, while the area under Precision-Recall curve (AUPRC) for all outcomes was greater than the prevalence, demonstrating effective risk stratification in VLBW preterm infants.
Conclusions:
Our study demonstrates the potential of predictive analytics leveraging ML techniques in advancing precision medicine.
Impact:
Reliable prediction of adverse outcomes before they occur has the potential to institute interventions and possibly improve health trajectories in VLBW preterm infants. We used machine learning to develop and test predictive models for mortality and five major morbidities in VLBW preterm infants. Individualized prediction of outcomes and individualized interventions will advance Precision Medicine in Neonatology.
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