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Predictive Models for Neonatal Follow-Up Serum Bilirubin: Model Development and Validation
1Massachusetts General Hospital, Boston, MA, United States.
Insights
New AI models accurately predict infant jaundice (total serum bilirubin) levels, outperforming clinical judgment. This advancement aids in preventing brain damage from hyperbilirubinemia in newborns.
Area of Science:
- Neonatal Medicine
- Artificial Intelligence in Healthcare
- Predictive Analytics
Background:
- Neonatal hyperbilirubinemia is a common condition in newborns.
- Untreated, it can lead to severe, irreversible brain injury.
Purpose of the Study:
- To develop and compare the accuracy of predictive models for follow-up total serum bilirubin (TSB) measurements.
- To evaluate these models against clinician predictions.
Main Methods:
- Supervised learning models were trained on TSB measurements from 4 Massachusetts hospitals (June 2015-June 2019).
- A dataset of 27,428 measurements was used for training and 3320 for testing.
- Model performance was compared to prospective clinician predictions.
Main Results:
- Neural network and Xgboost models achieved the highest predictive accuracy (MAE 1.05 and 1.04 mg/dL, respectively).
- Key predictors included current bilirubin, rate of rise, phototherapy use, and gestational age.
- AI models significantly outperformed clinician predictions (MAE 1.06 vs. 1.38 mg/dL, P<.0001).
Conclusions:
- Developed predictive models for neonatal TSB measurements demonstrate superior accuracy compared to clinicians.
- These models are the first to predict specific bilirubin values, include diverse patient populations, and account for phototherapy effects.
Background:
Hyperbilirubinemia affects many newborn infants and, if not treated appropriately, can lead to irreversible brain injury.
Objective:
This study aims to develop predictive models of follow-up total serum bilirubin measurement and to compare their accuracy with that of clinician predictions.
Methods:
Subjects were patients born between June 2015 and June 2019 at 4 hospitals in Massachusetts. The prediction target was a follow-up total serum bilirubin measurement obtained <72 hours after a previous measurement. Birth before versus after February 2019 was used to generate a training set (27,428 target measurements) and a held-out test set (3320 measurements), respectively. Multiple supervised learning models were trained. To further assess model performance, predictions on the held-out test set were also compared with corresponding predictions from clinicians.
Results:
The best predictive accuracy on the held-out test set was obtained with the multilayer perceptron (ie, neural network, mean absolute error [MAE] 1.05 mg/dL) and Xgboost (MAE 1.04 mg/dL) models. A limited number of predictors were sufficient for constructing models with the best performance and avoiding overfitting: current bilirubin measurement, last rate of rise, proportion of time under phototherapy, time to next measurement, gestational age at birth, current age, and fractional weight change from birth. Clinicians made a total of 210 prospective predictions. The neural network model accuracy on this subset of predictions had an MAE of 1.06 mg/dL compared with clinician predictions with an MAE of 1.38 mg/dL (P<.0001). In babies born at 35 weeks of gestation or later, this approach was also applied to predict the binary outcome of subsequently exceeding consensus guidelines for phototherapy initiation and achieved an area under the receiver operator characteristic curve of 0.94 (95% CI 0.91 to 0.97).
Conclusions:
This study developed predictive models for neonatal follow-up total serum bilirubin measurements that outperform clinicians. This may be the first report of models that predict specific bilirubin values, are not limited to near-term patients without risk factors, and take into account the effect of phototherapy.
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