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Rehospitalization in Preterm Infants: Machine Learning Prediction Model and Associated Risk Factors
Dana Benni1,2, Roni Ramon-Gonen3, Gil Klinger4,5
1Department of Management, Bar-Ilan University, Ramat Gan, Israel, dana1509.db@gmail.com.
Insights
A new machine learning model predicts one-year rehospitalization risk in preterm infants after Neonatal Intensive Care Unit discharge. Key factors include early gestational age, low birth weight, and specific medical conditions, aiding early intervention.
Area of Science:
- Neonatal Medicine
- Machine Learning in Healthcare
- Predictive Analytics
Background:
- Preterm birth is a significant global health challenge.
- Preterm infants face elevated risks of morbidity and rehospitalization post-Neonatal Intensive Care Unit (NICU) discharge.
- Predictive modeling for these outcomes remains underexplored.
Purpose of the Study:
- To develop a machine learning model predicting one-year rehospitalization in preterm infants.
- To identify clinical factors associated with increased rehospitalization risk.
- To enhance early intervention strategies for high-risk preterm infants.
Main Methods:
- Retrospective cohort study of 2,226 preterm infants (2018-2023).
- Data sourced from NICU and inpatient records.
- eXtreme Gradient Boosting (XGBoost) machine learning algorithm utilized with 20 clinical predictors.
Main Results:
- 16.1% of preterm infants were rehospitalized within one year.
- The predictive model achieved an Area Under the Curve (AUC) of 0.69.
- Significant predictors included early gestational age, low birth weight, prolonged NICU stay, trisomy, low socioeconomic status, specific medical conditions (gastrointestinal, neurological, bronchopulmonary dysplasia), surgical interventions, and abnormal lab values.
Conclusions:
- This study presents a novel machine learning model for predicting one-year rehospitalization in preterm infants.
- The model integrates clinical data for improved risk stratification.
- Findings can inform targeted interventions for high-risk preterm infants, addressing a critical gap in neonatal care.
Introduction:
Preterm birth is a major global health concern, with preterm infants (PIs) at increased risk of morbidity and rehospitalization in the first year after neonatal intensive care unit (NICU) discharge. Few studies have applied predictive modeling in this domain. This study aimed to develop a machine learning model to predict rehospitalization within 1 year of NICU discharge and to identify clinical characteristics associated with increased rehospitalization risk.
Methods:
This is a retrospective cohort study of 2,226 PIs born between 2018 and 2023 at a tertiary-care pediatric hospital in Israel. Data were obtained from NICU and inpatient records, including clinical history, laboratory results, and hospitalization outcomes. A machine learning model was developed using the eXtreme Gradient Boosting algorithm (XGBoost) with 20 clinical predictors.
Results:
Rehospitalization within 1 year occurred in 358 of 2,226 (16.1%) PIs, with one-third occurring within 30 days. The predictive model achieved an AUC of 0.69 (95% CI: 0.59-0.78), sensitivity of 0.38, specificity of 0.87, positive predictive value of 0.38, and negative predictive value of 0.87. Key predictors included early gestational age, lower birth weight, discharge weight >2,000 g, prolonged NICU stay, trisomy, low socioeconomic score, gastrointestinal and neurological conditions, bronchopulmonary dysplasia, surgical interventions, and abnormal laboratory values.
Conclusions:
This study introduces one of the first machine learning models for predicting 1-year rehospitalization in PIs, combining predictive modeling with interpretability through the integration of novel parameters. The model provides insight into high-risk profiles and may support early targeted interventions in an underexplored clinical area.