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Construction and Validation of a Risk Prediction Model for Prolonged Hospitalization of Very Premature Infants
Insights
A new predictive model can identify very preterm infants at risk for extended hospital stays. This tool aids clinicians in early risk management and decision-making for neonatal intensive care.
Area of Science:
- Neonatal Medicine
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Predicting length of stay (LOS) for very preterm infants (VPIs) is crucial for resource management and clinical decision-making.
- Early identification of VPIs with potential for extended hospitalization is needed.
Purpose of the Study:
- To develop and validate a predictive model for identifying VPIs at risk of extended length of stay (LOS).
- To support risk management and clinical decision-making in the early postnatal period for VPIs.
Main Methods:
- A cohort of 1044 VPIs was used, with 70% for training and 30% for testing.
- Five machine learning algorithms were evaluated, with logistic regression (LR) selected as the best performing.
- LOS extension was defined as exceeding the 75th percentile of hospitalization days for specific gestational age groups.
Main Results:
- The logistic regression model achieved an AUC of 0.773 in internal validation and 0.727 in external validation.
- The model demonstrated good calibration and clinical applicability via decision curve analysis.
- 23.9% of VPIs in the development cohort experienced LOS extension.
Conclusions:
- A validated predictive model can assist healthcare professionals in anticipating and managing potential LOS extensions in VPIs.
- The model offers a valuable tool for risk stratification and informed clinical decisions for VPIs.
Objectives:
This study aims to design a predictive model for extension of length of stay (LOS) in very preterm infants (VPIs), for risk management and assisted decision making in the early postnatal period.
Methods:
VPIs in the development cohort were randomly divided into training and testing sets in a 7:3 ratio. A total of 5 machine learning algorithms were used to construct and evaluate the model. LOS extension was defined as exceeding the 75th percentile of total hospitalization days for different gestational age groups.
Results:
This study included a total of 1044 VPIs in the development cohort, and 23.9% (n = 250) were classified as having LOS extension. Seven variables were screened and selected to construct the prediction model based on the best algorithm, logistic regression (LR). In the internal validation, compared with other algorithms, the LR algorithm achieved the highest area under the curve (AUC) of 0.773 (95% CI 0.717-0.830). The accuracy was 0.729, specificity was 0.782, recall was 0.566, and F1 score was 0.503. External validation of the LR model yielded an AUC value of 0.727 (95% CI 0.674-0.780). In terms of calibration curves, apart from the internal validation set showing a slight overestimation, both the training set and the external validation set demonstrated good consistency. Moreover, the decision curve analysis showed that the model has appropriate clinical applicability.
Conclusions:
The predictive model could help healthcare professionals predict and address potential risks of LOS extension in VPIs.
