Model-based characterization of total serum bilirubin dynamics in preterm infants

Objectives

This study aims to characterize the age-related natural dynamics of total serum bilirubin (TSB) in preterm infants through a mathematical model and to study the model parameters as potential biomarkers for detecting associated morbidities.

Methods

We proposed an exponential decay model and applied it to each infant. Patient-specific parameters were obtained by minimizing the error between measured TSB and model output. Modeling evaluation was based on root-mean-square error (RMSE). The occurrence of high-risk clinical events was analyzed based on RMSE.

Results

In a subset of the CARESS-Premi study involving 373 preterm infants (24–32 weeks’ gestation), 72 patient-specific models were fitted. RMSE ranged from 1.20 to 40.25 µmol/L, with a median [IQR] of 8.74 [4.89, 14.25] µmol/L.

Conclusions

Our model effectively characterized TSB dynamics for 72 patients, providing valuable insights from model parameters and fitting errors. To our knowledge, this is the first long-term mathematical description of natural TSB decay in preterm infants. Furthermore, the model was able to estimate the occurrence of clinical events such as necrotizing enterocolitis, as reflected by the relatively high RMSE. Future implications include the development of model-based clinical decision support systems for optimizing NICU monitoring and detecting high-risk events.

Impact

The study characterizes the natural dynamics of total serum bilirubin in preterm infants (24–32 weeks’ gestation) using a patient-specific exponential decay model.

The model describes patient-specific patterns of TSB evolution from day three to the first weeks, providing a median [IQR] root-mean-squared error of 8.74 [4.89, 14.25] µmol/L.

Complementary to previous studies focusing on the first 72–96 h, our study emphasizes the later decay course, contributing to a comprehensive long-term characterization of the natural TSB dynamics in preterm infants.

The proposed model holds potential for clinical decision support systems for the optimization of NICU monitoring and high-risk event detection.