Predicting the Need for Intubation Within 3 Hours in the Neonatal Intensive Care Unit Using a Multimodal Deep Neural Network

Abstract

Respiratory distress is a common chief complaint in neonates admitted to the neonatal intensive care unit. Despite the increasing use of non-invasive ventilation in neonates with respiratory difficulty, some of them require advanced airway support. Delayed intubation is associated with increased morbidity, particularly in urgent unplanned cases. Early and accurate prediction of the need for intubation may provide more time for preparation and increase safety margins by avoiding the late intubation at high-risk infants. This study aimed to predict the need for intubation within 3 hours in neonates initially managed with non-invasive ventilation for respiratory distress during the first 48 hours of life using a multimodal deep neural network. We developed a multimodal deep neural network model to simultaneously analyze 4 time series data collected at 1-hour intervals and 19 variables including demographic data, physiological parameters, and laboratory values. Evaluating the dataset of 128 neonates with respiratory distress who underwent non-invasive ventilation, our model achieved an area under the curve of 0.917, sensitivity of 85.2%, and specificity of 89.2%. These findings demonstrated that the multimodal model successfully predicted the need for intubation within 3 hours.

Predicting CPAP failure after less invasive surfactant administration (LISA) in preterm infants by machine learning model on vital parameter data: a pilot study

“…study in a larger and preferably external patient cohort in order to reduce the influence of individual patient and center characteristics (Siu et al 2020, Im et al 2022.…”

“…ML-algorithms have been used to predict several clinical outcomes in the neonatal intensive care unit (NICU) (McAdams et al 2022). Next to univariate (Gulczyńska et al 2019) and multivariate analysis (Roberts et al 2020) to predict intubation in preterm infants, machine learning (ML) algorithms analyzing clinical and low resolution vital parameter data were able to predict the need for intubation in the intensive care setting (Siu et al 2020, Im et al 2022, Kanbar et al 2023. However, until now, no study focused on predicting CPAP-F after the newly developed LISA method while incorporating ML analysis of high resolution physiological data.…”