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.

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 h in neonates initially managed with non-invasive ventilation for respiratory distress during the first 48 h of life using a multimodal deep neural network. We developed a multimodal deep neural network model to simultaneously analyze four time-series data collected at 1-h intervals and 19 variables including demographic, physiological and laboratory parameters. 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 demonstrate promising results for the multimodal model in predicting neonatal intubation within 3 h.

No abstract

Neonates admitted to neonatal intensive care units (NICUs) are at risk for respiratory decompensation and may require endotracheal intubation. Delayed intubation is associated with increased morbidity and mortality, particularly in urgent unplanned intubation. By accurately predicting the need for intubation in real-time, additional time can be made available for preparation, thereby increasing the safety margins by avoiding high-risk late intubation. In this study, the probability of intubation in neonatal patients with respiratory problems was predicted using a deep neural network. A multimodal transformer model was developed to simultaneously analyze time-series data (1-3 h of vital signs and FiO 2 setting value) and numeric data including initial clinical information. Over a dataset including information of 128 neonatal patients who underwent noninvasive ventilation, the proposed model successfully predicted the need for intubation 3 h in advance (area under the receiver operator characteristic curve = 0.880 ± 0.051, F1-score = 0.864 ± 0.031, sensitivity = 0.886 ± 0.041, specificity = 0.849 ± 0.035, and accuracy = 0.857 ± 0.032). Moreover, the proposed model showed high generalization ability by achieving AUROC 0.890, F1-score 0.893, specificity 0.871, sensitivity 0.745, and accuracy 0.864 with an additional 91 dataset for testing.