Early prediction of need for invasive mechanical ventilation in the neonatal intensive care unit using artificial intelligence and electronic health records: a clinical study

Adaptation to the extra-uterine environment is critical for the survival of neonates, and respiratory support is crucial in the neonatal intensive care unit (NICU). Particularly in preterm infants, lung immaturity can cause respiratory failure (RF) [1]. Every patient has various etiologies, symptoms, and progression of lung disease and different types of respiratory support devices used for treatment [2]. In the case of severe RF in neonates, invasive mechanical ventilation has been considered a life-saving treatment [1, 2]. The administration of therapies such as surfactant replacement or corticosteroids differs between NICUs depending on the physician’s experience [3]. Similarly, decisions regarding invasive mechanical ventilation (IMV) use also differ. The clinical outcomes of patients depend on the clinician’s ability to recognize the underlying status of the presented symptoms and signs. Multiple factors influence RF; therefore, accurately identifying neonates at risk for developing RF is a significant challenge for clinicians. Despite clinical advances, newborn morbidity and mortality remain high globally [4].

NICUs continuously monitor the physiological parameters of neonates, and physicians are confronted with plenty of data from many patients stored in electronic health records (EHR). Identifying the most important information required to make care decisions has become increasingly difficult. Furthermore, false-positive alarms can occasionally lead to alarm fatigue, negatively influencing clinicians [5]. The limited ability of humans to process such an enormous amount of data can lead to information overload. Thus, Artificial intelligence (AI) has begun to penetrate the healthcare systems in the NICU [6‐10]. AI techniques have been developed over the past few decades [11]. These techniques range from traditional machine learning (ML) classifiers, such as eXtreme gradient boosting (XGBoost), Random Forest, support vector machine (SVM), and linear discriminant analysis (LDA), to deep learning (DL) models, such as artificial neural networks (ANN), convolutional neural networks (CNN), and long short-term models (LSTM) [12]. DL techniques help analyze complex signals with vast amounts of information [13]. Establishing high-quality, valuable, and multidimensional neonatal datasets can provide accurate prediction models. With the increasing number of high-risk infants, AI should be considered as a tool for personalized neonatal care; however, it is not widely used for newborns, and there are only a few DL studies related to neonatal lung disease [10]. Recent studies have investigated the potential of ML in predicting a wide range of neonatal outcomes, including sepsis, morbidity, retinopathy of preterm birth, and neural development [14‐17].

This study sought to develop DL prediction models for the swift and precise detection of mechanical ventilation requirements in neonates using EHR. Moreover, our goal was to create a DL model that can be applied across all hospital tiers using data obtained non-invasively.

In this study, we demonstrated DL to support clinical decision-making concerning applying IMV among neonates using non-invasive methods such as monitoring vital signs and demographic information. RF is a critical condition commonly observed in newborns admitted to the NICU, leading to an increased mortality rate [33]. Repeated or prolonged episodes of desaturation and tachypnea, including hypoxia, neurodevelopmental impairment, persistent pulmonary hypertension, and cardiac arrest, may worsen the prognosis. The rapid and accurate decision of intubation is vital to increase survival [1, 32].

Among the articles published to date, an accurate tool for predicting intubation has yet to be established. Several neonatal severity scoring systems have been developed to predict the prognosis of critically ill neonates, including the Clinical Risk Index for Babies II (CRIB II), Neonatal Therapeutic Intervention Scoring System (NTISS), Score for Neonatal Acute Physiology II (SNAP II), Score for Neonatal Acute Physiology with Perinatal Extension II (SNAPPE-II), and Modified Sick Neonatal Score (MSNS). These scores accurately predicted mortality in the NICU, and the AUCs were approximately 0.86–0.91 [34, 35]. However, these scores were originally designed to assess the worst clinical status found in the first 24 h after admission [36]. The proposed model achieved the highest predictive accuracy for respiratory deterioration requiring IMV. Both Random Forest and XGBoost exhibited similar performances. The XGBoost model that utilized only two features (SpO₂ and FiO₂) had a lower AUROC compare to the model that incorporated all selected features. We also found that the proposed model performed better for all groups and tended to make more accurate predictions for lower GA.

We also compared MACPD using the same sensitivity level for all methods. Poncette et al. [37] described that in one of the most digitized hospitals with an increasing number of novel medical devices with their own alarms, the sheer number of alarms frequently overwhelms clinicians. Kierra Jones [38] documented that Johns Hopkins reported an average of 350 alerts per bed per day, and one intensive care unit’s (ICU) average was 771 daily. This can cause alarm fatigue, and caregivers are more likely to ignore or have trouble distinguishing between the alarms. In this study, the proposed method had the lowest alarm rate compared to all other methods at the same sensitivity level. This result indicates that the proposed method can detect the same number of high-risk patients with fewer alarms, which can help reduce alarm fatigue and workload. It also improves the selection of alarms requiring immediate intervention, provides earlier recognition of treatment, and directs care toward more efficient and individualized situations.

The strengths of our study were two-fold. First, there were no restrictions on the equipment or human resources required to use the proposed model. We developed a model that makes accurate predictions with minimal key features: GA, birth weight, corrected age, gravida, head circumference, body weight, height, chest circumference at birth, sex, FiO₂, SpO₂, BT, systolic, diastolic, and mean BP, HR, PR, and RR, which can be obtained non-invasively. The model is versatile and can be used in primary to tertiary hospitals, even in situations with limited laboratory equipment or a shortage of specialists. If the risk of IMV application in a primary hospital is high, a transfer to a tertiary hospital can be promptly considered. Secondly, the proposed model is valuable for determining whether IMV is necessary for a patient hospitalized for several hours or days. Immediately after birth, the need for IMV support becomes conspicuously evident within the framework of the neonatal resuscitation program. This encompasses indicators such as apnea, gasping, desaturation, and bradycardia. Attention may wane several hours or days into hospitalization, even though close monitoring and accurate judgment by medical staff remain necessary throughout the hospitalization period. By developing the proposed model, intubation and mechanical ventilation support can be initiated without delay due to early detection with a reduced alarm burden.

The current study has some limitations that should be addressed in future studies. First, it was limited to a single hospital, which could have affected the generalizability of the model. This is because clinicians use different criteria to determine the necessity for intubation. The application of the proposed model requires further external validation in other institutions, and a bias in therapeutic strategies is inevitable. Second, patients who were intubated before admission were excluded, and most of them were extremely low birth weight infants (ELBWI). Data pertaining to the application of IMV in cases of extreme immaturity are crucial. The ELBWI exhibited insufficient self-respiration and decreased physical activity immediately after birth. Moreover, ELBWI generally received prophylactic surfactants via an endotracheal tube. In the future, we aim to monitor and evaluate each patient from the delivery room to the NICU. Third, outborn patients did not have sufficient information regarding their maternal history, such as prenatal ultrasound or laboratory test results, which are critical factors affecting neonatal lung disease. This prospective study aimed to collect various types of maternal data.

Using non-invasive data, we demonstrated the performance of a DL-based approach in predicting the need for mechanical ventilation in neonates in the NICU. The results of the predictive and alarm performances were superior for the proposed model compared to the other models. DL approaches offer an accurate and standardized way to predict applying IMV in neonatal patients, enabling advanced bedside neonatal care and the utilization of more sophisticated techniques.