Prediction of extubation failure among low birthweight neonates using machine learning

Predicting Extubation Readiness in Preterm Infants Utilizing Machine Learning: A Diagnostic Utility Study

OBJECTIVE

The objective of this study was to predict extubation readiness in preterm infants using machine learning analysis of bedside pulse oximeter and ventilator data.

STUDY DESIGN

This is an observational study with prospective recordings of oxygen saturation (SpO2) and ventilator data from infants <30 weeks of gestation age. Research pulse oximeters collected SpO2 (1 Hz sampling rate) to quantify intermittent hypoxemia (IH). Continuous ventilator metrics were collected (4-5-minute sampling) from bedside ventilators. Data modeling was completed using unbiased machine learning algorithms. Three model sets were created using the following data source combinations: (1) IH and ventilator (IH + SIMV), (2) IH, and (3) ventilator (SIMV). Infants were also analyzed separated by postnatal age (infants <2 or ≥2 weeks of age). Models were compared by area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 110 extubation events from 110 preterm infants were analyzed. Infants had a median gestation age and birth weight of 26 weeks and 825 g, respectively. Of the 3 models presented, the IH + SIMV model achieved the highest AUC of 0.77 for all infants. Separating infants by postnatal age increased accuracy further achieving AUC of 0.94 for <2 weeks of age group and AUC of 0.83 for ≥2 weeks group.

CONCLUSIONS

Machine learning analysis has the potential to enhance prediction accuracy of extubation readiness in preterm infants while utilizing readily available data streams from bedside pulse oximeters and ventilators.

Artificial intelligence in the NICU to predict extubation success in prematurely born infants

OBJECTIVES

Mechanical ventilation in prematurely born infants, particularly if prolonged, can cause long term complications including bronchopulmonary dysplasia. Timely extubation then is essential, yet predicting its success remains challenging. Artificial intelligence (AI) may provide a potential solution.

CONTENT

A narrative review was undertaken to explore AI's role in predicting extubation success in prematurely born infants. Across the 11 studies analysed, the range of reported area under the receiver operator characteristic curve (AUC) for the selected prediction models was between 0.7 and 0.87. Only two studies implemented an external validation procedure. Comparison to the results of clinical predictors was made in two studies. One group reported a logistic regression model that outperformed clinical predictors on decision tree analysis, while another group reported clinical predictors outperformed their artificial neural network model (AUCs: ANN 0.68 vs. clinical predictors 0.86). Amongst the studies there was an heterogenous selection of variables for inclusion in prediction models, as well as variations in definitions of extubation failure.

SUMMARY

Although there is potential for AI to enhance extubation success, no model's performance has yet surpassed that of clinical predictors.

OUTLOOK

Future studies should incorporate external validation to increase the applicability of the models to clinical settings.

A clinical informatics approach to bronchopulmonary dysplasia: current barriers and future possibilities

Bronchopulmonary dysplasia (BPD) is a complex, multifactorial lung disease affecting preterm neonates that can result in long-term pulmonary and non-pulmonary complications. Current therapies mainly focus on symptom management after the development of BPD, indicating a need for innovative approaches to predict and identify neonates who would benefit most from targeted or earlier interventions. Clinical informatics, a subfield of biomedical informatics, is transforming healthcare by integrating computational methods with patient data to improve patient outcomes. The application of clinical informatics to develop and enhance clinical therapies for BPD presents opportunities by leveraging electronic health record data, applying machine learning algorithms, and implementing clinical decision support systems. This review highlights the current barriers and the future potential of clinical informatics in identifying clinically relevant BPD phenotypes and developing clinical decision support tools to improve the management of extremely preterm neonates developing or with established BPD. However, the full potential of clinical informatics in advancing our understanding of BPD with the goal of improving patient outcomes cannot be achieved unless we address current challenges such as data collection, storage, privacy, and inherent data bias.

Developing a model to predict neonatal respiratory distress syndrome and affecting factors using data mining: A cross-sectional study

Background: One of the major challenges that hospitals and clinicians face is the early identification of newborns at risk for adverse events. One of them is neonatal respiratory distress syndrome (RDS). RDS is the widest spared respiratory disorder in immature newborns and the main source of death among them. Machine learning has been broadly accepted and used in various scopes to analyze medical information and is very useful in the early detection of RDS. Objective: This study aimed to develop a model to predict neonatal RDS and affecting factors using data mining. Materials and Methods: The original dataset in this cross-sectional study was extracted from the medical records of newborns diagnosed with RDS from July 2017-July 2018 in Alzahra hospital, Tabriz, Iran. This data includes information about 1469 neonates, and their mothers information. The data were preprocessed and applied to expand the classification model using machine learning techniques such as support vector machine, Naïve Bayes, classification tree, random forest, CN2 rule induction, and neural network, for prediction of RDS episodes. The study compares models according to their accuracy. Results: Among the obtained results, an accuracy of 0.815, sensitivity of 0.802, specificity of 0.812, and area under the curve of 0.843 was the best output using random forest. Conclusion: The findings of our study proved that new approaches, such as data mining, may support medical decisions, improving diagnosis in neonatal RDS. The feasibility of using a random forest in neonatal RDS prediction would offer the possibility to decrease postpartum complications of neonatal care.

Failed Extubation in Neonates After Cardiac Surgery: A Single-Center, Retrospective Study

OBJECTIVES

To describe factors associated with failed extubation (FE) in neonates following cardiovascular surgery, and the relationship with clinical outcomes.

DESIGN

Retrospective cohort study.

SETTING

Twenty-bed pediatric cardiac ICU (PCICU) in an academic tertiary care children's hospital.

PATIENTS

Neonates admitted to the PCICU following cardiac surgery between July 2015 and June 2018.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Patients who experienced FE were compared with patients who were successfully extubated. Variables associated with FE ( p < 0.05) from univariate analysis were considered for inclusion in multivariable logistic regression. Univariate associations of FE with clinical outcomes were also examined. Of 240 patients, 40 (17%) experienced FE. Univariate analyses revealed associations of FE with upper airway (UA) abnormality (25% vs 8%, p = 0.003) and delayed sternal closure (50% vs 24%, p = 0.001). There were weaker associations of FE with hypoplastic left heart syndrome (25% vs 13%, p = 0.04), postoperative ventilation greater than 7 days (33% vs 15%, p = 0.01), Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery (STAT) category 5 operations (38% vs 21%, p = 0.02), and respiratory rate during spontaneous breathing trial (median 42 vs 37 breaths/min, p = 0.01). In multivariable analysis, UA abnormalities (adjusted odds ratio [AOR] 3.5; 95% CI, 1.4-9.0), postoperative ventilation greater than 7 days (AOR 2.3; 95% CI, 1.0-5.2), and STAT category 5 operations (AOR 2.4; 95% CI, 1.1-5.2) were independently associated with FE. FE was also associated with unplanned reoperation/reintervention during hospital course (38% vs 22%, p = 0.04), longer hospitalization (median 29 vs 16.5 d, p < 0.0001), and in-hospital mortality (13% vs 3%, p = 0.02).

CONCLUSIONS

FE in neonates occurs relatively commonly following cardiac surgery and is associated with adverse clinical outcomes. Additional data are needed to further optimize periextubation decision-making in patients with multiple clinical factors associated with FE.

Optimal timing of extubation in preterm infants

In neonatal intensive care, endotracheal intubation is usually performed as an urgent or semi-urgent procedure in infants with critical or unstable conditions related to progressive respiratory failure. Extubation is not. Patients undergoing extubation are typically stable, with improved respiratory function. The key elements to facilitating extubation are to recognize improvement in respiratory status, promote weaning of mechanical ventilation, and accurately identify readiness for removal of the endotracheal tube. Therefore, extubation should be a planned and well-organized procedure. In this review, we will appraise the evidence for existing predictors of extubation readiness and provide patient-specific, pathophysiology-derived strategies to optimize the timing and success of extubation in neonates, with a focus on extremely preterm infants.