Evaluation and validation of a prediction model for extubation success in very preterm infants

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.

Peri-extubation settings in preterm neonates: a systematic review and meta-analysis

OBJECTIVE

To systematically review: 1) peri-extubation settings; and 2) association between peri-extubation settings and outcomes in preterm neonates.

STUDY DESIGN

In this systematic review, studies were eligible if they reported patient-data on peri-extubation settings (objective 1) and/or evaluated peri-extubation levels in relation to clinical outcomes (objective 2). Data were meta-analyzed when appropriate using random-effects model.

RESULTS

Of 9681 titles, 376 full-texts were reviewed and 101 included. The pooled means of peri-extubation settings were summarized. For objective 2, three experimental studies were identified comparing post-extubation CPAP levels. Meta-analyses revealed lower odds for treatment failure [pooled OR 0.46 (95% CI 0.27-0.76); 3 studies, 255 participants] but not for re-intubation [pooled OR 0.66 (0.22-1.97); 3 studies, 255 participants] with higher vs. lower CPAP.

CONCLUSIONS

Summary of peri-extubation settings may guide clinicians in their own practices. Higher CPAP levels may reduce extubation failure, but more data on peri-extubation settings that optimize outcomes are needed.

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.