Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review and Meta-Analysis

Romijn, Michelle; Dhiman, Paula; Finken, Martijn J J; Kaam, Anton H. van; Katz, Trixie A; Rotteveel, Joost; Schuit, Ewoud; Collins, Gary S.; Onland, Wes; Torchin, Héloïse

doi:10.1016/j.jpeds.2023.01.024

Cited by 10 publications

(3 citation statements)

References 82 publications

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“…Based on the limited number of studies exploring the long-term effects of invasive NAVA in neonates, this meta-analysis is the first to address this knowledge gap in the literature. The prevalence of BPD continues to be substantial despite significant advancements in neonatal care and contemporary ventilatory management [ 20 , 65 , 66 , 67 , 68 ]. Compared to CMV, NAVA has been found to improve patient–ventilator interaction and minimize asynchrony [ 2 , 17 , 18 , 28 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

Optimizing Invasive Neonatal Respiratory Care: A Systematic Review of Invasive Neurally Adjusted Ventilatory Assist

Balasundaram,

Sakr

2024

Healthcare

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Background: Mechanical ventilation in preterm neonates aims for synchrony, preventing complications such as lung injury. Neurally Adjusted Ventilatory Assist (NAVA) is a unique mode relying on diaphragmatic electrical signals for synchronization. We conducted a review focusing on the long-term consequences of using invasive NAVA in neonates with a focus on bronchopulmonary dysplasia (BPD). Methods: A systematic review following PRISMA explored invasive NAVA in preterm neonates. Primary objectives compared NAVA to conventional ventilation, assessing BPD incidence, ventilation duration, length of stay, and adverse events. Secondary objectives analyzed ventilator parameters. Results: After screening 282 records, the review incorporated two randomized controlled trials for primary outcomes and seven trials for secondary outcomes, including two randomized crossovers, four prospective crossovers, and one retrospective study. NAVA showed reduced oxygen requirement at 28 days but no significant differences in oxygen need at 36 weeks postmenstrual age, total length of stay, or ventilator days. Substantial variations were not observed in adverse events. Ventilator variables favored NAVA, indicating decreased peak inspiratory pressure, tidal volume, work of breathing, and respiratory severity score. Conclusion: Our study found no significant reduction in BPD with NAVA despite short-term benefits. Future large-scale trials are essential to assess NAVA’s impact on long-term outcomes comprehensively.

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Section: Discussionmentioning

confidence: 99%

Optimizing Invasive Neonatal Respiratory Care: A Systematic Review of Invasive Neurally Adjusted Ventilatory Assist

Balasundaram,

Sakr

2024

Healthcare

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“…The advantages of this approach included a computational and molecular integration to predict BPD. Overall, there have been more than 100 prediction models that have been developed to predict BPD, as summarized in a systemic review by Romjin et al ( 77 ).…”

Section: Bpd Predictive Models and Support Toolsmentioning

confidence: 99%

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

Moreira,

Husain,

Knake

et al. 2024

Front. Pediatr.

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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.

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“…Good-quality predictive models for BPD aim to establish an individual risk of BPD, helping clinicians to identify high-risk patients and individualize care, and hopefully enabling more effective preventive strategies. While a plethora of predictive models have been developed in recent decades ( 9 ), none are fully incorporated into routine clinical practice ( 5 , 10 ) and all suffer from a high risk of bias ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of bronchopulmonary dysplasia in very preterm infants: competitive risk model nomogram

Sucasas-Alonso,

Pértega-Díaz,

Balboa-Barreiro

et al. 2024

Front. Pediatr.

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ObjectiveTo develop predictive clinical models of bronchopulmonary dysplasia (BPD) through competing risk analysis.MethodsRetrospective observational cohort study, including preterm newborns ≤32 weeks gestational age, conducted between January 1, 2013 and September 30, 2022 in a third-level Neonatal Intensive Care Unit in Spain. A prediction study was carried out using competing risk models, where the event of interest was BPD and the competing event was death. A multivariate competing risk model was developed separately for each postnatal day (days 1, 3, 7 and 14). Nomograms to predict BPD risk were developed from the coefficients of the final models and internally validated.ResultsA total of 306 patients were included in the study, of which 73 (23.9%) developed BPD and 29 (9.5%) died. On day 1, the model with the greatest predictive capacity was that including birth weight, days since rupture of membranes, and surfactant requirement (area under the receiver operating characteristic (ROC) curve (AUC), 0.896; 95% CI, 0.792–0.999). On day 3, the final predictive model was based on the variables birth weight, surfactant requirement, and Fraction of Inspired Oxygen (FiO2) (AUC, 0.891; 95% CI, 0.792–0.989).ConclusionsCompeting risk analysis allowed accurate prediction of BPD, avoiding the potential bias resulting from the exclusion of deceased newborns or the use of combined outcomes. The resulting models are based on clinical variables measured at bedside during the first 3 days of life, can be easily implemented in clinical practice, and can enable earlier identification of patients at high risk of BPD.

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Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review and Meta-Analysis

Cited by 10 publications

References 82 publications

Optimizing Invasive Neonatal Respiratory Care: A Systematic Review of Invasive Neurally Adjusted Ventilatory Assist

Optimizing Invasive Neonatal Respiratory Care: A Systematic Review of Invasive Neurally Adjusted Ventilatory Assist

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

Prediction of bronchopulmonary dysplasia in very preterm infants: competitive risk model nomogram

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