A Novel Patent Ductus Arteriosus Severity Score to Predict Clinical Outcomes in Premature Neonates

Personalized Evidence-Based Management of Patent Ductus Arteriosus in Preterm Infants

There is no universal consensus on management of patent ductus arteriosus (PDA) in preterm infants and it varies significantly worldwide, even among the clinicians within units. The decision to treat requires a thorough understanding of the clinical status of the patient, clinical evaluation of PDA, echocardiographic diagnosis, and hemodynamic impact of ductal shunt on the pulmonary and systemic circulation. In this article, updated evidence on the efficacy and adverse effects of pharmacological treatment options and expectant management are presented, while highlighting the long-term benefits of PDA treatment remains equivocal and controversial. The authors propose a schematic targeted PDA treatment approach based on gestational and chronological age for practical clinical use, and they emphasize important future directions including advancement in PDA device closure techniques, diagnostic echo-parameters, hemodynamic evaluation to assess the impact on other organs, and understanding the long-term outcomes.

Automated Identification of Patent Ductus Arteriosus Using a Computer Vision Model

OBJECTIVES

Patent ductus arteriosus (PDA) is a vascular defect common in preterm infants and often requires treatment to avoid associated long-term morbidities. Echocardiography is the primary tool used to diagnose and monitor PDA. We trained a deep learning model to identify PDA presence in relevant echocardiographic images.

METHODS

Echocardiography video clips (n = 2527) in preterm infants were reviewed by a pediatric cardiologist and those relevant to PDA diagnosis were selected and labeled (PDA present/absent/indeterminate). We trained a convolutional neural network to classify each echocardiography frame of a clip as belonging to clips with or without PDA. A novel attention mechanism that aggregated predictions for all frames in each clip to obtain a clip-level prediction by weighting relevant frames.

RESULTS

In early model iterations, we discovered training with color Doppler echocardiography clips produced the best performing classifier. For model training and validation, 1145 such clips from 66 patients (661 PDA+ clips, 484 PDA- clips) were used. Our best classifier for clip level performance obtained sensitivity of 0.80 (0.83-0.90), specificity of 0.77 (0.62-0.92) and AUC of 0.86 (0.83-0.90). Study level performance obtained sensitivity of 0.83 (0.72-0.94), specificity of 0.89 (0.79-1.0) and AUC of 0.93 (0.89-0.98).

CONCLUSIONS

Our novel deep learning model demonstrated strong performance in classifying echocardiography clips with and without PDA. Further model development and external validation are warranted. Ultimately, integration of such a classifier into auto detection software could streamline PDA imaging workflow. This work is the first step toward semi-automated, bedside detection of PDA in preterm infants.

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

OBJECTIVE

To review systematically and assess the accuracy of prediction models for bronchopulmonary dysplasia (BPD) at 36 weeks of postmenstrual age.

STUDY DESIGN

Searches were conducted in MEDLINE and EMBASE. Studies published between 1990 and 2022 were included if they developed or validated a prediction model for BPD or the combined outcome death/BPD at 36 weeks in the first 14 days of life in infants born preterm. Data were extracted independently by 2 authors following the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (ie, CHARMS) and PRISMA guidelines. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (ie, PROBAST).

RESULTS

Sixty-five studies were reviewed, including 158 development and 108 externally validated models. Median c-statistic of 0.84 (range 0.43-1.00) was reported at model development, and 0.77 (range 0.41-0.97) at external validation. All models were rated at high risk of bias, due to limitations in the analysis part. Meta-analysis of the validated models revealed increased c-statistics after the first week of life for both the BPD and death/BPD outcome.

CONCLUSIONS

Although BPD prediction models perform satisfactorily, they were all at high risk of bias. Methodologic improvement and complete reporting are needed before they can be considered for use in clinical practice. Future research should aim to validate and update existing models.