Association Between Dead Space to Tidal Volume Ratio and Duration of Respiratory Support After Extubation in Children With Congenital Heart Disease.

Abstract

Background

Children with cardiac disease liberated from mechanical ventilation often receive noninvasive respiratory support (NRS) postextubation via high-flow nasal cannula, CPAP, or noninvasive ventilation. Predicting the type and duration of postextubation NRS can be challenging due to a lack of objective tools to guide decision-making. The dead space to tidal volume ratio (VD/VT) is a potential tool to guide this decision. We hypothesized that an elevated VD/VT would be associated with longer duration and higher level of NRS following extubation in children with cardiac disease.

Methods

We conducted a retrospective cohort study of mechanically ventilated patients admitted to our pediatric cardiac intensive care unit between March 2019 and July 2021 with at least one VD/VT recorded before extubation. Subjects were dichotomized a priori into two groups VD/VT < 0.30 and VD/VT ≥ 0.30. We recorded the type of NRS at 24 hours, 48 hours, 72 hours, 7 days, and 14 days after extubation.

Results

We included 226 subjects. Median (IQR) weight was 4.1 (3.3-6.6) kg, 47% were female, 47% had cyanotic heart disease, and 90% were mechanically ventilated for respiratory failure or cardiac surgery. Subjects with VD/VT ≥ 0.30 experienced longer postextubation NRS (4 [1.9-9.1] vs 3 [1.2-5.3] days, P = .001) and were more likely to receive high-flow nasal cannula (67% vs 45%, P = .02) 24 hours following extubation. NRS modality immediately postextubation and reintubtion rates were similar between groups. Subjects with VD/VT ≥ 0.30 were younger (1.2 [0.1-3.6] vs 4.8 [1.2-30] months, P < .001) and more likely to have cyanotic congenital heart disease (59% vs 26%, P < .001). After adjusting for demographic and clinical characteristics, VD/VT was not associated with NRS use.

Conclusions

VD/VT was not associated with the length of NRS after extubation or re-intubation after controlling for demographic and clinical differences.

Department

Description

Provenance

Subjects

Humans, Respiratory Insufficiency, Heart Defects, Congenital, Tidal Volume, Respiration, Artificial, Ventilator Weaning, Retrospective Studies, Respiratory Dead Space, Time Factors, Child, Child, Preschool, Infant, Infant, Newborn, Intensive Care Units, Pediatric, Female, Male, Airway Extubation, Noninvasive Ventilation

Citation

Published Version (Please cite this version)

10.1177/19433654251362706

Publication Info

Bederman, Leonid, Monica Alvarez, Andrew G Miller, Elizabeth J Thompson, Rachel M Watts, Alexandre T Rotta and Karan R Kumar (2026). Association Between Dead Space to Tidal Volume Ratio and Duration of Respiratory Support After Extubation in Children With Congenital Heart Disease. Respiratory care, 71(1). pp. 1–8. 10.1177/19433654251362706 Retrieved from https://hdl.handle.net/10161/34244.

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Scholars@Duke

Thompson

Elizabeth Thompson

Assistant Professor of Pediatrics
Rotta

Alexandre Tellechea Rotta

Professor of Pediatrics
Kumar

Karan Ravindra Kumar

Assistant Professor of Pediatrics

I am an Assistant Professor of Pediatrics and a Pediatric Critical Care Physician at Duke University School of Medicine, and I am also affiliated with the Duke Clinical Research Institute (DCRI). My research focuses on advancing pediatric healthcare through the integration of biostatistics, clinical research informatics, and data science. I have significant experience managing large datasets, including the Pediatrix Medical Group Clinical Data Warehouse and the NICHD-funded Pediatric Trials Network, which have provided vital insights into disease patterns and pediatric drug labeling. I contribute analytical support to various pediatric research initiatives. My efforts extend to exploring automation of data integration in clinical trials, enhancing research infrastructure, and leading critical trials and registries to improve pediatric health outcomes. I am enthusiastic about applying my expertise in biostatistics, database management, and predictive analytics to future projects.


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