CFD Simulations for Pediatric Sleep Apnea

(OSA-MRI Trial)

Recruiting in Palo Alto (17 mi)

Overseen by Alister Bates, PhD

Age: < 65

Sex: Any

Travel: May Be Covered

Time Reimbursement: Varies

Trial Phase: Phase 4

Recruiting

Sponsor: Children's Hospital Medical Center, Cincinnati

Disqualifiers: Adequately treated with CPAP, braces, others

No Placebo Group

Prior Safety Data

Approved in 4 Jurisdictions

Trial Summary

What is the purpose of this trial?

To create a validated computational tool to predict surgical outcomes for pediatric patients with obstructive sleep apnea (OSA). The first line of treatment for children with OSA is to remove their tonsils and adenoids; however, these surgeries do not always cure the patient. Another treatment, continuous positive airway pressure (CPAP) is only tolerated by 50% of children. Therefore, many children undergo surgical interventions aimed at soft tissue structures surrounding the airway, such as tonsils, tongue, and soft palate, and/or the bony structures of the face. However, the success rates of these surgeries is surprisingly low. Therefore, there a need for a tool to improve the efficacy and predict which surgical option is going to benefit each individual patient most effectively. Computational fluid dynamics (CFD) simulations of respiratory airflow in the upper airways can provide this predictive tool, allowing the effects of various surgical options to be compared virtually and the option most likely to improve the patient's condition to be chosen. Previous CFD simulations have been unable to provide information about OSA as they were based on rigid geometries, or did not include neuromuscular motion, a key component in OSA. This project uses real-time magnetic resonance imaging (MRI) to provide the anatomy and motion of the airway to the CFD simulation, meaning that the exact in vivo motion is modeled for the first time. Furthermore, since the modeling is based on MRI, a modality which does not use ionizing radiation, it is suitable for longitudinal assessment of patients before and after surgical procedures. In vivo validation of these models will be achieved for the first time through comparison of CFD-based airflow velocity fields with those generated by phase-contrast MRI of inhaled hyperpolarized 129Xe gas. This research is based on data obtained from sleep MRIs achieved with the subject under sedation. While sedating the patient post-operatively is slightly more than minimal risk, the potential benefits to each patient outweigh this risk. As 58% of patients have persistent OSA postsurgery and the average trajectory of OSA severity is an increase over time, post-operative imaging and modeling can benefit the patient by identifying the changes to the airway made during surgery and which anatomy should be targeted in future treatments.

Do I have to stop taking my current medications for the trial?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.

What data supports the effectiveness of the treatment for pediatric obstructive sleep apnea?

Research shows that adenotonsillectomy, a surgical treatment for obstructive sleep apnea (OSA) in children, can change airflow characteristics and reduce airway collapse, although it is only successful in about 50% of obese children. Computational fluid dynamics (CFD) can help identify which patients might benefit most from surgery, potentially improving treatment outcomes. ^{1 2 3 4 5}

Is CFD simulation for pediatric sleep apnea safe for children?

The research does not provide specific safety data for CFD simulations in children with sleep apnea, but it discusses using these simulations to predict surgical outcomes and improve treatment planning, which suggests they are used as a non-invasive tool rather than a direct treatment. ^{1 2 4 6 7}

How does the treatment using CFD simulations for pediatric sleep apnea differ from other treatments?

This treatment is unique because it uses computational fluid dynamics (CFD) to model and predict the effectiveness of surgical interventions like adenotonsillectomy in children with obstructive sleep apnea, potentially improving the selection of patients who will benefit most from surgery. ^{1 2 3 4 5}

Research Team

Alister Bates, PhD

Principal Investigator

Children's Hospital Medical Center, Cincinnati

Eligibility Criteria

This trial is for children aged 5-18 with obstructive sleep apnea (OSA) who haven't improved after tonsil and adenoid removal, or those who can't tolerate CPAP therapy. It's also open to kids needing surgery for OSA as per a surgeon's assessment. Kids with braces/metal rods, well-managed on CPAP, or unable to undergo MRI are excluded.

Inclusion Criteria

I may have a blocked airway due to issues like a large tongue or small jaw.

I am between 5 and 18 years old.

My parents chose surgery for me without trying CPAP first.

Exclusion Criteria

My child cannot have sedatives due to health reasons.

Standard MRI exclusion criteria as set forth by the CCHMC Department of Radiology

My child is successfully treated with CPAP.

Trial Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Data Collection Pre-Surgery

Collect data characterizing upper airway anatomy, motion, and airflow using MRI and other measurements

4 weeks

Multiple visits for imaging and data collection

Surgical Intervention and Post-Surgery Data Collection

Perform surgical interventions and collect post-surgery data to assess changes in airway anatomy and function

12 weeks

Multiple visits for surgery and follow-up imaging

Follow-up

Participants are monitored for safety and effectiveness after treatment

12 weeks

Regular follow-up visits for monitoring

Treatment Details

Interventions

• 129-Xe (Other)
• Improving Outcomes in Pediatric Obstructive Sleep Apnea With Computational Fluid Dynamics (Other)

Trial Overview The study aims to develop a computational tool using Computational Fluid Dynamics (CFD) simulations based on real-time MRI data. This tool will predict which surgical options might best improve pediatric OSA by modeling airflow in the upper airways more accurately than previous methods.

Participant Groups

Group I: Phase 2 - Contrast 129Xe MRI ages 3-18 Experimental Treatment 1 Intervention

The research team plans to collect data characterizing upper airway anatomy, motion, and airflow. In patients, these data may be recorded before and after surgery. The data may include some or all of the following: (1) Static and dynamic proton MRI of the airway. (2) Respiratory airflow measurements. (3) Data from clinical PSGs. (5) Measurements may be repeated at different levels of CPAP.

Group II: Phase 1 - Contrast 129Xe MRI ages 5-18 Experimental Treatment 1 Intervention

The research team will collect data characterizing upper airway anatomy, motion, and airflow. In patients, these data may be recorded before and after surgery. The data may include some or all of the following: (1) Static and dynamic proton MRI of the airway. (2) Respiratory airflow measurements. (3) Phase contrast MRI of inhaled gas. (4) Data from clinical PSGs. (5) Measurements may be repeated at different levels of CPAP.

Who Is Running the Clinical Trial?

Children's Hospital Medical Center, Cincinnati

Lead Sponsor

Trials

844

Recruited

6,566,000+

Steve Davis

Children's Hospital Medical Center, Cincinnati

Chief Executive Officer since 2021

MD

Daniel Ostlie

Children's Hospital Medical Center, Cincinnati

Chief Medical Officer

MD from University of North Dakota

National Institutes of Health (NIH)

Collaborator

Trials

2,896

Recruited

8,053,000+

Dr. Jeanne Marrazzo

National Institutes of Health (NIH)

Chief Medical Officer

MD from University of California, Los Angeles

Dr. Jay Bhattacharya

National Institutes of Health (NIH)

Chief Executive Officer

MD, PhD from Stanford University

Findings from Research

In a study involving 10 patients with Down syndrome and persistent obstructive sleep apnea, virtual surgery using computational fluid dynamics showed promise in assessing airflow changes and upper airway resistance, indicating a potential new method for surgical planning.

Actual surgeries were successful in treating 6 out of 10 patients, and in 8 of 10 subjects, both the apnea-hypopnea index and upper airway resistance improved after virtual surgery, suggesting that this approach could help identify effective surgical interventions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computational Modeling of Airway Obstruction in Sleep Apnea in Down Syndrome: A Feasibility Study. Mylavarapu, G., Subramaniam, D., Jonnagiri, R., et al.[2018]

A computational fluid dynamics (CFD) study involving three children with obstructive sleep apnea syndrome (OSAS) revealed that airway geometry significantly influences internal pressure distribution, particularly during breathing.

In children with OSAS, the pressure drop during inspiration occurs mainly in the area where the adenoids and tonsils overlap, indicating that airway narrowing in this region is critical, while in healthy controls, most pressure loss happens in the nasal passages.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computational fluid dynamics modeling of the upper airway of children with obstructive sleep apnea syndrome in steady flow. Xu, C., Sin, S., McDonough, JM., et al.[2013]

Adenotonsillectomy, the main surgical treatment for obstructive sleep apnea (OSA) in children, is only effective in about 50% of obese children, highlighting the need for better patient selection.

Using computational fluid dynamics to model airway flow before and after surgery can help identify which obese children are more likely to benefit from adenotonsillectomy, potentially improving surgical outcomes and reducing health risks associated with OSA.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computational modeling of upper airway before and after adenotonsillectomy for obstructive sleep apnea. Mihaescu, M., Murugappan, S., Gutmark, E., et al.[2008]

A study involving 7 adult patients with obstructive sleep apnea (OSA) used 3D models from CT scans and computational fluid dynamics (CFD) simulations to analyze upper airway flow characteristics.

The research found a strong correlation (Pearson's r = 0.91) between a CFD parameter and the apnea-hypopnea index, suggesting that this method could help assess OSA severity by providing detailed functional insights into the upper airways.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluation of human obstructive sleep apnea using computational fluid dynamics. Taherian, S., Rahai, H., Lopez, S., et al.[2023]

A computer simulation model based on the specific anatomy of four obstructive sleep apnea (OSA) patients effectively predicted the percentage and sites of upper airway collapse, showing strong agreement with findings from drug-induced sleep endoscopy (DISE).

This innovative modeling approach suggests that personalized simulations could be a valuable tool in guiding surgical interventions for OSA, potentially improving treatment outcomes for patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Constructing a patient-specific computer model of the upper airway in sleep apnea patients. Dhaliwal, SS., Hesabgar, SM., Haddad, SMH., et al.[2018]

Computational models of the upper airway can predict how different surgical manipulations, like palatal and tongue stiffening, affect pharyngeal mechanics in patients with obstructive sleep apnea (OSA).

These models, developed from MRI images and muscle activity measurements, suggest that personalized treatment strategies for OSA could be improved by understanding individual anatomical variations and their impact on airway collapse.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Use of computational modeling to predict responses to upper airway surgery in obstructive sleep apnea. Huang, Y., White, DP., Malhotra, A.[2018]

References

1. England pubmed.ncbi.nlm.nih.gov

Computational fluid dynamics study in children with obstructive sleep apnea. [2023]

2. England pubmed.ncbi.nlm.nih.gov

Computational Modeling of Airway Obstruction in Sleep Apnea in Down Syndrome: A Feasibility Study. [2018]

3. United States pubmed.ncbi.nlm.nih.gov

Computational fluid dynamics modeling of the upper airway of children with obstructive sleep apnea syndrome in steady flow. [2013]

4. United States pubmed.ncbi.nlm.nih.gov

Computational modeling of upper airway before and after adenotonsillectomy for obstructive sleep apnea. [2008]

5. England pubmed.ncbi.nlm.nih.gov

Evaluation of human obstructive sleep apnea using computational fluid dynamics. [2023]

6. United States pubmed.ncbi.nlm.nih.gov

Constructing a patient-specific computer model of the upper airway in sleep apnea patients. [2018]

7. United States pubmed.ncbi.nlm.nih.gov

Use of computational modeling to predict responses to upper airway surgery in obstructive sleep apnea. [2018]