Computer Models of Airways in Children and Young Adults With Sleep Apnea and Down Syndrome

NCT ID: NCT01902407

Last Updated: 2025-09-29

Basic Information

• Recruitment Status: ENROLLING_BY_INVITATION
• Total Enrollment: 73 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2011-03-31
• Study Completion Date: 2028-12-31

Brief Summary

The purpose of this research study is to develop a way of predicting with computers how surgery on the airway will affect night time breathing called Obstructive Sleep Apnea (OSA) in children with Down Syndrome.

A research measurement for airway resistance will also be done during the clinical sleep MRI. The airway resistance measurement will take about 10 minutes and is done during sleep. The airway resistance measurement is called critical closing pressure (Pcrit).

Detailed Description

This is a proof-of-concept study to determine if a dynamic computational model can be used to predict surgical outcomes. If the results from the study are positive, they can be used to help design a larger subsequent study. The purpose of this research is to develop a computational model that simulates OSA and different surgical treatments for OSA in children and young adults with DS. Thus, the only population that will be studied is children and young adults with DS who have persistent OSA despite having previously undergone T&A.

Obstructive sleep apnea (OSA) occurs in 50-100% of patients with Down syndrome (DS) and can significantly cause and exacerbate medical problems in these patients. Current surgical management of children with DS is imperfect. There are variable surgical success rates for both first line surgery of palatine tonsillectomy and adenoidectomy (T&A) as well as secondary surgeries performed if and when T&A fails. There is a critical need for a diagnostic modality that takes into account airway anatomy, tissue compliance, and collapsibility to be able to predict surgical outcome and improve surgical planning in these patients. Our central hypothesis is that upper airway flow-structure interaction (FSI) modeling using three-dimensional (3-D) computational simulations from dynamic magnetic resonance imaging (MRI or MR) data can be used to predict surgical outcomes for children with DS who have OSA despite previous T&A. The long-term goal is to improve surgical outcome of children with Down syndrome and OSA by creating an accurate FSI predictive model. Such a diagnostic tool would help tailor surgical procedures to be more effective as well as identify and avoid unnecessary or unhelpful surgical procedures. These created models can in future be adjusted and applied to other populations with OSA. Our specific aims include: 1) In children with Down syndrome and persistent OSA despite previous T&A, to collect data characterizing upper airway anatomy, tissue compliance, and collapsibility; 2) to generate and validate individualized dynamic FSI models for each child and 3) to use the validated dynamic computational models to predict the success of surgical treatment on children with Down syndrome who have persistent OSA despite previous T&A. This work is innovative as it uses dynamic rather than static MR imaging data and applies a unique computational model that accurately captures the unsteadiness of the flow and accounts for the interaction between the airflow and the surrounding airway flexible structures.

Research components will involve two parts of the project. The first will be the generation, validation and use of computational models from MRI data. The second is the measure of critical closing pressure (Pcrit) on DS subjects who are at least three months post T&A, have OSA and are being evaluated for possible additional airway surgery. The measurement of Pcrit will be done during the research PSG (in the Sleep Center) and during the clinical sleep MRI (in the MRI suite). Pcrit measurements will be acquired with the use of a Continuous Positive Air Pressure (CPAP) mask during sleep. Additionally, to measure improvement in OSA based on quality of life (QOL) and sleep, the Obstructive Sleep Apnea questionnaire (OSA18) will be administered both preoperatively and postoperatively.

Conditions

Down Syndrome; Obstructive Sleep Apnea

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Diagnostic MRI and CT scan of the airway

All patients seen at CCHMC (up to 90 years of age) who are scheduled to have a clinical sleep MRI or CT scan for their OSA airway or lung disease.

Scheduled for both Sleep diagnostic tests (Sleep MRI and CT scans).

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

1. All patients seen at CCHMC (up to 90 years of age) who are scheduled to have a clinical sleep MRI or CT scan for their OSA airway or lung disease.

2. Both Sleep diagnostic tests (Sleep MRI and CT scans).

Exclusion Criteria

• Those patients whose body weight (>350 pounds) or circumference is greater than what can be safely accommodated by the MRI scanner
• Patients with pacemakers or other non-MRI compatible devices
• Patients with extensive dental hardware that causes MR artifact obscuring visualization of the area of interest.
• Body Mass Index (BMI) > 40

• Minimum Eligible Age: 1 Year
• Maximum Eligible Age: 90 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Heart, Lung, and Blood Institute (NHLBI)

NIH

Sponsor Role: collaborator

Children's Hospital Medical Center, Cincinnati

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Raouf Amin, MD

Role: PRINCIPAL_INVESTIGATOR

Children's Hospital Medical Center, Cincinnati

Locations

Cincinnati Children's Hospital Medical Center

Cincinnati, Ohio, United States

Countries

United States

References

Mahmoud M, Ishman SL, McConnell K, Fleck R, Shott S, Mylavarapu G, Gutmark E, Zou Y, Szczesniak R, Amin RS. Upper Airway Reflexes are Preserved During Dexmedetomidine Sedation in Children With Down Syndrome and Obstructive Sleep Apnea. J Clin Sleep Med. 2017 May 15;13(5):721-727. doi: 10.5664/jcsm.6592.

Reference Type: DERIVED

PMID: 28356179 (View on PubMed)

Other Identifiers

R01HL105206-01

Identifier Type: NIH

Identifier Source: secondary_id

View Link

CIN001-Dymosa

Identifier Type: -

Identifier Source: org_study_id