Computational Modeling of Cleft Lip Nasal Deformity and Assessment of Nasal Function and Treatment Outcomes

January 11, 2024 updated by: Duke University

Computational Modeling of the Mature Unilateral Cleft Lip Nasal Deformity for Objective Assessment of Patient Nasal Function and Treatment Outcomes

The purpose of this study is to use computers to simulate airflow in 3D construction of your nasal cavity generated from cone beam CT images. The results from computer simulations will help researchers identify the severity of cleft-induced nasal dysfunction and assess the impact of current treatment in restoring breathing function. The ultimate goal is to improve post-surgery outcomes to restore nasal breathing function to normal levels.

Study Overview

Study Type

Observational

Enrollment (Estimated)

40

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

    • North Carolina
      • Durham, North Carolina, United States, 27710
        • Recruiting
        • Duke University Medical Center and affiliated practices
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients with unilateral cleft lip nasal deformity scheduled to undergo elective surgery for nasal obstruction at the Duke Cleft and Craniofacial Center.

Existing CBCT imaging data and QOL measures previously collected on healthy subjects will be used for comparison. Healthy subjects will not be prospectively enrolled in the study.

Description

Inclusion Criteria:

  • Provide signed and dated informed consent form.
  • Willing to comply with all study procedures and be available for the duration of the study.
  • Male or female, aged ≥18 years of age.
  • Clinical diagnosis of unilateral cleft lip nasal deformity (uCLND)
  • Scheduled to undergo elective surgery for nasal obstruction
  • Scheduled to have a Cone Beam Computed Tomography (CBCT) as part of the pre- operative work-up for elective surgery.

Exclusion Criteria:

  • Prior cleft rhinoplasty or septoplasty for correction of nasal obstruction
  • Pregnant women: A pregnancy test will be performed within 48 hours of baseline
  • Patients unable or unwilling to comply with study procedures outlined in protocol

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Prospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Unilateral Cleft Lip Nasal Deformity (uCLND)
uCLND patients who are scheduled to undergo surgical treatment for nasal obstruction as standard of care.
Patient reported quality of life questionnaires
Other Names:
  • observational, patient reported outcome measures
Healthy Subjects
Existing data from healthy subjects with no prior symptoms of nasal obstruction used to create normative ranges for comparison to uCLND cohort.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Validation of computational fluid dynamics simulation with breathing measurement
Time Frame: 6 months
Percent agreement between computational fluid dynamics simulated nasal resistance and nasal resistance from rhinomanometry breathing measurement
6 months
Validation of computational fluid dynamics simulation with in vitro experiment
Time Frame: 6 months
Percent agreement between computational fluid dynamics simulated nasal resistance and nasal resistance measurement from in vitro experiment of 3D printed plastic nasal replica
6 months
Effectiveness of current surgery in restoring nasal function based on volumetric airflow
Time Frame: 6 months
Analysis of significant difference in volumetric airflow rate between pre-surgery and post-surgery data from computational fluid dynamics simulations of patient-specific 3D nasal models generated from cone-beam computed tomography images.
6 months
Effectiveness of current surgery in restoring nasal function based on nasal resistance
Time Frame: 6 months
Analysis of significant difference in nasal resistance between pre-surgery and post-surgery data from computational fluid dynamics simulations of patient-specific 3D nasal models generated from cone-beam computed tomography images
6 months
Effectiveness of current surgery in restoring nasal function based on nasal heat flux
Time Frame: 6 months
Analysis of significant difference in nasal heat flux between pre-surgery and post-surgery data from computational fluid dynamics simulations of patient-specific 3D nasal models generated from cone-beam computed tomography images.
6 months
Effectiveness of current surgery in restoring nasal function based on nasal moisture flux
Time Frame: 6 months
Analysis of significant difference in nasal moisture flux between pre-surgery and post-surgery data from computational fluid dynamics simulations of patient-specific 3D nasal models generated from cone-beam computed tomography images.
6 months
Anatomical sites of greatest nasal obstruction
Time Frame: 6 months
Analysis on agreement of computational fluid dynamics identified anatomical sites of greatest nasal obstruction from pre-surgery 3D nasal models and actual surgical anatomical sites. CFD identified sites of greatest nasal obstruction are regions in the airway with highest nasal resistance.
6 months
Computational fluid dynamics based optimized treatment options for unilateral cleft lip nasal deformity patients based on volumetric airflow
Time Frame: 6 months
Creation of virtual surgery nasal airway models based on computational fluid dynamics identified anatomical sites of greatest nasal obstruction. Computational fluid dynamics generated unilateral left and right side percent asymmetric will be computed used to identify the top three virtual surgery nasal airway models with best treatment potentials for unilateral cleft lip nasal deformity patients
6 months
Computational fluid dynamics based optimized treatment options for unilateral cleft lip nasal deformity patients based on nasal resistance
Time Frame: 6 months
Creation of virtual surgery nasal airway models based on computational fluid dynamics identified anatomical sites of greatest nasal obstruction. Computed nasal resistance will be used to identify the top three virtual surgery nasal airway models with best treatment potentials for unilateral cleft lip nasal deformity patients
6 months
Computational fluid dynamics based optimized treatment options for unilateral cleft lip nasal deformity patients based on nasal heat flux
Time Frame: 6 months
Creation of virtual surgery nasal airway models based on computational fluid dynamics identified anatomical sites of greatest nasal obstruction. Computed nasal moisture flux will be used to identify the top three virtual surgery nasal airway models with best treatment potentials for unilateral cleft lip nasal deformity patients
6 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change in patient-reported outcome (PRO) measures
Time Frame: baseline, 2 weeks, 8 weeks, 6 months, 12 months
Change PRO measures as measured by NOSE questionnaire
baseline, 2 weeks, 8 weeks, 6 months, 12 months
Change in patient-reported outcome (PRO) measures
Time Frame: baseline, 2 weeks, 8 weeks, 6 months, 12 months
Change PRO measures as measured by SCHNOS questionnaire
baseline, 2 weeks, 8 weeks, 6 months, 12 months
Change in patient-reported outcome (PRO) measures
Time Frame: baseline, 2 weeks, 8 weeks, 6 months, 12 months
Change PRO measures as measured by CLEFT-Q questionnaire
baseline, 2 weeks, 8 weeks, 6 months, 12 months
in vitro analysis for top three virtual surgery nasal models with best treatment based on nasal pressure
Time Frame: 6 months
in vitro analysis from 3D printed plastic nasal replica of top three virtual surgery nasal models with best treatment potential will be done using unilateral airflow pressure.
6 months
in vitro analysis for top three virtual surgery nasal models with best treatment based on unilateral volumetric airflow
Time Frame: 6 months
in vitro analysis from 3D printed plastic nasal replica of top three virtual surgery nasal models with best treatment potential will be done using unilateral volumetric airflow rate.
6 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Investigators

  • Principal Investigator: Dennis Frank-Ito, PhD, Duke University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

November 1, 2019

Primary Completion (Estimated)

December 1, 2024

Study Completion (Estimated)

June 1, 2025

Study Registration Dates

First Submitted

October 24, 2019

First Submitted That Met QC Criteria

October 31, 2019

First Posted (Actual)

November 5, 2019

Study Record Updates

Last Update Posted (Estimated)

January 15, 2024

Last Update Submitted That Met QC Criteria

January 11, 2024

Last Verified

January 1, 2024

More Information

Terms related to this study

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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