Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT06101017 |
| Other study ID # |
MS.0823.001 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
October 12, 2023 |
| Est. completion date |
October 31, 2025 |
Study information
| Verified date |
October 2023 |
| Source |
Keratoplasty Alliance International |
| Contact |
Nitin G Vaswani, MD |
| Phone |
7577263449 |
| Email |
nitin[@]manoshealth.com |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
The goal is to develop a nationwide registry to track longitudinal clinical outcomes of and
store imaging data related to numerous corneal conditions. There are two main objectives
including the establishment of the first nationwide corneal transplant registry in the United
States to include information related to the donor tissue, recipient, surgical procedure, and
long-term clinical outcomes. Ultimately, this prospective data collection will allow us to
determine prognostic factors for successful corneal transplantation and create an algorithm
to guide clinical practice based on real world outcomes. The second objective is to collect
and create a database of historical, de-identified optical coherence topography (OCT) and
corneal topography images to ultimately develop artificial intelligence (AI) based diagnostic
and prognostic algorithms for corneal disease and surgery.
Description:
Background. Overview of ocular conditions and global statistics Corneal disease is the fifth
leading cause of blindness in the world, and approximately 4.5 million individuals have
moderate to severe vision impairment secondary to loss of corneal clarity. Compared to other
leading causes of blindness, corneal disease primarily affects a younger population and
therefore has a greater disability-adjusted life years. Only 1 in 70 individuals with corneal
blindness ultimately undergoes corneal transplantation due to a number of issues including
socioeconomic and political factors. As a result, the number of keratoplasty procedures
completed in the U.S. per annum is about 50,000.
Tracking Long-Term Outcomes After Corneal Transplantation In the United States, there is
currently no registry or database tracking donor or recipient longitudinal outcomes after
corneal transplantation. Other organ transplants including kidney, liver, heart, lung, and
pancreas have an established registry , despite corneal transplants being one of the most
common transplantations in the US. Australia is one of the few countries that has an
established corneal graft registry since 1985, which has provided invaluable insight to
determine positive and negative prognostic factors affecting corneal graft survival. In order
to obtain best subject outcomes, clinical practice should ideally be tailored to selecting
the best type of surgery (i.e. penetrating keratoplasty [PKP], endothelial keratoplasty [EK],
anterior lamellar keratoplasty [ALK], or artificial cornea) for each individual patient,
based on real world outcomes data.
Developing and utilizing artificial intelligence for corneal disease Machine learning, which
plays an ever-growing role in developing artificial intelligence systems for medical
applications, is a powerful means of handling very large data sets. A variety of algorithms
can incorporate many values more efficiently and accurately than humans. Imaging studies are
particularly rich, making them well-suited for machine learning.
An accurate AI/ML-enabled algorithm assessment of various imaging studies could improve
precision over physical exams, improving patient outcomes by earlier and more accurate
detection of abnormalities and better prediction of future outcomes. Additionally,
AI/ML-enabled remote collection of patient data presents substantial potential benefits for
patients, providers, and the broader health system to monitor disease, outcomes of surgery or
treatment. With home- or community-based monitoring, healthy patients can save time and money
traveling frequently to the clinic. For those where issues are detected, potential ocular
conditions or post-surgical complications can be identified earlier before they become more
severe and require intervention or surgery, which improves both patient outcomes and saves
health system resources.
Objectives. Primary: To establish the first nationwide corneal registry in the United States
to include information related to the disease state, information on donor tissue, recipient
data, surgical procedure, and long-term clinical outcomes. Ultimately, this prospective data
collection will allow us to determine prognostic factors for successful corneal
transplantation and create an algorithm to guide clinical practice based on real world
outcomes.
Secondary: To collect and create a database of de-identified imaging studies (including but
not limited to optical coherence topography (OCT), in vivo confocal biomicroscopy, specular
biomicroscopy, and corneal topography) to ultimately develop artificial intelligence (AI)
based diagnostic and prognostic algorithms for corneal disease prevalence, progression and
surgery outcomes.
Study Design. Design Prospective and observational.
Study Size The initial study subject recruitment will be piloted at a variety of US centers.
All eligible subjects will be recruited and consented subjects will be enrolled during the
initial phase of the study.
Data Collection US based corneal surgeons will obtain corneal images pre- and post- corneal
transplantation. These de-identified images, along with the clinical information (donor and
recipient characteristics, surgical information, and longitudinal outcomes afterwards) will
be entered into the registry.
Data Elements for Corneal Graft Registry For the subjects undergoing corneal transplantation,
the following elements will be collected and entered into a secure, electronic database. The
imaging data source for this study are copies of corneal topography OCT, specular
biomicroscopy and in vivo confocal biomicroscopy images produced during routine clinical
care. The registry will receive copies of images in any format, including electronic data
transfers and CDs. OCT images from different providers and care sites may vary in quality and
detail. The abstraction process will map data to a single cohesive data schema.
Data sources All OCT and corneal topography images are de-identified with no subject health
information. Only the raw images will be collected for analysis, and OCT images will be
compiled with an aim to create an online registry.
Data collection and storage OCT images will be submitted by healthcare providers through a
secure, encrypted, imaging request platform with personnel follow-up as needed. Imaging
documentation is uploaded to the study's servers and de-identified of all subject data and
protected health information (PHI).
Data abstraction Study staff with expertise in assessing OCT images will review all images
submitted to detect patterns. These patterns will eventually be used to train AI/ML
algorithms for the collection of measurement data.
Data security This study will comply with Health Insurance Portability and Accountability Act
(HIPAA) security standards. In addition, the study team has a comprehensive set of security
policies, including risk management strategies, incident response protocols, access controls,
encryption standards, and study staff training to safeguard all subject images submitted.
Proposed Algorithm Development. Description of proposed Machine Learning method The algorithm
has the opportunity to be the most versatile of any automated OCT image classifier and data
collector. With enough data, it also can be the most accurate. The algorithm will be trained
and optimized using a variety of OCT data from this study.
Data Management. Retention of images Images and documents pertaining to the study will be
retained for the length of time required by relevant national or local health authorities,
whichever is longer. After that period of time, the documents may be destroyed, subject to
local regulations.
Data quality assurance policies The study team ensures the accuracy of data abstracted from
OCT images through a range of measures leveraging both technology and human expertise. The
image collection platform is designed to flag irregularities and low confidence images using
conservative thresholds.
The study team undergoes a training program and must pass rigorous data quality testing
before assuming full imaging screening responsibilities. All images are screened by a minimum
of two reviewers, and difficult scenarios that are not described in standard procedures are
escalated to a senior team lead, per policy. Procedures to document, review, and learn from
escalations create feedback loops that improve operational effectiveness and reduce human
error.
The study team will maintain logs of all data transformations and perform regular internal
data quality audits. The data quality will be continuously monitored and analyzed throughout
the submission and review process.
Access to Registry. Role-Based Access Control (RBAC) RBAC will be implemented to define
different levels of access based on the user's role. Roles will be well-defined and
correspond to specific responsibilities and permissions.
Authentication Strong authentication mechanisms, including two-factor authentication (2FA)
will be in place to ensure that only authorized users can access the imaging registry. An
authorization workflow where user access requests are reviewed and approved by study
personnel will be utilized before access is granted. Study personnel will regularly review
and manage user accounts, ensuring that only active users with legitimate access needs have
accounts in the registry.
Access Granting / Revocation Access rights and permissions to users will be shared based on
roles and responsibilities and the least privilege necessary will be granted for users to
perform their tasks. Revoking access rights will be streamlined if users change roles or no
longer require access. The study team will implement logging mechanisms to record user
activities and access attempts. The study team will review logs to detect and investigate any
suspicious or unauthorized activities.
Upon request, auditors from certain regulatory institutions (i.e., CMS, FDA, etc.) or other
third-party institutions may be granted temporary access to the registry for auditing
purposes.
Incident Reporting Security incidents or breaches related to unauthorized access will be
dealt with promptly to mitigate the impact of security incidents and prevent recurrence.
Withdrawal of Imaging Data The Principal Investigator or IRB has the right to remove and
imaging data for medical, safety, or administrative reasons at any time. Appropriate
procedures will be followed to ensure the safe withdrawal of each image from the study.
Image De-identification.
To ensure the secure and ethical handling of OCT data, a comprehensive image
de-identification process will be implemented. This process aims to systematically remove or
alter identifiable information from each image and its associated metadata while preserving
clinical and research value of the images. The following steps outline the key aspects of
this de-identification process:
Removing direct identifiers
- All direct identifiers used for the purpose of individual identification, such as
subject names, medical record and accession numbers, and dates of birth, will be
thoroughly searched for and removed from each image's pixel data.
- Concurrently, these direct identifiers will also be sought out and removed from the
image metadata, except for the medical record number, which will be irreversibly
transformed via a cryptographic hashing function.
Pixel-level Anonymization
● If required, specific image regions containing identifiable features, such as facial
details or unique markings will undergo either masking or blurring. Such regions lacking
diagnostic features will be masked, while those with diagnostic features will be subject to
blurring.
Quality Control
- Rigorous quality checks will be executed to ensure that the anonymization process does
not compromise the clinical value of the images.
- Trained professionals will review a subset of de-identified images to verify that
critical diagnostic features are preserved accurately.
Encryption
- Both the original and de-identified images will be encrypted to ensure their security
during storage and transmission.
- All data will be stored in a secure environment with controlled access, adhering to
regulatory requirements and industry best practices.
Documentation
- A detailed record of the de-identification process will be maintained, including a
comprehensive account of the steps undertaken, personnel involved, and any challenges
encountered.
- This document serves as an essential audit trail, offering transparency and aiding in
demonstrating compliance with data protection regulations.
