Developing a Nationwide Registry to Track Longitudinal Clinical Outcomes of Corneal Surgery and Disease

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.