Preventing Glaucoma Vision Loss with ‘Big Data’

Posted on September 16th, 2021 by Dr. Francis Collins

Each morning, more than 2 million Americans start their rise-and-shine routine by remembering to take their eye drops. The drops treat their open-angle glaucoma, the most-common form of the disease, caused by obstructed drainage of fluid where the eye’s cornea and iris meet. The slow drainage increases fluid pressure at the front of the eye. Meanwhile, at the back of the eye, fluid pushes on the optic nerve, causing its bundled fibers to fray and leading to gradual loss of side vision.

For many, the eye drops help to lower intraocular pressure and prevent vision loss. But for others, the drops aren’t sufficient and their intraocular pressure remains high. Such people will need next-level care, possibly including eye surgery, to reopen the clogged drainage ducts and slow this disease that disproportionately affects older adults and African Americans over age 40.

Sally Baxter, a physician-scientist with expertise in ophthalmology at the University of California, San Diego (UCSD), wants to learn how to predict who is at greatest risk for serious vision loss from open-angle and other forms of glaucoma. That way, they can receive more aggressive early care to protect their vision from this second-leading cause of blindness in the U.S..

To pursue this challenging research goal, Baxter has received a 2020 NIH Director’s Early Independence Award. Her research will build on the clinical observation that people with glaucoma frequently battle other chronic health problems, such as high blood pressure, diabetes, and heart disease. To learn more about how these and other chronic health conditions might influence glaucoma outcomes, Baxter has begun mining a rich source of data: electronic health records (EHRs).

In an earlier study of patients at UCSD, Baxter showed that EHR data helped to predict which people would need glaucoma surgery within the next six months [1]. The finding suggested that the EHR, especially information on a patient’s blood pressure and medications, could predict the risk for worsening glaucoma.

In her NIH-supported work, she’s already extended this earlier “Big Data” finding by analyzing data from more than 1,200 people with glaucoma who participate in NIH’s All of Us Research Program [2]. With consent from the participants, Baxter used their EHRs to train a computer to find telltale patterns within the data and then predict with 80 to 99 percent accuracy who would later require eye surgery.

The findings confirm that machine learning approaches and EHR data can indeed help in managing people with glaucoma. That’s true even when the EHR data don’t contain any information specific to a person’s eye health.

In fact, the work of Baxter and other groups have pointed to an especially important role for blood pressure in shaping glaucoma outcomes. Hoping to explore this lead further with the support of her Early Independence Award, Baxter also will enroll patients in a study to test whether blood-pressure monitoring smart watches can add important predictive information on glaucoma progression. By combining round-the-clock blood pressure data with EHR data, she hopes to predict glaucoma progression with even greater precision. She’s also exploring innovative ways to track whether people with glaucoma use their eye drops as prescribed, which is another important predictor of the risk of irreversible vision loss [3].

Glaucoma research continues to undergo great progress. This progress ranges from basic research to the development of new treatments and high-resolution imaging technologies to improve diagnostics. But Baxter’s quest to develop practical clinical tools hold great promise, too, and hopefully will help one day to protect the vision of millions of people with glaucoma around the world.

References:

[1] Machine learning-based predictive modeling of surgical intervention in glaucoma using systemic data from electronic health records. Baxter SL, Marks C, Kuo TT, Ohno-Machado L, Weinreb RN. Am J Ophthalmol. 2019 Dec; 208:30-40.

[2] Predictive analytics for glaucoma using data from the All of Us Research Program. Baxter SL, Saseendrakumar BR, Paul P, Kim J, Bonomi L, Kuo TT, Loperena R, Ratsimbazafy F, Boerwinkle E, Cicek M, Clark CR, Cohn E, Gebo K, Mayo K, Mockrin S, Schully SD, Ramirez A, Ohno-Machado L; All of Us Research Program Investigators. Am J Ophthalmol. 2021 Jul;227:74-86.

[3] Smart electronic eyedrop bottle for unobtrusive monitoring of glaucoma medication adherence. Aguilar-Rivera M, Erudaitius DT, Wu VM, Tantiongloc JC, Kang DY, Coleman TP, Baxter SL, Weinreb RN. Sensors (Basel). 2020 Apr 30;20(9):2570.

Links:

Glaucoma (National Eye Institute/NIH)

All of Us Research Program (NIH)

Video: Sally Baxter (All of Us Research Program)

Sally Baxter (University of California San Diego)

Baxter Project Information (NIH RePORTER)

NIH Director’s Early Independence Award (Common Fund)

NIH Support: Common Fund