Predictive Analytics for Glaucoma Using Data From the All of Us Research Program

Sally L Baxter¹, Bharanidharan Radha Saseendrakumar², Paulina Paul³, Jihoon Kim³, Luca Bonomi³, Tsung-Ting Kuo³, Roxana Loperena⁴, Francis Ratsimbazafy⁴, Eric Boerwinkle⁵, Mine Cicek⁶, Cheryl R Clark⁷, Elizabeth Cohn⁸, Kelly Gebo⁹, Kelsey Mayo⁴, Stephen Mockrin¹⁰, Sheri D Schully¹¹, Andrea Ramirez¹², Lucila Ohno-Machado¹³; All of Us Research Program Investigators

Affiliations

¹ From the Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, (S.L.B., B.R.S.), La Jolla, California; UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California. Electronic address: s1baxter@health.ucsd.edu.
² From the Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, (S.L.B., B.R.S.), La Jolla, California; UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California.
³ UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California.
⁴ Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee (R.L., F.R.).
⁵ School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas (E.B.).
⁶ Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (M.C.).
⁷ Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts (C.R.C.).
⁸ Hunter-Bellevue School of Nursing, Hunter College City University of New York, New York, New York (E.C.).
⁹ Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, Maryland.
¹⁰ Life Sciences Division, Leidos, Inc, Frederick, (S.M.), Maryland.
¹¹ All of Us Research Program, National Institutes of Health, Bethesda (K.M., S.S.), Bethesda, Maryland.
¹² Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee (A.R.).
¹³ UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California; Division of Health Services Research and Development, Veterans Affairs San Diego Healthcare System, La Jolla, California (L.O.-M.), USA.

PMID: 33497675
PMCID: PMC8184631
DOI: 10.1016/j.ajo.2021.01.008

Multicenter Study

Predictive Analytics for Glaucoma Using Data From the All of Us Research Program

Sally L Baxter et al. Am J Ophthalmol. 2021 Jul.

. 2021 Jul:227:74-86.

doi: 10.1016/j.ajo.2021.01.008. Epub 2021 Jan 23.

Authors

Affiliations

¹ From the Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, (S.L.B., B.R.S.), La Jolla, California; UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California. Electronic address: s1baxter@health.ucsd.edu.
² From the Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, (S.L.B., B.R.S.), La Jolla, California; UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California.
³ UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California.
⁴ Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee (R.L., F.R.).
⁵ School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas (E.B.).
⁶ Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (M.C.).
⁷ Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts (C.R.C.).
⁸ Hunter-Bellevue School of Nursing, Hunter College City University of New York, New York, New York (E.C.).
⁹ Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, Maryland.
¹⁰ Life Sciences Division, Leidos, Inc, Frederick, (S.M.), Maryland.
¹¹ All of Us Research Program, National Institutes of Health, Bethesda (K.M., S.S.), Bethesda, Maryland.
¹² Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee (A.R.).
¹³ UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California; Division of Health Services Research and Development, Veterans Affairs San Diego Healthcare System, La Jolla, California (L.O.-M.), USA.

PMID: 33497675
PMCID: PMC8184631
DOI: 10.1016/j.ajo.2021.01.008

Abstract

Purpose: To (1) use All of Us (AoU) data to validate a previously published single-center model predicting the need for surgery among individuals with glaucoma, (2) train new models using AoU data, and (3) share insights regarding this novel data source for ophthalmic research.

Design: Development and evaluation of machine learning models.

Methods: Electronic health record data were extracted from AoU for 1,231 adults diagnosed with primary open-angle glaucoma. The single-center model was applied to AoU data for external validation. AoU data were then used to train new models for predicting the need for glaucoma surgery using multivariable logistic regression, artificial neural networks, and random forests. Five-fold cross-validation was performed. Model performance was evaluated based on area under the receiver operating characteristic curve (AUC), accuracy, precision, and recall.

Results: The mean (standard deviation) age of the AoU cohort was 69.1 (10.5) years, with 57.3% women and 33.5% black, significantly exceeding representation in the single-center cohort (P = .04 and P < .001, respectively). Of 1,231 participants, 286 (23.2%) needed glaucoma surgery. When applying the single-center model to AoU data, accuracy was 0.69 and AUC was only 0.49. Using AoU data to train new models resulted in superior performance: AUCs ranged from 0.80 (logistic regression) to 0.99 (random forests).

Conclusions: Models trained with national AoU data achieved superior performance compared with using single-center data. Although AoU does not currently include ophthalmic imaging, it offers several strengths over similar big-data sources such as claims data. AoU is a promising new data source for ophthalmic research.

Published by Elsevier Inc.

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Figures

**Figure 1.**
Study workflow and cohort definition for evaluating predictive models for participants with primary open-angle glaucoma in the *All of Us* Research Program.

**Figure 2.**
Composite receiver operating characteristic (ROC) curves for multivariable Logistic Regression (LR) model with area under the curve (AUC) of 0.80, Artificial Neural Network (ANN) model with AUC of 0.93, and Random Forests (RF) model with AUC of 0.99 predicting need for surgery among *All of Us* research participants with glaucoma.

**Figure 3.**
Strengths and limitations of *All of Us* data for ophthalmic research.

See this image and copyright information in PMC

References

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Predictive Analytics for Glaucoma Using Data From the All of Us Research Program

Affiliations

Predictive Analytics for Glaucoma Using Data From the All of Us Research Program

Authors

Affiliations

Abstract

Figures

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources