TY - JOUR
T1 - Predictive Analytics for Glaucoma Using Data From the All of Us Research Program
AU - All of Us Research Program Investigators
AU - Baxter, Sally L.
AU - Saseendrakumar, Bharanidharan Radha
AU - Paul, Paulina
AU - Kim, Jihoon
AU - Bonomi, Luca
AU - Kuo, Tsung Ting
AU - Loperena, Roxana
AU - Ratsimbazafy, Francis
AU - Boerwinkle, Eric
AU - Cicek, Mine
AU - Clark, Cheryl R.
AU - Cohn, Elizabeth
AU - Gebo, Kelly
AU - Mayo, Kelsey
AU - Mockrin, Stephen
AU - Schully, Sheri D.
AU - Ramirez, Andrea
AU - Ohno-Machado, Lucila
N1 - Funding Information:
All authors have completed and submitted the ICMJE form for disclosure of potential conflicts of interest. This work was supported by the All of Us Research Program Investigators ( Appendix 1 ). Funding/Support: This study was supported by National Institutes of Health grants T15LM011271, 1DP5OD029610, OT2OD026552, and an unrestricted departmental grant from Research to Prevent Blindness. The All of Us Research Program is supported (or funded) by grants through the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. In addition to the funded partners, the All of Us Research Program would not be possible without the contributions made by its participants. Financial Disclosures: The authors indicate no financial support or conflicts of interest. All authors attest that they meet the current ICMJE criteria for authorship. Dr. Gebo initiated this work while the Chief Medical and Scientific Officer of the All of Us Research Program.
Publisher Copyright:
© 2021
PY - 2021/7
Y1 - 2021/7
N2 - 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.
AB - 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.
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U2 - 10.1016/j.ajo.2021.01.008
DO - 10.1016/j.ajo.2021.01.008
M3 - Article
C2 - 33497675
AN - SCOPUS:85104476361
VL - 227
SP - 74
EP - 86
JO - American Journal of Ophthalmology
JF - American Journal of Ophthalmology
SN - 0002-9394
ER -