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Am J Ophthalmol. 2019 Jul 16. pii: S0002-9394(19)30328-9. doi: 10.1016/j.ajo.2019.07.005. [Epub ahead of print]

Machine learning-based predictive modeling of surgical intervention in glaucoma using systemic data from electronic health records.

Author information

1
Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center and Shiley Eye Institute, University of California San Diego, La Jolla, CA; UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA.
2
UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA; Interdisciplinary Research on Substance Use Joint Doctoral Program, University of California San Diego and San Diego State University, San Diego, CA.
3
UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA.
4
UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA; Division of Health Services Research and Development, Veterans Administration San Diego Healthcare System, La Jolla, CA.
5
Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center and Shiley Eye Institute, University of California San Diego, La Jolla, CA. Electronic address: rweinreb@ucsd.edu.

Abstract

PURPOSE:

To predict the need for surgical intervention in patients with primary open-angle glaucoma (POAG) using systemic data in electronic health records (EHR).

DESIGN:

Development and evaluation of machine learning models.

METHODS:

Structured EHR data for 385 POAG patients from a single academic institution were incorporated into models using multivariable logistic regression, random forests, and artificial neural networks. Leave-one-out cross-validation was performed. Mean area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and Youden Index were calculated for each model to evaluate performance. Systemic variables driving predictions were identified and interpreted.

RESULTS:

Multivariable logistic regression was most effective at discriminating patients with progressive disease requiring surgery, with an AUC of 0.67. Higher mean systolic blood pressure was associated with significantly increased odds of needing glaucoma surgery (odds ratio [OR] 1.09, p<0.001). Ophthalmic medications (OR 0.28, P<0.001), non-opioid analgesic medications (OR 0.21, P=0.002), anti-hyperlipidemic medications (OR 0.39, P=0.004), macrolide antibiotics (OR 0.40, P=0.03), and calcium blockers (OR 0.43, P=0.03) were associated with decreased odds of needing glaucoma surgery.

CONCLUSIONS:

Existing systemic data in the EHR has some predictive value in identifying POAG patients at risk of progression to surgical intervention, even in the absence of eye-specific data. Blood pressure-related metrics and certain medication classes emerged as predictors of glaucoma progression. This approach provides an opportunity for future development of automated risk prediction within the EHR based on systemic data to assist with clinical decision-making.

PMID:
31323204
DOI:
10.1016/j.ajo.2019.07.005

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