Improved Prediction of Perimetric Loss in Glaucomatous Eyes Using Latent Class Mixed Modeling

Swarup S. Swaminathan, Alessandro A. Jammal, J. Sunil Rao, Felipe A. Medeiros

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: To evaluate whether the identification of distinct classes within a population of glaucoma patients improves estimates of future perimetric loss. Design: Longitudinal cohort study. Participants: A total of 6558 eyes of 3981 subjects from the Duke Ophthalmic Registry with ≥ 5 reliable standard automated perimetry (SAP) tests and ≥ 2 years of follow-up. Methods: Standard automated perimetry mean deviation (MD) values were extracted with associated timepoints. Latent class mixed models (LCMMs) were used to identify distinct subgroups (classes) of eyes according to rates of perimetric change over time. Rates for individual eyes were then estimated by considering both individual eye data and the most probable class membership for that eye. Data were split into training (80%) and test sets (20%), and test set mean squared prediction errors (MSPEs) were estimated using LCMM and ordinary least squares (OLS) regression. Main Outcome Measures: Rates of change in SAP MD in each class and MSPE. Results: The dataset contained 52 900 SAP tests with an average of 8.1 ± 3.7 tests per eye. The best-fitting LCMM contained 5 classes with rates of −0.06, −0.21, −0.87, −2.15, and +1.28dB/year (80.0%, 10.2%, 7.5%, 1.3%, and 1.0% of the population, respectively) labeled as slow, moderate, fast, catastrophic progressors, and “improvers” respectively. Fast and catastrophic progressors were older (64.1 ± 13.7 and 63.5 ± 16.9 vs. 57.8 ± 15.8, P < 0.001) and had generally mild-moderate disease at baseline (65.7% and 71% vs. 52%, P < 0.001) than slow progressors. The MSPE was significantly lower for LCMM compared to OLS, regardless of the number of tests used to obtain the rate of change (5.1 ± 0.6 vs. 60.2 ± 37.9, 4.9 ± 0.5 vs. 13.4 ± 3.2, 5.6 ± 0.8 vs. 8.1 ± 1.1, 3.4 ± 0.3 vs. 5.5 ± 1.1 when predicting the fourth, fifth, sixth, and seventh visual fields (VFs) respectively; P < 0.001 for all comparisons). MSPE of fast and catastrophic progressors was significantly lower with LCMM versus OLS (17.7 ± 6.9 vs. 48.1 ± 19.7, 27.1 ± 8.4 vs. 81.3 ± 27.1, 49.0 ± 14.7 vs. 183.9 ± 55.2, 46.6 ± 16.0 vs. 232.4 ± 78.0 when predicting the fourth, fifth, sixth, and seventh VFs respectively; P < 0.001 for all comparisons). Conclusions: Latent class mixed model successfully identified distinct classes of progressors within a large glaucoma population that seemed to reflect subgroups observed in clinical practice. Latent class mixed models were superior to OLS regression in predicting future VF observations. Financial Disclosure(s): Proprietary or commercial disclosuremay be found after the references.

Original languageEnglish (US)
Pages (from-to)642-650
Number of pages9
JournalOphthalmology Glaucoma
Volume6
Issue number6
DOIs
StatePublished - Nov 1 2023

Bibliographical note

Publisher Copyright:
© 2023 American Academy of Ophthalmology

Keywords

  • Glaucoma
  • Latent class mixed modeling
  • Linear mixed models
  • Standard automated perimetry
  • Visual field

PubMed: MeSH publication types

  • Journal Article

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