Clinical Prediction Performance of Glaucoma Progression Using a 2-Dimensional Continuous-Time Hidden Markov Model with Structural and Functional Measurements

Youngseok Song, Hiroshi Ishikawa, Mengfei Wu, Yu Ying Liu, Katie A. Lucy, Fabio Lavinsky, Mengling Liu, Gadi Wollstein, Joel S. Schuman

    Research output: Contribution to journalArticle

    Abstract

    Purpose: Previously, we introduced a state-based 2-dimensional continuous-time hidden Markov model (2D CT HMM) to model the pattern of detected glaucoma changes using structural and functional information simultaneously. The purpose of this study was to evaluate the detected glaucoma change prediction performance of the model in a real clinical setting using a retrospective longitudinal dataset. Design: Longitudinal, retrospective study. Participants: One hundred thirty-four eyes from 134 participants diagnosed with glaucoma or as glaucoma suspects (average follow-up, 4.4±1.2 years; average number of visits, 7.1±1.8). Methods: A 2D CT HMM model was trained using OCT (Cirrus HD-OCT; Zeiss, Dublin, CA) average circumpapillary retinal nerve fiber layer (cRNFL) thickness and visual field index (VFI) or mean deviation (MD; Humphrey Field Analyzer; Zeiss). The model was trained using a subset of the data (107 of 134 eyes [80%]) including all visits except for the last visit, which was used to test the prediction performance (training set). Additionally, the remaining 27 eyes were used for secondary performance testing as an independent group (validation set). The 2D CT HMM predicts 1 of 4 possible detected state changes based on 1 input state. Main Outcome Measures: Prediction accuracy was assessed as the percentage of correct prediction against the patient's actual recorded state. In addition, deviations of the predicted long-term detected change paths from the actual detected change paths were measured. Results: Baseline mean ± standard deviation age was 61.9±11.4 years, VFI was 90.7±17.4, MD was −3.50±6.04 dB, and cRNFL thickness was 74.9±12.2 μm. The accuracy of detected glaucoma change prediction using the training set was comparable with the validation set (57.0% and 68.0%, respectively). Prediction deviation from the actual detected change path showed stability throughout patient follow-up. Conclusions: The 2D CT HMM demonstrated promising prediction performance in detecting glaucoma change performance in a simulated clinical setting using an independent cohort. The 2D CT HMM allows information from just 1 visit to predict at least 5 subsequent visits with similar performance.

    Original languageEnglish (US)
    Pages (from-to)1354-1361
    Number of pages8
    JournalOphthalmology
    Volume125
    Issue number9
    DOIs
    StatePublished - Sep 2018

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    ASJC Scopus subject areas

    • Ophthalmology

    Cite this

    Song, Y., Ishikawa, H., Wu, M., Liu, Y. Y., Lucy, K. A., Lavinsky, F., Liu, M., Wollstein, G., & Schuman, J. S. (2018). Clinical Prediction Performance of Glaucoma Progression Using a 2-Dimensional Continuous-Time Hidden Markov Model with Structural and Functional Measurements. Ophthalmology, 125(9), 1354-1361. https://doi.org/10.1016/j.ophtha.2018.02.010