Purpose: A previously developed machine-learning approach with Kalman-filtering technology accurately predicted disease trajectory for patients with various glaucoma types and severities using clinical trials data. This study assesses performance of the KF approach with real-world data.
Design: Retrospective cohort study.
Methods: We tested the performance of a previously validated KF model (PKF) initially trained using African Descent and Glaucoma Evaluation Study and Diagnostic Innovations in Glaucoma Study data on patients with different types and severities of glaucoma receiving care in the United Kingdom (UK), comparing the predictive accuracy to 2 conventional linear regression (LR) models and a newly-developed KF trained on UK patients (UK-KF).
Results: 3116 patients with open-angle glaucoma or suspects were divided into training (n=1584) and testing (n=1532) sets. The predictive accuracy for MD within 2.5 dB of the observed value at 60 months' follow-up for PKF (75.7%) was substantially better than those for the LR models (P<0.01 for both) and similar to that for UK-KF (75.2%, p=0.70). The proportion of MD predictions in the 95% repeatability intervals at 60 months' follow-up for PKF (67.9%) was higher than those for the LR models (40.2%, 40.9%) and similar to that for UK-KF (71.4%).
Conclusion: This study validates the performance of our previously developed KF model on a real-world, multicenter patient population. Our model substantially outperforms the current clinical standard (LR) and forecasts well for patients with different glaucoma types and severities. This study supports the generalizability of PKF performance and supports prospective study of implementation into clinical practice.
Keywords: Glaucoma; Kalman Filter; Machine Learning; Perimetry.
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