Machine learning can predict anti-VEGF treatment demand in a Treat-and-Extend regimen for patients with nAMD, DME and RVO associated ME

Gallardo, Mathias; Munk, Marion R.; Kurmann, Thomas Kevin; De Zanet, Sandro; Mosinska, Agata; Karagoz, Isıl Kutlutürk; Zinkernagel, Martin S.; Wolf, Sebastian; Sznitman, Raphael (2021). Machine learning can predict anti-VEGF treatment demand in a Treat-and-Extend regimen for patients with nAMD, DME and RVO associated ME. Ophthalmology retina, 5(7), pp. 604-624. Elsevier 10.1016/j.oret.2021.05.002

Text 2021_Gallardo_etal_ORET_TreatmentDemandPrediction.pdf - Accepted Version Restricted to registered users only Available under License Publisher holds Copyright. Download (9MB) | Request a copy

Purpose: To assess the potential of machine learning to predict low and high treatment demand in
real life in patients with nAMD, RVO and DME treated according to a TER.

Design: Retrospective cohort study.

Participants: 377 eyes (340 patients) with nAMD, 333 eyes (285 patients) with RVO or DME, treated
with anti-VEGF according to a predefined TER protocol during 2014-2018.

Methods: Eyes were grouped by disease into low, moderate and high treatment demanders, defined
by the average treatment interval (low: ≥10 weeks, high: ≤5 weeks, moderate: remaining eyes). Two
Random Forest models were trained to predict the probability of the long-term treatment demand
of a new patient. Both models use morphological features automatically extracted from the OCT
volumes at baseline and after two consecutive visits, as well as patient demographic information.
Evaluation of the models included a 10 cross-validation ensuring that no patient was present in both
training (nAMD: ~339, RVO & DME: ~300) and test sets (nAMD: ~38, RVO & DME: ~33).

Main Outcome Measures: Mean Area under the Curve (AuC) of both models, contribution to the
prediction and statistical significance of the input features.

Results: Based on the first three visits, it was possible to predict a low and a high treatment
demander in nAMD and RVO & DME eyes with similar accuracy. Prediction performance for low and
high treatment demand showed similar performance levels across nAMD and RVO & DME eyes. For
nAMD, 127 low, 42 high- and 208 moderate demanders were identified, and for RVO & DME 61 low-,
50 high- and 222 moderate demanders. The nAMD trained models yielded mean AuCs of 0.79 and
0.79 over the 10 folds for low and high demanders, respectively. Models on RVO & DME showed
similar results with a mean AuC of 0.76 and 0.78 for low and high demanders, respectively. Even
more importantly, this study reveals that it is possible to predict reasonably well low demands at the
first visit, before the first injection.

Conclusions: Machine learning classifiers can predict treatment demand and may assist in
establishing patient specific treatment plans in the near future.

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