July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
A Probabilistic Forecast of 12 Month Visual Outcomes of nAMD
Author Affiliations & Notes
  • Vuong Nguyen
    Save Sight Institute, Sydney, New South Wales, Australia
  • Kelvin Teo
    Save Sight Institute, Sydney, New South Wales, Australia
  • Judy Simpson
    University of Sydney, Sydney, New South Wales, Australia
  • Vincent Daien
    Gui De Chauliac Hospital, Montpellier, France
  • Daniel Barthelmes
    Save Sight Institute, Sydney, New South Wales, Australia
    University of Zurich, Zurich, Switzerland
  • Mark C Gillies
    Save Sight Institute, Sydney, New South Wales, Australia
  • Richard Walton
    Cancer Institute NSW, Sydney, New South Wales, Australia
  • Footnotes
    Commercial Relationships   Vuong Nguyen, None; Kelvin Teo, None; Judy Simpson, None; Vincent Daien, Bayer (F); Daniel Barthelmes, Bayer (F), Bayer (R), Novartis (F), Novartis (R); Mark Gillies, Allergan (F), Bayer (F), Bayer (S), Novartis (F), Novartis (S), Ophthea (F); Richard Walton, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 829. doi:
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      Vuong Nguyen, Kelvin Teo, Judy Simpson, Vincent Daien, Daniel Barthelmes, Mark C Gillies, Richard Walton; A Probabilistic Forecast of 12 Month Visual Outcomes of nAMD. Invest. Ophthalmol. Vis. Sci. 2018;59(9):829.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To assess the potential of a large registry of patients with neovascular age-related macular degeneration (nAMD) to predict 12 month visual acuity (VA) outcomes and communicate uncertainty in our predictions in terms of probabilities.

Methods : Data were obtained from the Fight Retinal Blindness! registry, currently containing 12 month data for at least 4000 eyes. We used Bayesian regression models and random forest machine learning to predict 12 month VA using baseline characteristics including age, angiographic lesion type and size, and visual acuity. Predictions were expressed in terms of probabilities rather than single point estimates. Predictive performance was evaluated using cross-validation and diagnosed both calibration and sharpness.

Results : An example of a probabilistic prediction from the random forest model is shown in Figures 1 and 2. Communicating the likelihood of achieving certain outcomes can be expressed using the survival function (complementary cumulative distribution function). For example, the patient described in Figure 1 has a 75% chance of not losing further vision and a 46% chance of achieving a VA≥70 letters at 12 months.
Probability integral transform histograms suggested the predictions from the Bayesian regression model were overdispersed, while the random forest predictions were better calibrated. Predictions from random forest were also sharper; the average width of the 50% prediction intervals from cross-validation was 14.5 for random forest and 21.3 for random forest.

Conclusions : Integrating a predictive model into data registries to show likely outcomes for patients at baseline can be a valuable tool for patients and clinicians. Communicating these outcomes via probabilities rather than single point predictions can account for uncertainties and the possibility of negative outcomes. The random forest machine learning algorithm outperformed the Bayesian posterior in our data. Further investigations into machine learning algorithms for predicting 12 month outcomes and updating models with the 3 month VA will be explored.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

 

Figure 1. Predictive probability density from random forest of a 72 year old patient with minimally classic lesion of size 3000µm and baseline VA of 60 letters. This patient went on to achieve 80 letters at 12 months.

Figure 1. Predictive probability density from random forest of a 72 year old patient with minimally classic lesion of size 3000µm and baseline VA of 60 letters. This patient went on to achieve 80 letters at 12 months.

 

Figure 2. Survival function for the patient described in Figure 1.

Figure 2. Survival function for the patient described in Figure 1.

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