Demographic and Clinical Factors that Influence the Visual Response to Anti-Vascular Endothelial Growth Factor Therapy in Patients with Neovascular Age-Related Macular Degeneration: A Systematic Review

Impact of Routinely Performed Optical Coherence Tomography Examinations on Quality of Life in Patients with Retinal Diseases-Results from the ALBATROS Data Collection

The use of OCT to monitor intravitreal treatment varies in clinical practice and is not always mandatory. The ALBATROS data collection aimed to clarify the impact of routinely implemented OCT on clinical outcomes and its impact on vision-related quality of life (VRQoL).

METHODS

An observational cohort study included patients with retinal diseases starting an intravitreal anti-vascular endothelial growth factor treatment in Germany. Treatment followed clinical practice except mandatory OCT examination during the 12-month observation period. VRQoL was assessed by NEI VFQ-25 and compared with respect to OCT examinations and number of intravitreal injections in the different diseases (nAMD, DME, BRVO, CRVO).

RESULTS

1478 patients (74.5 ± 10.9 years, 54.9% female) were included in the analysis. Patients had neovascular AMD (65.2%), DME (18.4%), BRVO (9.5%), or CRVO (6.9%). 8.8 ± 2.6 OCT examinations and 6.1 ± 3.2 intravitreal injections were performed within 12 months. VRQoL differed between indications at baseline, with substantially lower values for neovascular AMD and CRVO. After twelve months, an increase in visual acuity and visual functional scale was observed for nAMD, DME, and BRVO, while in DME only, there was an association between number of OCT examinations and VRQoL.

CONCLUSION

Intravitreal treatment was able to maintain VRQoL over twelve months in a real-world setting. Regular OCT examinations were associated with higher gain in VRQoL in DME patients after 12 months.

Evaluating an automated machine learning model that predicts visual acuity outcomes in patients with neovascular age-related macular degeneration

Purpose

Neovascular age-related macular degeneration (nAMD) is a major global cause of blindness. Whilst anti-vascular endothelial growth factor (anti-VEGF) treatment is effective, response varies considerably between individuals. Thus, patients face substantial uncertainty regarding their future ability to perform daily tasks. In this study, we evaluate the performance of an automated machine learning (AutoML) model which predicts visual acuity (VA) outcomes in patients receiving treatment for nAMD, in comparison to a manually coded model built using the same dataset. Furthermore, we evaluate model performance across ethnic groups and analyse how the models reach their predictions.

Methods

Binary classification models were trained to predict whether patients’ VA would be ‘Above’ or ‘Below’ a score of 70 one year after initiating treatment, measured using the Early Treatment Diabetic Retinopathy Study (ETDRS) chart. The AutoML model was built using the Google Cloud Platform, whilst the bespoke model was trained using an XGBoost framework. Models were compared and analysed using the What-if Tool (WIT), a novel model-agnostic interpretability tool.

Results

Our study included 1631 eyes from patients attending Moorfields Eye Hospital. The AutoML model (area under the curve [AUC], 0.849) achieved a highly similar performance to the XGBoost model (AUC, 0.847). Using the WIT, we found that the models over-predicted negative outcomes in Asian patients and performed worse in those with an ethnic category of Other. Baseline VA, age and ethnicity were the most important determinants of model predictions. Partial dependence plot analysis revealed a sigmoidal relationship between baseline VA and the probability of an outcome of ‘Above’.

Conclusion

We have described and validated an AutoML-WIT pipeline which enables clinicians with minimal coding skills to match the performance of a state-of-the-art algorithm and obtain explainable predictions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00417-021-05544-y.

Artificial intelligence-based predictions in neovascular age-related macular degeneration

PURPOSE OF REVIEW

Predicting treatment response and optimizing treatment regimen in patients with neovascular age-related macular degeneration (nAMD) remains challenging. Artificial intelligence-based tools have the potential to increase confidence in clinical development of new therapeutics, facilitate individual prognostic predictions, and ultimately inform treatment decisions in clinical practice.

RECENT FINDINGS

To date, most advances in applying artificial intelligence to nAMD have focused on facilitating image analysis, particularly for automated segmentation, extraction, and quantification of imaging-based features from optical coherence tomography (OCT) images. No studies in our literature search evaluated whether artificial intelligence could predict the treatment regimen required for an optimal visual response for an individual patient. Challenges identified for developing artificial intelligence-based models for nAMD include the limited number of large datasets with high-quality OCT data, limiting the patient populations included in model development; lack of counterfactual data to inform how individual patients may have fared with an alternative treatment strategy; and absence of OCT data standards, impairing the development of models usable across devices.

SUMMARY

Artificial intelligence has the potential to enable powerful prognostic tools for a complex nAMD treatment landscape; however, additional work remains before these tools are applicable to informing treatment decisions for nAMD in clinical practice.

Predictors of Visual Acuity After Treatment of Neovascular Age-Related Macular Degeneration - Current Perspectives

Visual acuity is a key outcome measure in the treatment of neovascular age-related macular degeneration (nAMD) using anti-vascular endothelial growth factor agents. Large variations in visual responses between individuals within clinical trials and real-world studies may relate to underlying differences in patient and treatment factors. Most notably, a better baseline visual acuity, younger age and smaller choroidal neovascularization lesion size have been strongly associated with achieving better visual outcomes. In addition, there is emerging evidence for other roles including genetic factors and anatomical variables such as fluid status. Apart from patient-related factors, treatments that favor a higher number of injections tend to provide better visual outcomes. Overall, the identification of predictive factors does not currently play an essential role in the clinical management of patients with nAMD. However, they have allowed for the understanding that early detection, timely management and close monitoring of the disease are required to achieve optimal visual outcomes. Further investigation into predictive factors alongside the development of novel therapeutic agents may one day provide a means to accurately predict patient outcomes. Treatment regimens that offer flexible dosing patterns such as the treat-and-extend strategy currently provide a degree of personalization during treatment.