Investigative Ophthalmology & Visual Science Cover Image for Volume 64, Issue 8
June 2023
Volume 64, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2023
Predictors of visual response after lapse in anti-VEGF treatment among patients with diabetic retinopathy
Author Affiliations & Notes
  • Meghana Chalasani
    College of Medicine, Northeast Ohio Medical University, Rootstown, Ohio, United States
  • Christopher Maatouk
    Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
  • Rishi P Singh
    Center for Ophthalmic Bioinformatics, Cleveland Clinic Cole Eye Institute, Cleveland, Ohio, United States
    Cleveland Clinic Martin Health, Stuart, Florida, United States
  • Katherine Talcott
    Center for Ophthalmic Bioinformatics, Cleveland Clinic Cole Eye Institute, Cleveland, Ohio, United States
  • Footnotes
    Commercial Relationships   Meghana Chalasani None; Christopher Maatouk None; Rishi Singh Genentech/Roche, Alcon, Novartis, Regeneron, Asclepix, Gyroscope, Bausch and Lomb, Apellis, Code I (Personal Financial Interest); Katherine Talcott Zeiss, Regenxbio, Code F (Financial Support), Genentech/Roche, Code I (Personal Financial Interest)
  • Footnotes
    Support  P30EY025585(BA-A), Research to Prevent Blindness (RPD) Challenge Grant, Cleveland Eye Bank Foundation Grant
Investigative Ophthalmology & Visual Science June 2023, Vol.64, 2696. doi:
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    • Get Citation

      Meghana Chalasani, Christopher Maatouk, Rishi P Singh, Katherine Talcott; Predictors of visual response after lapse in anti-VEGF treatment among patients with diabetic retinopathy. Invest. Ophthalmol. Vis. Sci. 2023;64(8):2696.

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

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Abstract

Purpose : While first line treatment for diabetic macular edema (DME) is anti-vascular endothelial growth factor (VEGF) therapy, many patients experience lapses in treatment. Less is known about the visual outcomes following lapses in treatment. The purpose of this study is to identify baseline factors that predict visual response after a treatment lapse of anti-VEGF for eyes with DME.

Methods : This is a retrospective analysis of patients with DME who underwent a lapse of anti-VEGF treatment for at least three months. 262 patients were separated into groups of “stable vision” (n=201) and “vision loss” (n=61), defined as loss of at least 10 letters on ETDRS testing. The two groups were compared across several baseline past medical and ophthalmic history factors to identify those associated with vision loss. Step-wise backward logistic regression was used to create a simplified prediction algorithm and identify factors associated with higher odds of losing vision.

Results : The average lapse length was greater in the “vision loss” group than in the “stable vision group” (9.2±9.5 vs 5.8±3.4 months, p<0.001). The groups also differed significantly in their distribution across insurance types (p<0.05) (Table 1). Age, insurance type, history of diabetic foot disease, diabetic medications, lapse length, time since diabetic retinopathy (DR) diagnosis, and total injections prior to lapse were included in the simplified regression model. The use of diabetic medications and time since DR diagnosis significantly reduced the odds of vision loss, while lapse length, total injections, and foot disease significantly increased the odds of losing vision (p<0.05) (Table 1). The final model had a sensitivity of 13% and specificity of 87%, with 65.3% area under the curve (Table 2).

Conclusions : Patients with history of diabetic foot disease, more injections, and longer lapses in treatment are at higher risk for vision loss after a lapse in treatment for DME. Providers should consider these factors when creating a collaborative treatment and follow-up plan.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

 

Table 1: Baseline differences between “Vision Loss” and “Stable Vision” groups among predictors included in simplified regression model. (*Significant with α=0.05; **Significant with α=0.1)

Table 1: Baseline differences between “Vision Loss” and “Stable Vision” groups among predictors included in simplified regression model. (*Significant with α=0.05; **Significant with α=0.1)

 

Table 2: Confusion matrix of prediction algorithm on testing dataset.

Table 2: Confusion matrix of prediction algorithm on testing dataset.

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