September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Statistical modeling to predict initial treatment response to Ozurdex for diabetic macular edema
Author Affiliations & Notes
  • Karishma Habbu
    Cole Eye Institute, Cleveland Clinic , Cleveland, Ohio, United States
    Case Western Reserve School of Medicine, Cleveland, Ohio, United States
  • Rishi Singh
    Cole Eye Institute, Cleveland Clinic , Cleveland, Ohio, United States
  • Sunil Srivastava
    Cole Eye Institute, Cleveland Clinic , Cleveland, Ohio, United States
  • Fabiana Silva
    Cole Eye Institute, Cleveland Clinic , Cleveland, Ohio, United States
  • Peter K Kaiser
    Cole Eye Institute, Cleveland Clinic , Cleveland, Ohio, United States
  • Amy Babiuch
    Cole Eye Institute, Cleveland Clinic , Cleveland, Ohio, United States
  • Cal Al-Dhubaib
    Triple Analytics, Cleveland, Ohio, United States
  • Footnotes
    Commercial Relationships   Karishma Habbu, None; Rishi Singh, Regeneron, Alcon, Genentech, Zeiss, Shire (C), Zeiss, Regeneron, Genentech, Alcon (F); Sunil Srivastava, None; Fabiana Silva, None; Peter Kaiser, None; Amy Babiuch, None; Cal Al-Dhubaib, None
  • Footnotes
    Support  Research trial grant from Regeneron Inc. and an unrestricted grant from Research to Prevent Blindness
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 3241. doi:
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      Karishma Habbu, Rishi Singh, Sunil Srivastava, Fabiana Silva, Peter K Kaiser, Amy Babiuch, Cal Al-Dhubaib; Statistical modeling to predict initial treatment response to Ozurdex for diabetic macular edema. Invest. Ophthalmol. Vis. Sci. 2016;57(12):3241.

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

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Abstract

Purpose : Dexamethasone 0.7 mg intravitreal implants have been shown to improve functional and anatomic outcomes in diabetic macular edema (DME). This retrospective, observational clinical study aims to create a statistical model to predict a patient’s initial response to Ozurdex for DME based on his/her baseline characteristics.

Methods : Data from 38 eyes treated with Ozurdex for DME at a single institution was used for this analysis. Baseline demographics and anatomic and visual outcomes at 1,3, and 6 months were collected. Change in central subfield thickness (CST) and best corrected visual acuity (BCVA) from baseline to month 3 were used to stratify patients as “responders” versus “non-responders” to therapy using an adaption of Harvard’s high dimension propensity score algorithm. Forty patient variables including presence of cystoid macular edema, prior vitrectomy, and use of insulin were assigned an “impact score” (IS) corresponding to the strength of that variable’s association with the responder versus non-responder group (an IS >1 and <1 indicated a positive versus negative response to treatment, respectively); variables with a statistically significant IS were isolated. Finally, a simple equation was derived to combine statistically significant ISs of a patient’s anatomic, metabolic and clinical variables for a final IS which could predict a poor, limited, or favorable response to Ozurdex therapy for persistent DME.

Results : Ten of 40 tested variables had statistically significant ISs indicating a meaningful impact on treatment response to Ozurdex within 3 months. Specifically, CST >500 μm, cube volume >13 mm3, cube average thickness > 350 μm, and a BMI <30 were all associated with a favorable response to therapy based on reductions in CST (p< .05). Previous treatment with panretinal photocoagulation and focal macular laser had a positive and negative impact respectively on change in BCVA compared to baseline (p< .05). A validation test comparing patients’ ISs to change in CST from baseline showed a sensitivity of 82% and specificity of 72% for predicting responders and non-responders using an IS threshold of 1.

Conclusions : Statistical modeling for patients receiving a dexamethasone implant could be used to prognosticate outcomes with a high specificity and sensitivity and may help guide treatment choices for these patients

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

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