Article Text

Download PDFPDF
Area under the dark adaptation curve as a reliable alternate measure of dark adaptation response
  1. Shrinivas Pundlik1,2,
  2. Archana Nigalye2,3,
  3. Inês Laíns2,3,
  4. Kevin M Mendez2,3,4,
  5. Raviv Katz2,3,
  6. Janice Kim2,3,
  7. Ivana K Kim2,3,
  8. John B Miller2,3,
  9. Demetrios Vavvas2,3,
  10. Joan W Miller2,3,
  11. Gang Luo1,2,
  12. Deeba Husain2,3
  1. 1Schepens Eye Research Institute of Massachusetts Eye and Ear, Boston, Massachusetts, USA
  2. 2Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts, USA
  3. 3Retina Service, Massachusetts Eye & Ear Infirmary, Boston, Massachusetts, USA
  4. 4Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
  1. Correspondence to Dr Shrinivas Pundlik, Schepens Eye Research Institute of Massachusetts Eye and Ear, Boston, Massachusetts, USA; shrinivas_pundlik{at}meei.harvard.edu

Abstract

Purpose Quantification of dark adaptation (DA) response using the conventional rod intercept time (RIT) requires very long testing time and may not be measurable in the presence of impairments due to diseases such as age-related macular degeneration (AMD). The goal of this study was to investigate the advantages of using area under the DA curve (AUDAC) as an alternative to the conventional parameters to quantify DA response.

Methods Data on 136 eyes (AMD: 98, normal controls: 38) from an ongoing longitudinal study on AMD were used. DA was measured using the AdaptDx 20 min protocol. AUDAC was computed from the raw DA characteristic curve at different time points, including 6.5 min and 20 min (default). The presence of AMD in the given eye was predicted using a logistic regression model within the leave-one-out cross-validation framework, with DA response as the predictor while adjusting for age and gender. The DA response variable was either the AUDAC values computed at 6.5 min (AUDAC6.5) or at 20 min (AUDAC20) cut-off, or the conventional RIT.

Results AUDAC6.5 was strongly correlated with AUDAC20 (β=86, p<0.001, R2=0.87). The accuracy of predicting the presence of AMD using AUDAC20 was 76%, compared with 79% when using RIT, the current gold standard. In addition, when limiting AUDAC calculation to 6.5 min cut-off, the predictive accuracy of AUDAC6.5 was 80%.

Conclusions AUDAC can be a valuable measure to quantify the overall DA response and can potentially facilitate shorter testing duration while maintaining diagnostic accuracy.

  • macula
  • vision
  • visual perception
  • diagnostic tests/investigation

Data availability statement

Data sharing is not applicable as no data sets are generated and/or analysed for this study.

Statistics from Altmetric.com

Data availability statement

Data sharing is not applicable as no data sets are generated and/or analysed for this study.

View Full Text

Footnotes

  • GL and DH contributed equally.

  • Contributors SP was involved in the conception or design of the work, performed data analysis and interpretation, drafted the article and critically revised the article. AN was involved in data collection and critically revised the article. IL was involved in the conception or design of the work, data collection and critically revised the article. KMM performed data analysis and interpretation, and critically revised the article. RK was involved in data collection and critically revised the article. JK was involved in data collection and critically revised the article. IKK was involved in the conception or design of the work and critically revised the article. JBM was involved in the conception or design of the work and critically revised the article. DGV was involved in the conception or design of the work and critically revised the article. JWM was involved in the conception or design of the work and critically revised the article. GL was involved in the conception or design of the work, performed data analysis and interpretation, and critically revised the article. DH was involved in the conception or design of the work and critically revised the article.

  • Funding This work was funded in part by NIH grant EY029847, the Miller Retina Research Fund (Mass. Eye and Ear) (Award No.: NA), the Champalimaud Vision Award (Award No.: NA), the unrestricted departmental Grant from Research to Prevent Blindness, New York (Award No.: NA) and the Commonwealth Unrestricted Grant for Eye Research (Award No.: NA).

  • Competing interests SP and GL have a financial interest in EyeNexo, a company developing smartphone applications for eye measurement. Their interests were reviewed and are managed by Schepens Eye Research Institute and Partners HealthCare in accordance with their conflict of interest policies.

  • Provenance and peer review Not commissioned; externally peer reviewed.

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.