May 2014
Volume 55, Issue 5
Free
Letters to the Editor  |   May 2014
Additional Considerations in the Utility of Dark Adaptometry for the Diagnosis of Age-Related Macular Degeneration
Author Affiliations
  • Brian L. VanderBeek
    Scheie Eye Institute, Department of Ophthalmology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States; and
    Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States.
Investigative Ophthalmology & Visual Science May 2014, Vol.55, 3148. doi:10.1167/iovs.14-14317
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    • Get Citation

      Brian L. VanderBeek; Additional Considerations in the Utility of Dark Adaptometry for the Diagnosis of Age-Related Macular Degeneration. Invest. Ophthalmol. Vis. Sci. 2014;55(5):3148. doi: 10.1167/iovs.14-14317.

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

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I read with great interest the paper by Jackson et al. 1 describing the sensitivity and specificity of dark adaptometry as a new diagnostic modality for age-related macular degeneration (AMD). Although AdaptDx (Maculogix, Hummelstown, PA, USA) appears to have fantastic research potential, I am concerned that the data as presented do not fully support the authors' assertion that, “Use of dark adaption testing in primary eye care practices would significantly increase the likelihood of diagnosing AMD in affected cases…”. 1 One worry is that the reported values of sensitivity (90.6%) and specificity (90.5%) are artificially high due to underlying factors within the study population. 1 AdaptDx, as pointed out by the authors, performs better at the ends of the Age-Related Eye Disease Study (AREDS) scale. This difference in AMD detection based on disease severity allows spectrum bias into the study, where the over-representation of intermediate and late AMD, coupled with the under-representation of mild AMD (i.e., no AREDS step 2 patients used in the study) influences the reported sensitivity and specificity. Also affecting these values are the stringent study exclusion criteria, which disqualified 31% of the screened patients in the study. 1 Including patients who were previously excluded (as could be expected to happen in a general clinical setting) and using a study population more representative of the total AMD population would likely negatively impact the reported sensitivity and specificity. 
Even if the sensitivity and specificity are accepted as accurate, other issues should be considered before a widespread implementation of AdaptDx. Similar to other diagnostic tests, generating positive (PPV) and negative (NPV) predictive values can be clinically informative and must be examined in the context in which the test is expected to be implemented. Using a recently published prevalence rate of 5% in Americans over 40, but maintaining the test's sensitivity and specificity over a theoretical population of 1000 people, we can model how the test would perform in a general clinical setting. 2 Applying the AdaptDx test to the 50 people with AMD (5% prevalence × 1000 people) in our population would yield 45 (90% sensitivity × 50 people with disease) true-positive and 5 false-negative tests. In the 950 (95% no disease × 1000 people) without disease, 860 (90% specificity × 950 without AMD) would have a true-negative test and 90 would falsely test positive. This means that the anticipated PPV and NPV for the AdaptDx in a general ophthalmologist's office would be closer to 33% and 99%, respectively. Although the NPV is excellent, the PPV shows that for every 3 people who test positive, only 1 actually will have AMD. Even if the “best-case-scenario” sensitivity and specificity are true, the PPV of this test for general use still is undesirable. 
The usefulness of AdaptDx as a daily clinical tool also is limited for a more practical reason than diagnostic accuracy. The technologic advancements that are integrated into routine clinical practice are those that either can replace a portion of the exam (i.e., autorefraction) or offer new information to the clinician not otherwise attainable (i.e., formal visual field testing). Unfortunately, diagnosing AMD with AdaptDx does not fall into either category. As mentioned previously, the rapid (6.5-minute test) AdaptDx performed best on patients at the extremes of the AMD spectrum, no disease, and late AMD. 1 These also are the disease states least likely to be misdiagnosed by a clinician. AdaptDx would be more beneficial if the rapid test could reliably distinguish between early and intermediate AMD, confirming which patients would best be served by AREDS vitamin supplementation. The authors also don't make a direct comparison of AdaptDx to OCT as an ancillary tool to the clinical exam in assessing AMD, making their comparisons to previous diagnostic studies less useful, since OCT now is a routine part of care in many practices. 3,4 Lastly, but most importantly, a clinician is unlikely to forgo a dilated fundus exam at the risk of missing other retinal diseases based solely on the results of an AdaptDx test. 
Daily clinical practice aside, the research value of AdaptDx appears tremendous. Currently, a great need exists for an automated test that can assess and quantify AMD disease stage and progression before the formation of late AMD. The lengthier 20-minute AdaptDx test demonstrated very promising results for differentiating stages of AMD and may prove to be the test's highest utility. 1 As AMD clinical trials focus on late AMD prevention, AdaptDx is a clear candidate to advance these studies. 
References
Jackson GR Scott IU Kim IK Quillen DA Iannaccone A Edwards JG. Diagnostic sensitivity and specificity of dark adaptometry for detection of age-related macular degeneration. Invest Ophthalmol Vis Sci . 2014; 55: 1427–1431. [CrossRef] [PubMed]
VanderBeek BL Talwar N Nan B Musch DC Zacks DN Stein JD. Prevalence and hazard of age-related macular degeneration in different races throughout the United States. Am J Ophthalmol . 2011; 152: 273–282. [CrossRef] [PubMed]
Tikellis G Robman LD Harper A McNeil JJ Taylor HR McCarty CA. Methods for detecting age-related maculopathy: a comparison between photographic and clinical assessment. Clin Experiment Ophthalmol . 2000; 28: 367–372. [CrossRef] [PubMed]
Seddon JM Sharma S Adelman RA. Evaluation of the clinical age-related maculopathy staging system. Ophthalmology . 2006; 113: 260–266. [CrossRef] [PubMed]
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