Abstract
Purpose
To compare the diagnostic performance of automated imaging for glaucoma.
Design
Prospective, direct comparison study.
Participants
Adults with suspected glaucoma or ocular hypertension referred to hospital eye services in the United Kingdom.
Methods
We evaluated 4 automated imaging test algorithms: the Heidelberg Retinal Tomography (HRT; Heidelberg Engineering, Heidelberg, Germany) glaucoma probability score (GPS), the HRT Moorfields regression analysis (MRA), scanning laser polarimetry (GDx enhanced corneal compensation; Glaucoma Diagnostics (GDx), Carl Zeiss Meditec, Dublin, CA) nerve fiber indicator (NFI), and Spectralis optical coherence tomography (OCT; Heidelberg Engineering) retinal nerve fiber layer (RNFL) classification. We defined abnormal tests as an automated classification of outside normal limits for HRT and OCT or NFI ≥ 56 (GDx). We conducted a sensitivity analysis, using borderline abnormal image classifications. The reference standard was clinical diagnosis by a masked glaucoma expert including standardized clinical assessment and automated perimetry. We analyzed 1 eye per patient (the one with more advanced disease). We also evaluated the performance according to severity and using a combination of 2 technologies.
Main Outcome Measures
Sensitivity and specificity, likelihood ratios, diagnostic, odds ratio, and proportion of indeterminate tests.
Results
We recruited 955 participants, and 943 were included in the analysis. The average age was 60.5 years (standard deviation, 13.8 years); 51.1% were women. Glaucoma was diagnosed in at least 1 eye in 16.8%; 32% of participants had no glaucoma-related findings. The HRT MRA had the highest sensitivity (87.0%; 95% confidence interval [CI], 80.2%–92.1%), but lowest specificity (63.9%; 95% CI, 60.2%–67.4%); GDx had the lowest sensitivity (35.1%; 95% CI, 27.0%–43.8%), but the highest specificity (97.2%; 95% CI, 95.6%–98.3%). The HRT GPS sensitivity was 81.5% (95% CI, 73.9%–87.6%), and specificity was 67.7% (95% CI, 64.2%–71.2%); OCT sensitivity was 76.9% (95% CI, 69.2%–83.4%), and specificity was 78.5% (95% CI, 75.4%–81.4%). Including only eyes with severe glaucoma, sensitivity increased: HRT MRA, HRT GPS, and OCT would miss 5% of eyes, and GDx would miss 21% of eyes. A combination of 2 different tests did not improve the accuracy substantially.
Conclusions
Automated imaging technologies can aid clinicians in diagnosing glaucoma, but may not replace current strategies because they can miss some cases of severe glaucoma.
To compare the diagnostic performance of automated imaging for glaucoma.
Design
Prospective, direct comparison study.
Participants
Adults with suspected glaucoma or ocular hypertension referred to hospital eye services in the United Kingdom.
Methods
We evaluated 4 automated imaging test algorithms: the Heidelberg Retinal Tomography (HRT; Heidelberg Engineering, Heidelberg, Germany) glaucoma probability score (GPS), the HRT Moorfields regression analysis (MRA), scanning laser polarimetry (GDx enhanced corneal compensation; Glaucoma Diagnostics (GDx), Carl Zeiss Meditec, Dublin, CA) nerve fiber indicator (NFI), and Spectralis optical coherence tomography (OCT; Heidelberg Engineering) retinal nerve fiber layer (RNFL) classification. We defined abnormal tests as an automated classification of outside normal limits for HRT and OCT or NFI ≥ 56 (GDx). We conducted a sensitivity analysis, using borderline abnormal image classifications. The reference standard was clinical diagnosis by a masked glaucoma expert including standardized clinical assessment and automated perimetry. We analyzed 1 eye per patient (the one with more advanced disease). We also evaluated the performance according to severity and using a combination of 2 technologies.
Main Outcome Measures
Sensitivity and specificity, likelihood ratios, diagnostic, odds ratio, and proportion of indeterminate tests.
Results
We recruited 955 participants, and 943 were included in the analysis. The average age was 60.5 years (standard deviation, 13.8 years); 51.1% were women. Glaucoma was diagnosed in at least 1 eye in 16.8%; 32% of participants had no glaucoma-related findings. The HRT MRA had the highest sensitivity (87.0%; 95% confidence interval [CI], 80.2%–92.1%), but lowest specificity (63.9%; 95% CI, 60.2%–67.4%); GDx had the lowest sensitivity (35.1%; 95% CI, 27.0%–43.8%), but the highest specificity (97.2%; 95% CI, 95.6%–98.3%). The HRT GPS sensitivity was 81.5% (95% CI, 73.9%–87.6%), and specificity was 67.7% (95% CI, 64.2%–71.2%); OCT sensitivity was 76.9% (95% CI, 69.2%–83.4%), and specificity was 78.5% (95% CI, 75.4%–81.4%). Including only eyes with severe glaucoma, sensitivity increased: HRT MRA, HRT GPS, and OCT would miss 5% of eyes, and GDx would miss 21% of eyes. A combination of 2 different tests did not improve the accuracy substantially.
Conclusions
Automated imaging technologies can aid clinicians in diagnosing glaucoma, but may not replace current strategies because they can miss some cases of severe glaucoma.
| Original language | English |
|---|---|
| Pages (from-to) | 930-938 |
| Number of pages | 9 |
| Journal | Ophthalmology |
| Volume | 123 |
| Issue number | 5 |
| Early online date | 23 Mar 2016 |
| DOIs | |
| Publication status | Published - May 2016 |
Bibliographical note
AcknowledgmentsThe authors thank all the GATE study participants and the staff at each of our recruiting centers for taking part in this study: NHS Grampian, Hinchingbrooke Hospital NHS Trust, Bedford Hospital NHS Trust, Moorfields Eye Hospital NHS Foundation Trust, The Royal Liverpool, and Broadgreen University Hospitals NHS Trust; the members of our independent steering committee: Colm O'Brien (chair), Anthony King, Anja Tuulonen, Russell Young, and David Wright; the staff at the GATE study office; and Gladys McPherson and the programming team based in the Centre for Healthcare Randomised Trials within the Health Services Research Unit, University of Aberdeen.
Financial Support:
Supported by the National Institute for Health Research (NIHR), Health Technology Assessment (HTA) program (grant no.: 09/22/111). The Health Services Research Unit is core funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates. The views and opinions expressed in this article are those of the authors and do not necessarily reflect those of the NIHR HTA program, the NIHR, the National Health Service or the Department of Health, or the funders that provided institutional support for the study.
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Rodolfo Andrés Hernández
- School of Medicine, Medical Sciences & Nutrition, Health Economics Research Unit - Research Fellow
Person: Academic Related - Research
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Craig Ramsay
- School of Medicine, Medical Sciences & Nutrition, Health Services Research Unit (HSRU) - Director of Health Services Research Unit
- Institute of Applied Health Sciences
Person: Academic