| Title |
Short wave–automated perimetry (SWAP) versus optical coherence tomography in early detection of glaucoma
|
|---|---|
| Published in |
Clinical Ophthalmology, September 2016
|
| DOI | 10.2147/opth.s116073 |
| Pubmed ID | |
| Authors |
Adel Galal Zaky, Ahmed Tarek Yassin, Saber Hamed El Sayid |
| Abstract |
To assess the role and diagnostic effectiveness of optical coherence tomography (OCT) and short wave-automated perimetry (SWAP) to distinguish between normal, glaucoma suspects, and surely diagnosed glaucomatous eye. Changes in the optic disc and retinal nerve fiber layer (RNFL) often precede the appearance of visual field defect with standard automated perimetry. Unfortunately, RNFL defect can be difficult to identify during clinical examination. Early detection of glaucoma is still controversial, whether by OCT, SWAP, or frequency-doubling technology perimetry. In this randomized controlled, consecutive, prospective study, a total 70 subjects (140 eyes) were included in the study, divided into three groups: Group A, 10 healthy volunteers (20 eyes); Group B, 30 patients (60 eyes) with glaucoma suspect; and Group C, 30 patients (60 eyes) with already diagnosed glaucomatous eyes. Average RNFL thickness was 75±9.0 in the glaucoma group, 99±15.5 in the control group, and 94±12 in glaucoma suspect. The inferior quadrant was the early parameter affected. There was significant correlation between visual field parameters and RNFL thickness in both glaucoma and glaucoma suspect groups. Both RNFL thickness measured by OCT and SWAP indices are good discrimination tools between glaucomatous, glaucoma suspect, and normal eyes. OCT parameters tend to be more sensitive than SWAP parameters. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 13 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 3 | 23% |
| Researcher | 2 | 15% |
| Other | 1 | 8% |
| Lecturer > Senior Lecturer | 1 | 8% |
| Student > Postgraduate | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 5 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 4 | 31% |
| Nursing and Health Professions | 2 | 15% |
| Economics, Econometrics and Finance | 1 | 8% |
| Social Sciences | 1 | 8% |
| Neuroscience | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 4 | 31% |
Attention Score in Context
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#20,656,161
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Outputs from Clinical Ophthalmology
#2,605
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#271,795
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Outputs of similar age from Clinical Ophthalmology
#56
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