| Title |
Correlation between short-term and long-term intraocular pressure fluctuation in glaucoma patients
|
|---|---|
| Published in |
Clinical Ophthalmology, September 2016
|
| DOI | 10.2147/opth.s116859 |
| Pubmed ID | |
| Authors |
Naoki Tojo, Shinya Abe, Mari Miyakoshi, Atsushi Hayashi |
| Abstract |
We investigated correlations between short-term and long-term intraocular pressure (IOP) fluctuations. We examined 50 eyes of glaucoma patients who were followed for >2 years. We measured short-term IOP fluctuation using a Triggerfish(®) contact lens sensor (CLS). The short-term IOP fluctuation (mVeq) was defined as the difference between the maximum value and the minimum value measured during the 24-hour course with CLS. The long-term IOP fluctuation was defined by four parameters: 1) the mean IOP (mmHg) determined during follow-up; 2) the IOP difference, which was defined as the difference between the maximum IOP and the minimum IOP; 3) the standard deviation of IOP; and 4) the peak IOP, which was defined as the maximum IOP. Correlations between these parameters and the short-term IOP fluctuation were examined. The mean follow-up period was 5.4 years. The average IOP was 15.0±4.0 mmHg. The range of short-term IOP fluctuation identified with CLS was significantly correlated with all the four long-term IOP fluctuation parameters. Short-term IOP fluctuations were found to be associated with long-term IOP fluctuations. Examination of 24-hour IOP fluctuations with the CLS might be useful for predicting the long-term IOP fluctuation. |
X Demographics
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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
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 17 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 18% |
| Student > Ph. D. Student | 3 | 18% |
| Student > Doctoral Student | 2 | 12% |
| Student > Postgraduate | 2 | 12% |
| Lecturer > Senior Lecturer | 1 | 6% |
| Other | 3 | 18% |
| Unknown | 3 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 53% |
| Social Sciences | 1 | 6% |
| Computer Science | 1 | 6% |
| Neuroscience | 1 | 6% |
| Engineering | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 4 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 September 2016.
All research outputs
#20,656,161
of 25,373,627 outputs
Outputs from Clinical Ophthalmology
#2,605
of 3,712 outputs
Outputs of similar age
#271,792
of 348,369 outputs
Outputs of similar age from Clinical Ophthalmology
#56
of 87 outputs
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