Dataset Information

Using CorvisST tonometry to assess glaucoma progression.

ABSTRACT:

Purpose

To investigate the utility of the Corneal Visualization Scheimpflug Technology instrument (CST) to assess the progression of visual field (VF) damage in primary open angle glaucoma patients.

Method

A total of 75 eyes from 111 patients with primary open-angle glaucoma were investigated. All patients underwent at least nine VF measurements with the Humphrey Field Analyzer, CST measurements, axial length (AL), central corneal thickness (CCT) and intraocular pressure (IOP) with Goldmann applanation tonometry (GAT). Mean total deviation (mTD) progression rates of the eight VFs, excluding the first VF, were calculated and the association between progression rate and the other listed measurements was analyzed using linear regression, and the optimal to describe mTD progression rate was selected based on the second order bias corrected Akaike Information Criterion (AICc) index.

Results

VF progression was described best in a model that included CST parameters as well as other ocular measurements. The optimal linear model to describe mTD progression rate was given by the equation: -8.9-0.068 x mean GAT + 0.68 x A1 time + 0.31 x A2 time -0.39 x A2 length- 1.26 x highest deformation amplitude.

Conclusion

CST measurements are useful when assessing VF progression in glaucoma patients. In particular, careful consideration should be given to patients where: (i) an eye is observed to be applanated fast in the first and second applanations, (ii) the applanated area is wide in the second applanation and (iii) the indentation is deep at the maximum deformation, since these eyes appear to be at greater risk of VF progression.

SUBMITTER: Matsuura M

PROVIDER: S-EPMC5417509 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Using CorvisST tonometry to assess glaucoma progression.

Matsuura Masato M Hirasawa Kazunori K Murata Hiroshi H Nakakura Shunsuke S Kiuchi Yoshiaki Y Asaoka Ryo R

PloS one 20170504 5

<h4>Purpose</h4>To investigate the utility of the Corneal Visualization Scheimpflug Technology instrument (CST) to assess the progression of visual field (VF) damage in primary open angle glaucoma patients.<h4>Method</h4>A total of 75 eyes from 111 patients with primary open-angle glaucoma were investigated. All patients underwent at least nine VF measurements with the Humphrey Field Analyzer, CST measurements, axial length (AL), central corneal thickness (CCT) and intraocular pressure (IOP) wit ...[more]

PMID: 28472062

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Project description:ImportanceAccurate determination of intraocular pressure (IOP) is crucial for the diagnosis and management of glaucoma. Objective clinical evaluation of the correction equations for Goldmann applanation tonometry (GAT) is lacking.ObjectivesTo investigate the difference between corrected and conventional GAT and Pascal dynamic contour tonometry (DCT) measurements, as well as the correlation between discordant IOP values and stage of glaucoma.Design, setting, and participantsThis prospective cross-sectional case series was conducted at the Department of Ophthalmology, University Hospital Zurich, and Talacker Eye Center between July 1, 2011, and May 31, 2016, among 112 white patients with glaucoma.InterventionsIntraocular pressure measurements were performed with GAT and DCT in a randomized order. Goldmann applanation tonometry measurements were modified with 5 correction equations.Main outcomes and measuresThe primary end point was degree of concordance between corrected or uncorrected GAT and DCT measurements. The secondary end point was association between discordant IOP measurements and the stage of glaucoma, as assessed by the Glaucoma Severity Score.ResultsAmong the 112 patients (67 women and 45 men; mean [SD] age, 66.3 [13.1] years), 63 of the eyes in the study (56.3%) were left eyes and 85 patients (75.9%) were taking ocular antihypertensive medications. Mean (SD) IOP was 20.3 (4.5) mm Hg (95% CI, 19.4-21.1) as measured by DCT and 17.0 [4.1] mm Hg (95% CI, 16.3-17.8) as measured by GAT. The mean (SD) discordance between DCT and GAT measurements was -3.3 (2.0) mm Hg (95% CI, 2.9-3.6). The 5 corrected GAT values ranged from -2.7 to -5.4 mm Hg compared with DCT. The mean (SD) result of the Dresdner correction formula (17.6 [4.1] mm Hg) was closer to the DCT measurement than the original GAT measurement. The mean (SD) Glaucoma Severity Score was 4.7 (3.4) (95% CI, 4.1-5.4). The uncorrected discordance IOPDCT - IOPGAT showed a positive correlation with the Glaucoma Severity Score (rs = 0.33; P < .001) and a negative correlation with central corneal thickness (rs = -0.22; P = .02).Conclusions and relevanceIn comparison with DCT measurements, these data suggest that GAT values are significantly discordant in eyes with thin corneas and advanced glaucoma. Application of GAT-based correction formulas involves a possible risk of creating an even greater number of unpredictable measurement errors. Hence, we advise with caution, especially pertaining to eyes with thin corneas, to not place reliance on GAT readings, and abandon any correction formula.Trial registrationclinicaltrials.gov Identifier: NCT01474070.

Project description:SIGNIFICANCE:This article shows that self-tonometry can provide robust measures of diurnal intraocular pressure (IOP) and also detect changes to IOP in response to treatment within a short period of monitoring. These advances in IOP monitoring may contribute to improved management of glaucoma patients and suspects. PURPOSE:The aim of this study was to prospectively investigate the utility of rebound self-tonometry performed over several weeks for detecting diurnal IOP fluctuations in glaucoma patients and suspects and also initial response to topical treatment in glaucoma patients. METHODS:Forty patients were recruited following glaucoma-specific examination. Subsequent to successful training with the iCare HOME tonometer, patients were instructed to measure IOP, in a sitting position, four times a day over 4 to 6 weeks. Date, time, laterality, and IOP downloaded from the tonometer and clinical examination data, including applanation IOP and corneal thickness, were analyzed. A user satisfaction survey was also administered at study completion. t Test and analysis of variance were used to compare groups and IOP across days. Pearson correlation was used to compare measurements to Goldmann applanation tonometry and IOP measurements from the first day/s to the overall mean IOP. RESULTS:Twenty-seven patients (18 suspects and 9 glaucoma patients) completed data collection. Patients self-measured IOP on 118 (±29) occasions for 40 (±7.4) days. Two dominant patterns of fluctuation were revealed: peak IOP upon awakening (n = 11) and at midday (n = 13). Diurnal IOP measured in the first 7 days showed strong correlation to diurnal IOP across the entire study period (r = 0.82, P < .0001). Within 24 hours of treatment commencement (latanoprost 0.005% ophthalmic solution), IOP reduced from 23.9 (±5.2) to 16.1 (±2.6) mmHg. Overall, patients rated the instrument as easy to use, although difficulties with correct alignment were expressed. CONCLUSIONS:Rebound self-tonometry demonstrated utility for measuring diurnal IOP fluctuations in most patients, hence enhancing management of patient with or at risk of developing glaucoma.

Project description:PurposeTo develop an objective and automated method for measuring intraocular pressure using deep learning and fixed-force Goldmann applanation tonometry (GAT) techniques.DesignProspective cross-sectional study.ParticipantsPatients from an academic glaucoma practice.MethodsIntraocular pressure was estimated by analyzing videos recorded using a standard slit-lamp microscope and fixed-force GAT. Video frames were labeled to identify the outline of the reference tonometer and the applanation mires. A deep learning model was trained to localize and segment the tonometer and mires. Intraocular pressure values were calculated from the deep learning-predicted tonometer and mire diameters using the Imbert-Fick formula. A separate test set was collected prospectively in which standard and automated GAT measurements were collected in random order by 2 independent masked observers to assess the deep learning model as well as interobserver variability.Main outcome measuresIntraocular pressure measurements between standard and automated methods were compared.ResultsTwo hundred sixty-three eyes of 135 patients were included in the training and validation videos. For the test set, 50 eyes from 25 participants were included. Each eye was measured by 2 observers, resulting in 100 videos. Within the test set, the mean difference between automated and standard GAT results was -0.9 mmHg (95% limits of agreement [LoA], -5.4 to 3.6 mmHg). Mean difference between the 2 observers using standard GAT was 0.09 mmHg (LoA,-3.8 to 4.0 mmHg). Mean difference between the 2 observers using automated GAT videos was -0.3 mmHg (LoA, -4.1 to 3.5 mmHg). The coefficients of repeatability for automated and standard GAT were 3.8 and 3.9 mmHg, respectively. The bias for even-numbered measurements was reduced when using automated GAT.ConclusionsPreliminary measurements using deep learning to automate GAT demonstrate results comparable with those of standard GAT. Automated GAT has the potential to improve on our current GAT measurement standards significantly by reducing bias and improving repeatability. In addition, ocular pulse amplitudes could be observed using this technique.

Project description:BackgroundDeep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts.MethodsWe established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively.ResultsThe AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81-0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83-0.95) and 0.88 (0.79-0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88-0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81-0.92) and 0.88 (0.83-0.94) in external test sets 1 and 2, respectively.ConclusionOur study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression.FUNDINGNational Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC.

Dataset Information

Using CorvisST tonometry to assess glaucoma progression.

Purpose

Method

Results

Conclusion

Publications

Using CorvisST tonometry to assess glaucoma progression.

Similar Datasets

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