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Item Clinicians’ Use of Quantitative Information when Assessing the Rate of Functional Progression in Glaucoma(Elsevier, 2022) Gardiner, Stuart K.; Kinast, Robert M.; De Moraes, Carlos Gustavo; Budenz, Donald L.; Jeoung, Jin Wook; Lind, John T.; Myers, Jonathan S.; Nouri-Mahdavi, Kouros; Rhodes, Lindsay A.; Strouthidis, Nicholas G.; Chen, Teresa C.; Mansberger, Steven L.; Ophthalmology, School of MedicinePurpose: Clinicians use both global and point-wise information from visual fields to assess the rate of glaucomatous functional progression. We asked which objective, quantitative measures best correlated with subjective assessment by glaucoma experts. In particular, we aimed to determine how much that judgment was based on localized rates of change vs. on global indices reported by the perimeter. Design: Prospective cohort study. Participants: Eleven academic, expert glaucoma specialists independently scored the rate of functional progression, from 1 (improvement) to 7 (very rapid progression), for a series of 5 biannual clinical printouts from 100 glaucoma or glaucoma suspect eyes of 51 participants, 20 of which were scored twice to assess repeatability. Methods: Regression models were used to predict the average of the 11 clinicians' scores based on objective rates of change of mean deviation (MD), visual field index (VFI), pattern standard deviation (PSD), the Nth fastest progressing location, and the Nth fastest progressing of 10 anatomically defined clusters of locations after weighting by eccentricity. Main outcome measures: Correlation between the objective rates of change and the average of the 11 clinicians' scores. Results: The average MD of the study eyes was -2.4 dB (range, -16.8 to +2.8 dB). The mean clinician score was highly repeatable, with an intraclass correlation coefficient of 0.95. It correlated better with the rate of change of VFI (pseudo-R2 = 0.73, 95% confidence interval [CI, 0.60-0.83]) than with MD (pseudo-R2 = 0.63, 95% CI [0.45-0.76]) or PSD (pseudo-R2 = 0.41, 95% CI [0.26-0.55]). Using point-wise information, the highest correlations were found with the fifth-fastest progressing location (pseudo-R2 = 0.71, 95% CI [0.56-0.80]) and the fastest-progressing cluster after eccentricity weighting (pseudo-R2 = 0.61, 95% CI [0.48-0.72]). Among 25 eyes with an average VFI of > 99%, the highest observed pseudo-R2 value was 0.34 (95% CI [0.16-0.61]) for PSD. Conclusions: Expert academic glaucoma specialists' assessment of the rate of change correlated best with VFI rates, except in eyes with a VFI near the ceiling of 100%. Sensitivities averaged within clusters of locations have been shown to detect change sooner, but the experts' opinions correlated more closely with global VFI. This could be because it is currently the only index for which the perimeter automatically provides a quantitative estimate of the rate of functional progression.Item Prediction Accuracy of the Dynamic Structure-Function Model for Glaucoma Progression Using Contrast Sensitivity Perimetry and Confocal Scanning Laser Ophthalmoscopy(Wolters Kluwer, 2018-09) Ramezani, Koosha; Marín-Franch, Iván; Hu, Rongrong; Swanson, William H.; Racette, Lyne; Ophthalmology, School of MedicinePURPOSE: The purpose of this study was to determine whether combining a structural measure with contrast sensitivity perimetry (CSP), which has lower test-retest variability than static automated perimetry (SAP), reduces prediction error with 2 models of glaucoma progression. METHODS: In this retrospective analysis, eyes with 5 visits with rim area (RA), SAP, and CSP measures were selected from 2 datasets. Twenty-six eyes with open-angle glaucoma were included in the analyses. For CSP and SAP, mean sensitivity (MS) was obtained by converting the sensitivity values at each location from decibel (SAP) or log units (CSP) to linear units, and then averaging all values. MS and RA values were expressed as percent of mean normal based on independent normative data. Data from the first 3 and 4 visits were used to calculate errors in prediction for the fourth and fifth visits, respectively. Prediction errors were obtained for simple linear regression and the dynamic structure-function (DSF) model. RESULTS: With linear regression, the median prediction errors ranged from 6% to 17% when SAP MS and RA were used and from 9% to 17% when CSP MS and RA were used. With the DSF model, the median prediction errors ranged from 6% to 11% when SAP MS and RA were used and from 7% to 16% when CSP MS and RA were used. CONCLUSIONS: The DSF model had consistently lower prediction errors than simple linear regression. The lower test-retest variability of CSP in glaucomatous defects did not, however, result in lower prediction error.