Sectorwise Visual Field Simulation Using Optical Coherence Tomographic Angiography Nerve Fiber Layer Plexus Measurements in Glaucoma.

PURPOSE: To simulate 24-2 visual field (VF) using optical coherence tomographic angiography (OCTA) for glaucoma evaluation. DESIGN: Cross-sectional study. METHODS: One eye each of 39 glaucoma and 31 age-matched normal participants was scanned using 4.5-mm OCTA scans centered on the disc. The peripapillary retinal nerve fiber layer plexus capillary density (NFLP-CD, %area) was measured. The NFLP-CD and 24-2 VF maps were divided into 8 corresponding sectors using an extension of Garway-Heath scheme. RESULTS: Sector NFLP-CD was transformed to a logarithmic dB scale and converted to sector simulated VF deviation maps. Comparing simulated and actual 24-2 VF maps, the worst sector was in the same or adjacent location in the same hemisphere 97% of the time. VF mean deviation (VF-MD) was simulated by NFLP mean deviation (NFLP-MD). The differences between NFLP-MD and VF-MD in early, moderate, and severe glaucoma stages were -0.9 ± 2.0, 0.9 ± 2.9, and 5.8 ± 3.2 dB. NFLP-MD had better (P = .015) between-visit reproducibility (0.63 dB pooled standard deviation) than VF-MD (1.03 dB). NFLP-MD had a significantly higher sensitivity than VF-MD (P < .001) and overall NFL thickness (P = .031). CONCLUSIONS: OCTA-based simulated VF agreed well with actual 24-2 VF in terms of both the location and severity of glaucoma damage, with the exception of severe glaucoma in which the simulation tended to underestimate severity. The NFLP-MD had better reproducibility than actual VF-MD and holds promise for improving glaucoma monitoring. The NFLP-MD had better diagnostic accuracy than both VF-MD and overall NFL thickness and may be useful for early glaucoma diagnosis.

Projection-Resolved Optical Coherence Tomography Angiography of the Peripapillary Retina in Glaucoma.

PURPOSE: To detect plexus-specific peripapillary retinal perfusion defects in glaucoma, using projection-resolved optical coherence tomography angiography (PR-OCTA). DESIGN: Prospective cross-sectional study. METHODS: One eye each of 45 perimetric glaucoma participants and 37 age-matched normal participants were scanned using 4.5-mm OCTA scans centered on the disc. The PR-OCTA algorithm removed flow projection artifacts in OCT angiograms. Five en face OCTA slabs were analyzed: nerve fiber layer plexus (NFLP), ganglion cell layer plexus (GCLP), superficial vascular complex (SVC [NFLP + GCLP]), deep vascular complex (DVC), and all plexi combined. Peripapillary retinal capillary density (CD) and vessel density (VD) were calculated using a reflectance-compensated algorithm. RESULTS: Focal capillary dropout could be visualized more clearly in the NFLP than in the other slabs. The NFLP, SVC, and all-plexus CD in the glaucoma group were significantly lower (P < 0.001) than in the normal group, but no significant differences in GCLP-CD and DVC-CD appeared between the 2 groups. Both NFLP-CD and SVC-CD had excellent diagnostic accuracy, as measured by the area under the receiver operating characteristic curve (AROC = 0.981 and 0.976), correlation with visual field mean deviation (Pearson r = 0.819 and 0.831), and repeatability (intraclass correlation coefficients = 0.947 and 0.942). Performances of NFLP-VD and SVC-VD were similar to the corresponding CD parameters. CONCLUSIONS: In this glaucoma group, reduction in perfusion was more pronounced in superficial layers of the peripapillary retina (NFLP and SVC) than in the deeper layers. Reflectance-compensated CD and VD parameters for both NFLP and SVC could be useful in the clinical management of glaucoma.

Time Requirements for Electronic Health Record Use in an Academic Ophthalmology Center.

Importance: Electronic health record (EHR) systems have transformed the practice of medicine. However, physicians have raised concerns that EHR time requirements have negatively affected their productivity. Meanwhile, evolving approaches toward physician reimbursement will require additional documentation to measure quality and cost of care. To date, little quantitative analysis has rigorously studied these topics. Objective: To examine ophthalmologist time requirements for EHR use. Design, Setting, and Participants: A single-center cohort study was conducted between September 1, 2013, and December 31, 2016, among 27 stable departmental ophthalmologists (defined as attending ophthalmologists who worked at the study institution for ≥6 months before and after the study period). Ophthalmologists who did not have a standard clinical practice or who did not use the EHR were excluded. Exposures: Time stamps from the medical record and EHR audit log were analyzed to measure the length of time required by ophthalmologists for EHR use. Ophthalmologists underwent manual time-motion observation to measure the length of time spent directly with patients on the following 3 activities: EHR use, conversation, and examination. Main Outcomes and Measures: The study outcomes were time spent by ophthalmologists directly with patients on EHR use, conversation, and examination as well as total time required by ophthalmologists for EHR use. Results: Among the 27 ophthalmologists in this study (10 women and 17 men; mean [SD] age, 47.3 [10.7] years [median, 44; range, 34-73 years]) the mean (SD) total ophthalmologist examination time was 11.2 (6.3) minutes per patient, of which 3.0 (1.8) minutes (27% of the examination time) were spent on EHR use, 4.7 (4.2) minutes (42%) on conversation, and 3.5 (2.3) minutes (31%) on examination. Mean (SD) total ophthalmologist time spent using the EHR was 10.8 (5.0) minutes per encounter (range, 5.8-28.6 minutes). The typical ophthalmologist spent 3.7 hours using the EHR for a full day of clinic: 2.1 hours during examinations and 1.6 hours outside the clinic session. Linear mixed effects models showed a positive association between EHR use and billing level and a negative association between EHR use per encounter and clinic volume. Each additional encounter per clinic was associated with a decrease of 1.7 minutes (95% CI, -4.3 to 1.0) of EHR use time per encounter for ophthalmologists with high mean billing levels (adjusted R2 = 0.42; P = .01). Conclusions and Relevance: Ophthalmologists have limited time with patients during office visits, and EHR use requires a substantial portion of that time. There is variability in EHR use patterns among ophthalmologists.

Optical coherence tomography-based corneal power measurement and intraocular lens power calculation following laser vision correction (an American Ophthalmological Society thesis).

PURPOSE: To use optical coherence tomography (OCT) to measure corneal power and improve the selection of intraocular lens (IOL) power in cataract surgeries after laser vision correction. METHODS: Patients with previous myopic laser vision corrections were enrolled in this prospective study from two eye centers. Corneal thickness and power were measured by Fourier-domain OCT. Axial length, anterior chamber depth, and automated keratometry were measured by a partial coherence interferometer. An OCT-based IOL formula was developed. The mean absolute error of the OCT-based formula in predicting postoperative refraction was compared to two regression-based IOL formulae for eyes with previous laser vision correction. RESULTS: Forty-six eyes of 46 patients all had uncomplicated cataract surgery with monofocal IOL implantation. The mean arithmetic prediction error of postoperative refraction was 0.05 ± 0.65 diopter (D) for the OCT formula, 0.14 ± 0.83 D for the Haigis-L formula, and 0.24 ± 0.82 D for the no-history Shammas-PL formula. The mean absolute error was 0.50 D for OCT compared to a mean absolute error of 0.67 D for Haigis-L and 0.67 D for Shammas-PL. The adjusted mean absolute error (average prediction error removed) was 0.49 D for OCT, 0.65 D for Haigis-L (P=.031), and 0.62 D for Shammas-PL (P=.044). For OCT, 61% of the eyes were within 0.5 D of prediction error, whereas 46% were within 0.5 D for both Haigis-L and Shammas-PL (P=.034). CONCLUSIONS: The predictive accuracy of OCT-based IOL power calculation was better than Haigis-L and Shammas-PL formulas in eyes after laser vision correction.