Methods: This is a prospective, non-interventional, comparative study of 59 male (27 smokers and 32 non-smokers) undergraduates of a public university. Tear film stability was evaluated using non-invasive tear break-up time and fluorescein tear break-up time. Corneal staining was determined using Efron grading scale. MDEQ and OSDI Questionnaires were used to assess dry eye symptoms. Data were obtained from the right eye only and analyzed using descriptive and correlation analysis.
Results: The age range of the participants was between 19 and 25 years. The mean age for smokers and non-smokers was 22.19 ± 2.20 and 21.22 ± 1.83 years, respectively (P = 0.07). The smoker group had statistically significant lower tear film stability than the non-smoker group (P < 0.0001). Corneal staining was statistically significant higher at the nasal and temporal parts of the cornea in smokers (P < 0.05). There was a moderate correlation between tear film stability and scores of MDEQ and OSDI.
Conclusions: Tobacco smoke has a significant effect on the tear film stability, seen in reduced tear stability values among smokers. Corneal staining was found to be more extensive in the smokers. These findings would be useful to eye-care providers in the management of their dry eye patients related to smoking.
Methods: A search was performed on PubMed, EBSCO host MEDLINE Complete, and Scopus databases encompassing the past ten years for mass pediatric screening practice patterns that met the selection criteria regarding their objectives and implementation. Results were analyzed from 18 countries across five continents.
Results: Eight countries (44%) offered screening for distance visual acuity only, where the majority of the countries (88%) used either Snellen or Tumbling E chart. High-income countries initiated screening earlier and applied a more comprehensive approach, targeting conditions other than reduced vision only, compared with middle-income countries. Chart-based testing was most commonly performed, with only three countries incorporating an instrument-based approach. Lack of eyecare and healthcare practitioners frequently necessitated the involvement of non-eyecare personnel (94%) as a vision screener including parent, trained staff, and nurse.
Conclusions: Implementation of a vision screening program was diverse within countries preceded by limited resources issues. Lack of professional eyecare practitioners implied the need to engage a lay screener. The limitation of existing tests to detect a broader range of visual problems at affordable cost advocated the urgent need for the development of an inexpensive and comprehensive screening tool.
Methods: In this prospective study, 104 eyes of 104 patients with cataract who underwent phacoemulsification and IOL implantation were recruited. Three IOL brands were used and for all eyes, IOL power calculation was performed using SRK-T, Hoffer Q formula and also OKULIX ray-tracing software. For all patients, axial length and keratometry data was obtained with IOLMaster 500 device and IOL power was determined using Hoffer Q and SRK-T formula. The IOL powers were also calculated using the OKULIX ray-tracing software combined with CASIA AS-OCT and IOLMaster 500 device. Optically measured axial length of eyes were inserted to OKULIX software from IOLMaster 500 device, and anterior and posterior tomographic and corneal pachymetry data was imported from CASIA AS-OCT into the OKULIX.The performance of each calculation methods was measured by subtracting the predicted postoperative refraction from the postoperative manifest refraction spherical equivalent (MRSE). For each of the 3 methods, the mean absolute prediction error was determined, too.
Results: The mean value absolute prediction error by OKULIX, SRK-T and Hoffer Q formulas, respectively, were 0.42 (±0.03), 0.36 (±0.02) and 0.37 (±0.02). The mean absolute prediction error by OKULIX had no significant difference between three IOL groups (P = 0.96), and it was confirmed that there was no meaningful statistically difference in mean absolute prediction error between the OKULIX, SRK-T and Hoffer Q formula. (P = 0.25). Also in each group of implanted IOLs, all three formulas worked with the same accuracy. The prediction error using OKULIX were within ±0.50 diopter in 63.5% of eyes and within ±1.00 diopter in 94.2% of eyes.
Conclusion: OKULIX ray-tracing IOL power measurements provides reliable and satisfactory postoperative results, which are comparable to other 3rd generation formulas of SRK-T and Hoffer Q.
METHODS: Seventy one eyes of 36 subjects were enrolled in this study. Patients underwent PRK for treatment of myopia. Subjects were evaluated for refractive error, keratometry, and visual acuity before and six months after surgery. Pre- and post-op non-keratometric astigmatisms were calculated by vectorial analysis of the difference between the corneal plane refractive astigmatism and keratometric astigmatism. Astigmatic analysis explored the contribution of non-keratometric astigmatisms.
RESULTS: The pre-op spherical equivalent (SE) was -6.27 ± 1.48 with 1.16 ± 1.02 diopters of corneal plane refractive astigmatism and 1.44 ± 0.47 diopters keratometric astigmatism. Post-op values were -0.60 ± 0.85, 0.56 ± 0.47, and 1.06 ± 0.57, respectively, 6 months after surgery. Pre- and post-op non-keratometric astigmatisms were 0.76 ± 0.41 and 0.76 ± 0.46, respectively, (P = 0.976) with significant correlation (r = 0.37, P = 0.002). Pre-op non-keratometric astigmatisms correlated to the pre-op SE (r = -0.25, P = 0.04). Pre-op non-keratometric astigmatisms had significant correlation with keratometric difference vector of astigmatic correction (r = 0.369, P = 0.002). Post-op non-keratometric astigmatisms correlated to keratometric induced astigmatism (r = 0.334, P = 0.006), keratometric index of success (r = 0.571, P