Comparison of intraocular lens power prediction accuracy of formulas in American Society of Cataract and Refractive Surgery post-refractive surgery calculator in eyes with prior radial keratotomy

Purpose: To evaluate the accuracy of intraocular lens (IOL) power prediction of the formulas available on the American Society of Cataract and Refractive Surgery (ASCRS) post?refractive calculator in eyes with prior radial keratotomy (RK) for myopia. Methods: This retrospective study included 25 eyes of 18 patients whose status was post?RK for treatment of myopia, which had undergone cataract extraction with IOL implantation. Prediction error was calculated as the difference between implanted IOL power and predicted power by various formulae available on ASCRS post?refractive calculator. The formulas compared were Humphrey Atlas method, IOLMaster/Lenstar method, Barrett True?K no?history formula, ASCRS Average power, and ASCRS Maximum power on ASCRS post?refractive calculator. Results: Median absolute errors were the least for Barrett True?K and ASCRS Maximum power, that is, 0.56 (0.25, 1.04) and 0.56 (0.25, 1.06) D, respectively, and that of Atlas method was 1.60 (0.85, 2.28) D. Median arithmetic errors were positive for Atlas, Barrett True?K, ASCRS Average (0.86 [?0.17, 1.61], 0.14 [?0.22 to 0.54], and 0.23 [?0.054, 0.76] D, respectively) and negative for IOLMaster/Lenstar method and ASCRS Maximum power (?0.02 [?0.46 to 0.38] and ? 0.48 [?1.06 to ? 0.22] D, respectively). Multiple comparison analysis of Friedman抯 test revealed that Atlas formula was significantly different from IOLMaster/ Lenstar, Barrett True?K, and ASCRS Maximum power; ASCRS Maximum power was significantly different from all others (P < 0.00001). Conclusion: In post?RK eyes, Barrett True?K no?history formula and ASCRS Maximum power given by the ASCRS calculator were more accurate than other available formulas, with ASCRS Maximum leading to more myopic outcomes when compared to others

Cold bandage contact lens use reduces post-photorefractive keratectomy or corneal collagen-crosslinking pain perception in patients

Purpose: To compare post?operative pain perception using bandage contact lens (BCL) stored at 2–8?C (Cold BCL, CL?BCL) or room temperature (23 – 25?C, RT?BCL) after photorefractive keratectomy (PRK) or corneal collagen?crosslinking (CXL) and determine status of nociception associated factors. Methods: In this prospective interventional study, 56 patients undergoing PRK for refractive correction and 100 keratoconus (KC) undergoing CXL were recruited following approval from the institutional ethics committee with informed consent. Patients undergoing bilateral PRK received RT?BCL on one eye and CL?BCL on the other. Pain was graded by Wong–Baker scoring on the first post?operative day (PoD1). Expression of transient receptor potential channels (TRPV1, TRPA1, TRPM8), calcitonin gene?related peptide (CGRP) and IL?6 was measured in cellular content from used BCLs collected on PoD1. Equal number of KC patients received RT?BCL or CL?BCL post?CXL. Pain was graded by Wong–Baker scoring on PoD1. Results: Pain scores on PoD1 were significantly (P < 0.0001) reduced in subjects receiving CL?BCL (Mean ± SD: 2.6 ± 2.1) compared to RT?BCL (6.0 ± 2.4) post?PRK. 80.4% of subjects reported reduced pain scores with CL?BCL. 19.6% reported no change or increased pain scores with CL?BCL. TRPM8 expression was significantly (P < 0.05) increased in BCL of subjects reporting reduced pain with CL?BCL compared to those who did not. Pain scores on PoD1 were significantly (P < 0.0001) reduced in subjects receiving CL?BCL (3.2 ± 2.1) compared to RT?BCL (7.2 ± 1.8) post?CXL. Conclusion: The simple approach of using a cold BCL post?operatively substantially reduced pain perception and could overcome post?operative pain?related limited acceptance of PRK/CXL.

Artificial intelligence–based stratification of demographic, ocular surface high-risk factors in progression of keratoconus

Purpose: The purpose of this study was to identify and analyze the clinical and ocular surface risk factors influencing the progression of keratoconus (KC) using an artificial intelligence (AI) model. Methods: This was a prospective analysis in which 450 KC patients were included. We used the random forest (RF) classifier model from our previous study (which evaluated longitudinal changes in tomographic parameters to predict “progression” and “no progression”) to classify these patients. Clinical and ocular surface risk factors were determined through a questionnaire, which included presence of eye rubbing, duration of indoor activity, usage of lubricants and immunomodulator topical medications, duration of computer use, hormonal disturbances, use of hand sanitizers, immunoglobulin E (IgE), and vitamins D and B12 from blood investigations. An AI model was then built to assess whether these risk factors were linked to the future progression versus no progression of KC. The area under the curve (AUC) and other metrics were evaluated. Results: The tomographic AI model classified 322 eyes as progression and 128 eyes as no progression. Also, 76% of the cases that were classified as progression (from tomographic changes) were correctly predicted as progression and 67% of cases that were classified as no progression were predicted as no progression based on clinical risk factors at the first visit. IgE had the highest information gain, followed by presence of systemic allergies, vitamin D, and eye rubbing. The clinical risk factors AI model achieved an AUC of 0.812. Conclusion: This study demonstrated the importance of using AI for risk stratification and profiling of patients based on clinical risk factors, which could impact the progression in KC eyes and help manage them better

Changing trend in prevalence of refractive errors: a hospital-based study

Background: Refractive errors are the second most common reason of blindness in India after cataract. It accounts for 33.3% of cases of childhood blindness. The purpose of this study was to estimate the prevalence of refractive error and related visual impairment in children visiting a tertiary care eye center in Southern India.Methods: This was hospital-based descriptive study. Children <15 years of age with significant refractive error were included in the study. They were examined for visual acuity measurements, ocular motility evaluation, retinoscopy and autorefraction under cycloplegia, and examination of the anterior segment and fundus. Significant refractive error was defined as myopia ≥-0.75D, hypermetropia >+2D and astigmatism >0.75D. Descriptive statistics with frequency, mean±Standard deviation were computed for better and the worse eye. Statistical tests were applied between the worse and better eye using Chi square test.Results: A total of 946 children were screened. The mean age was 10.5±6.2 years (3-15 years) with 503 (53.2%) males and 443 (46.8%) females. With respect to vision in the worse eye; 54.01% (n=511) had moderately subnormal vision. One hundred and twenty- two children (23.4%) of <10 years, and 351 (67.4%) of ≥10 years group had myopia, with statistically significant difference (p<0.001). The reverse pattern was seen with respect to hypermetropia and astigmatism. Prevalence of amblyopia was found to be 8.6%.Conclusions: Though myopia is more prevalent among general population, prevalence of astigmatism is higher among children attending an eye hospital and uncorrected astigmatism is the most significant amblyogenic factor in refractive amblyopia.