Automatic Classification of Slit-Lamp Photographs by Imaging Illumination.

PURPOSE: The aim of this study was to facilitate deep learning systems in image annotations for diagnosing keratitis type by developing an automated algorithm to classify slit-lamp photographs (SLPs) based on illumination technique. METHODS: SLPs were collected from patients with corneal ulcer at Kellogg Eye Center, Bascom Palmer Eye Institute, and Aravind Eye Care Systems. Illumination techniques were slit beam, diffuse white light, diffuse blue light with fluorescein, and sclerotic scatter (ScS). Images were manually labeled for illumination and randomly split into training, validation, and testing data sets (70%:15%:15%). Classification algorithms including MobileNetV2, ResNet50, LeNet, AlexNet, multilayer perceptron, and k-nearest neighborhood were trained to distinguish 4 type of illumination techniques. The algorithm performances on the test data set were evaluated with 95% confidence intervals (CIs) for accuracy, F1 score, and area under the receiver operator characteristics curve (AUC-ROC), overall and by class (one-vs-rest). RESULTS: A total of 12,132 images from 409 patients were analyzed, including 41.8% (n = 5069) slit-beam photographs, 21.2% (2571) diffuse white light, 19.5% (2364) diffuse blue light, and 17.5% (2128) ScS. MobileNetV2 achieved the highest overall F1 score of 97.95% (CI, 97.94%-97.97%), AUC-ROC of 99.83% (99.72%-99.9%), and accuracy of 98.98% (98.97%-98.98%). The F1 scores for slit beam, diffuse white light, diffuse blue light, and ScS were 97.82% (97.80%-97.84%), 96.62% (96.58%-96.66%), 99.88% (99.87%-99.89%), and 97.59% (97.55%-97.62%), respectively. Slit beam and ScS were the 2 most frequently misclassified illumination. CONCLUSIONS: MobileNetV2 accurately labeled illumination of SLPs using a large data set of corneal images. Effective, automatic classification of SLPs is key to integrating deep learning systems for clinical decision support into practice workflows.

Validation of the C-DU(KE) Calculator as a Predictor of Outcomes in Patients Enrolled in Steroids for Corneal Ulcer and Mycotic Ulcer Treatment Trials.

PURPOSE: The aim of this study was to validate the C-DU(KE) calculator as a predictor of treatment outcomes on a data set derived from patients with culture-positive ulcers. METHODS: C-DU(KE) criteria were compiled from a data set consisting of 1063 cases of infectious keratitis from the Steroids for Corneal Ulcer Trial (SCUT) and Mycotic Ulcer Treatment Trial (MUTT) studies. These criteria include corticosteroid use after symptoms, visual acuity, ulcer area, fungal etiology, and elapsed time to organism-sensitive therapy. Univariate analysis was performed followed by multivariable logistic regressions on culture-exclusive and culture-inclusive models to assess for associations between the variables and outcome. The predictive probability of treatment failure, defined as the need for surgical intervention, was calculated for each study participant. Discrimination was assessed using the area under the curve for each model. RESULTS: Overall, 17.9% of SCUT/MUTT participants required surgical intervention. Univariate analysis showed that decreased visual acuity, larger ulcer area, and fungal etiology had a significant association with failed medical management. The other 2 criteria did not. In the culture-exclusive model, 2 of 3 criteria, decreased vision [odds ratio (OR) = 3.13, P < 0.001] and increased ulcer area (OR = 1.03, P < 0.001), affected outcomes. In the culture-inclusive model, 3 of 5 criteria, decreased vision (OR = 4.9, P < 0.001), ulcer area (OR = 1.02, P < 0.001), and fungal etiology (OR = 9.8, P < 0.001), affected results. The area under the curves were 0.784 for the culture-exclusive model and 0.846 for the culture-inclusive model which were comparable to the original study. CONCLUSIONS: The C-DU(KE) calculator is generalizable to a study population from large international studies primarily taking place in India. These results support its use as a risk stratification tool assisting ophthalmologists in patient management.

Assessing the Impact of Image Quality on Deep Learning Classification of Infectious Keratitis.

Objective: To investigate the impact of corneal photograph quality on convolutional neural network (CNN) predictions. Design: A CNN trained to classify bacterial and fungal keratitis was evaluated using photographs of ulcers labeled according to 5 corneal image quality parameters: eccentric gaze direction, abnormal eyelid position, over/under-exposure, inadequate focus, and malpositioned light reflection. Participants: All eligible subjects with culture and stain-proven bacterial and/or fungal ulcers presenting to Aravind Eye Hospital in Madurai, India, between January 1, 2021 and December 31, 2021. Methods: Convolutional neural network classification performance was compared for each quality parameter, and gradient class activation heatmaps were generated to visualize regions of highest influence on CNN predictions. Main Outcome Measures: Area under the receiver operating characteristic and precision recall curves were calculated to quantify model performance. Bootstrapped confidence intervals were used for statistical comparisons. Logistic loss was calculated to measure individual prediction accuracy. Results: Individual presence of either light reflection or eyelids obscuring the corneal surface was associated with significantly higher CNN performance. No other quality parameter significantly influenced CNN performance. Qualitative review of gradient class activation heatmaps generally revealed the infiltrate as having the highest diagnostic relevance. Conclusions: The CNN demonstrated expert-level performance regardless of image quality. Future studies may investigate use of smartphone cameras and image sets with greater variance in image quality to further explore the influence of these parameters on model performance. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Apollo Rising: Acute Conjunctivitis Outbreak in India, 2022.

Purpose: To identify pathogens associated with the 2022 conjunctivitis outbreak in Tamil Nadu, India. Methods: This prospective study was conducted in November of 2022. Patients with presumed acute infectious conjunctivitis presenting to the Aravind Eye Clinic in Madurai, India were eligible. Anterior nares and conjunctival samples from participants were obtained and processed for metagenomic RNA deep sequencing (RNA-seq). Results: Samples from 29 patients were sequenced. A pathogen was identified in 28/29 (97%) patients. Coxsackievirus A24v, a highly infectious RNA virus, was the predominant pathogen and detected in 23/29 patients. Human adenovirus D (HAdV-D), a DNA virus commonly associated with conjunctivitis outbreaks, was detected in the remaining patients (5/29). Hemorrhagic conjunctiva was documented in both HAdV-D and coxsackievirus A24v affected patients but was not the predominant clinical presentation. Phylogenetic analysis of coxsackievirus A24v revealed a recent divergence from the 2015 outbreak. Conclusions: Coxsackievirus A24v and HAdV-D were co-circulating during the 2022 conjunctivitis outbreak in Tamil Nadu, India. Clinical findings were similar between patients with HAD-V and coxsackievirus A24v associated conjunctivitis. As high-throughput technologies become more readily accessible and cost-effective, unbiased pathogen surveillance may prove useful for outbreak surveillance and control.

Rapid Point-of-Care Identification of Aspergillus Species in Microbial Keratitis.

Importance: Microbial keratitis (MK) is a common cause of unilateral visual impairment, blindness, and eye loss in low-income and middle-income countries. There is an urgent need to develop and implement rapid and simple point-of-care diagnostics for MK to increase the likelihood of good outcomes. Objective: To evaluate the diagnostic performance of the Aspergillus-specific lateral-flow device (AspLFD) to identify Aspergillus species causing MK in corneal scrape and corneal swab samples of patients presenting with microbial keratitis. Design, Setting, and Participants: This diagnostic study was conducted between May 2022 and January 2023 at the corneal clinic of Aravind Eye Hospital in Madurai, Tamil Nadu, India. All study participants were recruited during their first presentation to the clinic. Patients aged 15 years or older met the eligibility criteria if they were attending their first appointment, had a corneal ulcer that was suggestive of a bacterial or fungal infection, and were about to undergo diagnostic scrape and culture. Main Outcomes and Measures: Sensitivity and specificity of the AspLFD with corneal samples collected from patients with MK. During routine diagnostic scraping, a minimally invasive corneal swab and an additional corneal scrape were collected and transferred to aliquots of sample buffer and analyzed by lateral-flow device (LFD) if the patient met the inclusion criteria. Photographs of devices were taken with a smartphone and analyzed using a ratiometric approach, which was developed for this study. The AspLFD results were compared with culture reports. Results: The 198 participants who met the inclusion criteria had a mean (range) age of 51 (15-85) years and included 126 males (63.6%). Overall, 35 of 198 participants with corneal scrape (17.7%) and 17 of 40 participants with swab samples (42.5%) had positive culture results for Aspergillus species. Ratiometric analysis results for the scrape samples found that the AspLFD achieved high sensitivity (0.89; 95% CI, 0.74-0.95), high negative predictive value (0.97; 95% CI, 0.94-0.99), low negative likelihood ratio (0.12; 95% CI, 0.05-0.30), and an accuracy of 0.94 (95% CI, 0.90-0.97). Ratiometric analysis results for the swab samples showed that the AspLFD had high sensitivity (0.94; 95% CI, 0.73-1.00), high negative predictive value (0.95; 95% CI, 0.76-1.00), low negative likelihood ratio (0.07; 95% CI, 0.01-0.48), and an accuracy of 0.88 (95% CI, 0.73-0.96). Conclusions and Relevance: Results of this diagnostic study suggest that AspLFD along with the ratiometric analysis of LFDs developed for this study has high diagnostic accuracy in identifying Aspergillus species from corneal scrapes and swabs. This technology is an important step toward the provision of point-of-care diagnostics for MK and could inform the clinical management strategy.

FluoroPi Device With SmartProbes: A Frugal Point-of-Care System for Fluorescent Detection of Bacteria From a Pre-Clinical Model of Microbial Keratitis.

Purpose: Rapid and accurate diagnosis of microbial keratitis (MK) could greatly improve patient outcomes. Here, we present the development of a rapid, accessible multicolour fluorescence imaging device (FluoroPi) and evaluate its performance in combination with fluorescent optical reporters (SmartProbes) to distinguish bacterial Gram status. Furthermore, we show feasibility by imaging samples obtained by corneal scrape and minimally invasive corneal impression membrane (CIM) from ex vivo porcine corneal MK models. Methods: FluoroPi was built using a Raspberry Pi single-board computer and camera, light-emitting-diodes (LEDs), and filters for white-light and fluorescent imaging, with excitation and detection of bacterial optical SmartProbes: Gram-negative, NBD-PMX (exmax 488 nm); Gram positive, Merocy-Van (exmax 590 nm). We evaluated FluoroPi with bacteria (Pseudomonas aeruginosa and Staphylococcus aureus) isolated from ex vivo porcine corneal models of MK by scrape (needle) and CIM with the SmartProbes. Results: FluoroPi provides <1 µm resolution and was able to readily distinguish bacteria isolated from ex vivo models of MK from tissue debris when combined with SmartProbes, retrieved by both scrape and CIM. Single bacteria could be resolved within the field of view, with limits of detection demonstrated as 103 to 104 CFU/mL. Sample preparation prior to imaging was minimal (wash-free), and imaging and postprocessing with FluoroPi were straightforward, confirming ease of use. Conclusions: FluoroPi coupled with SmartProbes provides effective, low-cost bacterial imaging, delineating Gram-negative and Gram-positive bacteria directly sampled from a preclinical model of MK. Translational Relevance: This study provides a crucial stepping stone toward clinical translation of a rapid, minimally invasive diagnostic approach for MK.

The effect of social determinants of health on severity of microbial keratitis presentation at a tertiary eye care hospital in Southern India.

Purpose: Understanding the association between social determinants of health (SDoHs) and microbial keratitis (MK) can inform underlying risk for patients and identify risk factors associated with worse disease, such as presenting visual acuity (VA) and time to initial presentation. Methods: This was a cross-sectional study was conducted with patients presenting with MK to the cornea clinic at a tertiary care hospital in Madurai, India. Patient demographics, SDoH survey responses, geographic pollution, and clinical features at presentation were collected. Descriptive statistics, univariate analysis, multi-variable linear regression models, and Poisson regression models were utilized. Results: There were 51 patients evaluated. The mean age was 51.2 years (SD = 13.3); 33.3% were female and 55% did not visit a vision center (VC) prior to presenting to the clinic. The median presenting logarithm of the minimum angle of resolution (logMAR) VA was 1.1 [Snellen 20/240, inter-quartile range (IQR) = 20/80 to 20/4000]. The median time to presentation was 7 days (IQR = 4.5 to 10). The average particulate matter 2.5 (PM2.5) concentration, a measure of air pollution, for the districts from which the patients traveled was 24.3 µg/m3 (SD = 1.6). Age- and sex-adjusted linear regression and Poisson regression results showed that higher levels of PM2.5 were associated with 0.28 worse presenting logMAR VA (Snellen 2.8 lines, P = 0.002). Patients who did not visit a VC had a 100% longer time to presentation compared to those who did (incidence rate ratio = 2.0, 95% confidence interval = 1.3-3.0, P = 0.001). Conclusion: Patient SDoH and environmental exposures can impact MK presentation. Understanding SDoH is important for public health and policy implications to mitigate eye health disparities in India.

Association of Serious Adverse Effects With the Contamination of Ocular Medications-A Global Perspective.

This Viewpoint offers examples of the causes of infectious keratitis around the world and emphasizes the need for vigilance among ophthalmologists to reduce the risk of sight-threatening complications.

Deep Convolutional Neural Networks Detect no Morphological Differences Between Culture-Positive and Culture-Negative Infectious Keratitis Images.

Purpose: To determine whether convolutional neural networks can detect morphological differences between images of microbiologically positive and negative corneal ulcers. Methods: A cross-sectional comparison of prospectively collected data consisting of bacterial and fungal cultures and smears from eyes with acute infectious keratitis at Aravind Eye Hospital. Two convolutional neural network architectures (DenseNet and MobileNet) were trained using images obtained from handheld cameras collected from culture-positive and negative images and smear-positive and -negative images. Each architecture was trained on two image sets: (1) one with labels assigned using only culture results and (2) one using culture and smear results. The outcome measure was area under the receiver operating characteristic curve for predicting whether an ulcer would be microbiologically positive or negative. Results: There were 1970 images from 886 patients were included. None of the models were better than random chance at predicting positive microbiologic results (area under the receiver operating characteristic curve ranged from 0.49 to 0.56; all confidence intervals included 0.5). Conclusions: These two state-of-the-art deep convolutional neural network architectures could not reliably predict whether a corneal ulcer would be microbiologically positive or negative based on clinical photographs. This absence of detectable morphological differences informs the future development of computer vision models trained to predict the causative agent in infectious keratitis using corneal photography. Translational Relevance: These deep learning models were not able to identify morphological differences between microbiologically positive and negative corneal ulcers. This finding suggests that similar artificial intelligence models trained to identify the causative pathogen using only microbiologically positive cases may have potential to generalize well, including to cases with falsely negative microbiologic testing.

Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks.

Purpose: Develop computer vision models for image-based differentiation of bacterial and fungal corneal ulcers and compare their performance against human experts. Design: Cross-sectional comparison of diagnostic performance. Participants: Patients with acute, culture-proven bacterial or fungal keratitis from 4 centers in South India. Methods: Five convolutional neural networks (CNNs) were trained using images from handheld cameras collected from patients with culture-proven corneal ulcers in South India recruited as part of clinical trials conducted between 2006 and 2015. Their performance was evaluated on 2 hold-out test sets (1 single center and 1 multicenter) from South India. Twelve local expert cornea specialists performed remote interpretation of the images in the multicenter test set to enable direct comparison against CNN performance. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC) individually and for each group collectively (i.e., CNN ensemble and human ensemble). Results: The best-performing CNN architecture was MobileNet, which attained an AUC of 0.86 on the single-center test set (other CNNs range, 0.68-0.84) and 0.83 on the multicenter test set (other CNNs range, 0.75-0.83). Expert human AUCs on the multicenter test set ranged from 0.42 to 0.79. The CNN ensemble achieved a statistically significantly higher AUC (0.84) than the human ensemble (0.76; P < 0.01). CNNs showed relatively higher accuracy for fungal (81%) versus bacterial (75%) ulcers, whereas humans showed relatively higher accuracy for bacterial (88%) versus fungal (56%) ulcers. An ensemble of the best-performing CNN and best-performing human achieved the highest AUC of 0.87, although this was not statistically significantly higher than the best CNN (0.83; P = 0.17) or best human (0.79; P = 0.09). Conclusions: Computer vision models achieved superhuman performance in identifying the underlying infectious cause of corneal ulcers compared with cornea specialists. The best-performing model, MobileNet, attained an AUC of 0.83 to 0.86 without any additional clinical or historical information. These findings suggest the potential for future implementation of these models to enable earlier directed antimicrobial therapy in the management of infectious keratitis, which may improve visual outcomes. Additional studies are ongoing to incorporate clinical history and expert opinion into predictive models.

Quantifying Clinicians' Diagnostic Uncertainty When Making Initial Treatment Decisions for Microbial Keratitis.

PURPOSE: There is a need to understand physicians' diagnostic uncertainty in the initial management of microbial keratitis (MK). This study aimed to understand corneal specialists' diagnostic uncertainty by establishing risk thresholds for treatment of MK that could be used to inform a decision curve analysis for prediction modeling. METHODS: A cross-sectional survey of corneal specialists with at least 2 years clinical experience was conducted. Clinicians provided the percentage risk at which they would always or never treat MK types (bacterial, fungal, herpetic, and amoebic) based on initial ulcer sizes and locations (<2 mm2 central, <2 mm2 peripheral, and >8 mm2 central). RESULTS: Seventy-two of 99 ophthalmologists participated who were 50% female with an average of 14.7 (SD = 10.1) years of experience, 60% in academic practices, and 38% outside the United States. Clinicians reported they would "never" and "always" treat a <2 mm2 central MK infection if the median risk was 0% and 20% for bacterial (interquartile range, IQR = 0-5 and 5-50), 4.5% and 27.5% for herpetic (IQR = 0-10 and 10-50), 5% and 50% for fungal (IQR = 0-10 and 20-75), and 5% and 50.5% for amoebic (IQR = 0-20 and 32-80), respectively. Mixed-effects models showed lower thresholds to treat larger and central infections (P < 0.001, respectively), and thresholds to always treat differed between MK types for the United States (P < 0.001) but not international clinicians. CONCLUSIONS: Risk thresholds to treat differed by practice locations and MK types, location, and size. Researchers can use these thresholds to understand when a clinician is uncertain and to create decision support tools to guide clinicians' treatment decisions.

Outpatient human coronavirus associated conjunctivitis in India.

BACKGROUND: Viral conjunctivitis (pink eye) can be highly contagious and is of public health importance. There remains significant debate whether SARS-CoV-2 can present as a primary conjunctivitis. The aim of this study was to identify pathogens associated with outpatient infectious conjunctivitis during the COVID-19 Delta surge. METHODS: This prospective study was conducted in the spring and summer months of 2021. 106 patients with acute conjunctivitis who presented to the Aravind Eye Center in Madurai, India were included. One anterior nasal swab and one conjunctival swab of each eye were obtained for each enrolled patient. Samples were subsequently processed for unbiased metagenomic RNA deep sequencing (RNA-seq). Outcomes included clinical findings and codetection of other pathogens with SARS-CoV-2 in patients with conjunctivitis. RESULTS: Among the 13 patients identified with human coronavirus RNA fragments in their swabs, 6 patients had SARS-CoV-2 infection, 5 patients had coinfections of SARS-CoV-2 and human adenovirus (HAdV), 1 patient had a coinfection with human coronavirus OC43 and HAdV, and 1 patient had a coinfection of Vittaforma corneae and SARS-CoV-2. 30% had bilateral disease and symptoms on presentation. Petechial hemorrhage was noted in 33% of patients with SARS-CoV-2 infection. No patients with SARS-CoV-2 or SARS-CoV-2 and HAdV infections had subepithelial infiltrates on presentation. All patients denied systemic symptoms. CONCLUSIONS: Among the patients presented with conjunctivitis associated with human coronavirus infection, over 50% of the patients had co-infections with other circulating pathogens, suggesting the public-health importance of broad pathogen testing and surveillance in the outpatient conjunctivitis population.

Deep sequencing analysis of clinical samples from patients with acute infectious conjunctivitis during the COVID-19 delta surge in Madurai, India.

BACKGROUND: Seasonal outbreaks of infectious conjunctivitis remain a public health issue. Determination of outbreak etiologies in the context of a worldwide pandemic may provide useful information to guide public health strategies. The aim of this study was to identify pathogens associated with outpatient infectious conjunctivitis during the COVID-19 Delta surge. METHODS: This prospective study was conducted from April 2021 to September 2021. All outpatients presenting to the Aravind Eye Center (Madurai, India) with signs and symptoms consistent with acute infectious conjunctivitis were eligible. Three swabs were obtained from each participant: one from each conjunctiva and one from the anterior nares. Samples were processed for metagenomic RNA deep sequencing (RNA-seq). RESULTS: Samples from 106 study participants were sequenced. The most common presenting symptoms were tearing (86%) and itching (71%). Preauricular lymphadenopathy was present in 38% of participants. 20% of participants had close contacts with similar symptoms. Systemic symptoms such as coughing, runny nose, vomiting or diarrhea were uncommonly reported. 60% of all participants used some medicated eye drops upon enrollment. 75% of study participants demonstrated infection with human adenovirus D (HAdV-D). 11% of conjunctivitis was associated with SARS-CoV-2. 15% had no definitive pathogen detected. 8% of all participants had codetection of more than one pathogen on RNA-seq. CONCLUSIONS: During the COVID-19 Delta surge in India, HAdV-D was the most common pathogen associated with infectious conjunctivitis. SARS-CoV-2 was the second most common associated pathogen. Seasonal surveillance may be necessary for the determination of emerging and reemerging pathogens responsible for infectious conjunctivitis.

The Case for Fungal Keratitis to Be Accepted as a Neglected Tropical Disease.

Amongst the treatable cause of blindness among young people, fungal keratitis ranks high. There are an estimated 1,051,787 to 1,480,916 eyes affected annually, with 8-11% of patients having to have the eye removed. Diagnosis requires a corneal scraping, direct microscopy and fungal culture with a large number of airborne fungi implicated. Treatment involves the intensive application of antifungal eye drops, preferably natamycin, often combined with surgery. In low-resource settings, inappropriate corticosteroid eye drops, ineffective antibacterial therapy, diagnostic delay or no diagnosis all contribute to poor ocular outcomes with blindness (unilateral or bilateral) common. Modern detailed guidelines on fungal keratitis diagnosis and management are lacking. Here, we argue that fungal keratitis should be included as a neglected tropical disease, which would facilitate greater awareness of the condition, improved diagnostic capability, and access to affordable antifungal eye medicine.

Analysis on the completeness of case records of patients with penetrating ocular trauma following a multimodal intervention.

Purpose: To describe the process development of a multimodal intervention and the pre- and postintervention results on the completeness of case records of patients with penetrating ocular trauma in a high-volume tertiary eye care hospital in south India. Methods: A multimodal intervention including an objective-validated case sheet template, an education program, a physical template case record reminder, a continuous near-real time audit process, and a feedback system was developed. Analysis on the completeness of the case records of patients with ocular trauma from October 2020 to December 2020 (preintervention) and from January 2021 to March 2021 (postintervention) was performed. These case records and the personnel involved in the documentation, were given scores based on the scores assigned to the subsections of the validated template case sheet. The mean total score of the case records and of the personnel involved were analyzed. Results: One hundred and eleven case records of patients with ocular trauma who underwent primary wound repair were included in the study. Of these 111 case records, 46 belonged to preintervention group and 65 belonged to postintervention group. The mean total score for preintervention group during the study period was 57.93 ± 24 out of 100 and for postintervention group was 99.07 ± 4.49 out of 100. The temporal trend of postintervention group showed a consistent improvement every month (97.14, 100,100) during the 3-month study period. Postintervention improvement was noted in all the sections of case records completed by both fellows and consultants. Conclusion: A sustained improvement in ocular trauma case record documentation among all levels of medical professionals was noted following the five-component multimodal intervention.

The Ophthalmology Surgical Competency Assessment Rubric (OSCAR) for Open Globe Surgical Management.

Background/Aims: To produce an internationally developed rubric to assess surgical competency in open globe management. Methods: An international expert panel of seven ophthalmologist educators developed a standardized competency-based rubric. The steps to perform an open globe repair were outlined. Based on a modified Dreyfus model, the experts agreed on the steps of surgery and certain global indices. They then assigned descriptors for the competency expected of a novice, beginner, advanced beginner, and competent surgeon. The tool was then vetted by another panel of ten ophthalmologists. The main outcome measure was the final version of the tool as agreed upon by the expert review panel. Results: The steps of open globe repair and key global indices were established. Descriptive wording for each step and global indices for novice, beginner, advanced beginner, and competent surgery were listed. All the expert comments were integrated to establish face and content validity. Conclusion: This standardized rubric to evaluate resident competency should be used globally in training programs to assess open globe repair surgical skills. Using a modified Dreyfus model, four different levels of training competency were defined allowing a non-biased, objective, numerical and simple assessment.

Comparative study on costs incurred for treatment of patients with bacterial and fungal keratitis - A retrospective analysis.

Purpose: To compare the costs associated with medications and travel of patients with smear-proven bacterial keratitis and fungal keratitis in a tertiary care center in India. Methods: Retrospective analysis of case records of a cohort of patients who presented between April 2017 and March 2018 to a tertiary care center in India, with infectious keratitis who were smear-positive for bacteria or fungi, and whose costs of treatment and travel were supported by a philanthropic program. Results: In total, 672 case records of 177 smear-positive bacterial keratitis (BK) and 495 smear-positive fungal keratitis (FK) were included in the study. Further, 62% of BK and 75% of FK received more than one antimicrobial drug (P < 0.001). The mean total medication cost (INR) was significantly more in FK (959.1 ± 675.2) compared to BK (674.9 ± 463.7) (P < 0.0001). The mean medication cost (INR) per visit was also more for FK (201.1 ± 109.4) compared to BK (155.2 ± 84.1) (P < 0.0001). The mean total medication cost was significantly more for FK for both patients who healed with medical treatment (611.6 ± 395.6 for BK, 801.5 ± 599.9 for FK, P = 0.0005) and for patients who required TPK (953.7 ± 653.1 for BK, 1374.6 ± 701.5 for FK, P = 0.0023) compared to their respective counterparts in BK. Conclusion: Patients with fungal keratitis incurred significantly more on medications compared to patients with bacterial keratitis irrespective of whether they had healed with successful medical treatment or required therapeutic keratoplasty. Prolonged duration of treatment and the high costs of antifungal medications account for the significant economic burden of fungal keratitis.

Outcomes of amoebic, fungal, and bacterial keratitis: A retrospective cohort study.

BACKGROUND: Acanthamoeba keratitis is challenging to treat and thought to result in poor outcomes, but very few comparative studies exist to assess whether ulcers caused by Acanthamoeba are worse than those caused by bacteria or fungus. METHODS: In a retrospective cohort study, all cases of smear- or culture-proven Acanthamoeba keratitis diagnosed from January 2006 to June 2011 at an eye hospital in South India were identified from the microbiology database. Random samples of the same number of cases of bacterial and fungal keratitis, matched by year, were identified from the same database in order to compare outcomes between the three types of organism. The main outcomes were the time until the following events: re-epithelialization, discontinuation of antimicrobials, perforation/keratoplasty, elevated intraocular pressure, and new cataract. RESULTS: The median time until re-epithelialization was 113 days for Acanthamoeba keratitis, 30 days for fungal keratitis, and 25 days for bacterial keratitis, and the median time until discontinuation of antimicrobial therapy was 100 days for Acanthamoeba keratitis, 49 days for fungal keratitis, and 40 days for bacterial keratitis. Compared to the other two organisms, Acanthamoeba ulcers took significantly longer to re-epithelialize (adjusted HR 0.4, 95% CI 0.3 to 0.6 relative to bacterial ulcers and HR 0.3, 95% CI 0.2 to 0.5 relative to fungal ulcers; overall p<0.001) and had significantly longer courses of antimicrobials (adjusted HR 0.3, 95% CI 0.2 to 0.6 relative to bacterial ulcers and HR 0.5, 95%CI 0.3 to 0.8 relative to fungal ulcers; overall p<0.001). No statistically significant difference was observed between the three organisms for the other time-to-event outcomes. CONCLUSIONS: Acanthamoeba keratitis was more difficult to treat and had worse clinical outcomes than bacterial or fungal ulcers, highlighting the lack of adequate treatment regimens for this infection.