Atypical microbial keratitis

Assessing PCR-Positive Acanthamoeba Keratitis-A Retrospective Chart Review

Ophthalmologists' diagnostic and treatment competence in Acanthamoeba keratitis varies widely. This investigator-initiated, retrospective, single-center chart review examined the electronic patient files regarding PCR-positive Acanthamoeba keratitis. We included corneal and contact lens assessments. We further reviewed the patient's medical history, corneal scraping results regarding viral or fungal co-infections, and the duration from symptom onset to final diagnosis. We identified 59 eyes of 52 patients from February 2010 to February 2023, with 31 of 52 (59.6%) being female patients. The median (IQR, range) patient age was 33 (25.3 to 45.5 [13 to 90]) years, and the mean (SD, range) time to diagnosis after symptom onset was 18 (10.5 to 35 [3 to 70]) days. Overall, 7 of 52 (7.7%) patients displayed a bilateral Acanthamoeba infection, and 48 (92.3%) used contact lenses at symptom onset. Regarding other microbiological co-infections, we found virologic PCR testing in 45 of 52 (86.5%) patients, with 3 (6.7%) positive corneal scrapings. Fungal cultures were performed in 49 of 52 (94.2%) patients, with 5 (10.2%) positive corneal scrapings. The medical treatment success rate was 45/46 (97.8%). This study raises awareness of patient education in contact lens handling and screens for further microbial co-infections in suspected Acanthamoeba cases.

Monitoring the Progression of Clinically Suspected Microbial Keratitis Using Convolutional Neural Networks

Purpose

For this study, we aimed to determine whether a convolutional neural network (CNN)-based method (based on a feature extractor and an identifier) can be applied to monitor the progression of keratitis while managing suspected microbial keratitis (MK).

Methods

This multicenter longitudinal cohort study included patients with suspected MK undergoing serial external eye photography at the 5 branches of Chang Gung Memorial Hospital from August 20, 2000, to August 19, 2020. Data were primarily analyzed from January 1 to March 25, 2022. The CNN-based model was evaluated via F1 score and accuracy. The area under the receiver operating characteristic curve (AUROC) was used to measure the precision-recall trade-off.

Results

The model was trained using 1456 image pairs from 468 patients. In comparing models via only training the identifier, statistically significant higher accuracy (P  < 0.05) in models via training both the identifier and feature extractor (full training) was verified, with 408 image pairs from 117 patients. The full training EfficientNet b3-based model showed 90.2% (getting better) and 82.1% (becoming worse) F1 scores, 87.3% accuracy, and 94.2% AUROC for 505 getting better and 272 becoming worse test image pairs from 452 patients.

Conclusions

A CNN-based approach via deep learning applied in suspected MK can monitor the progress/regress during treatment by comparing external eye image pairs.

Translational Relevance

The study bridges the gap between the investigation of the state-of-the-art CNN-based deep learning algorithm applied in ocular image analysis and the clinical care of suspected patients with MK.

Impact of Exposomes on Ocular Surface Diseases

Ocular surface diseases (OSDs) are significant causes of ocular morbidity, and are often associated with chronic inflammation, redness, irritation, discomfort, and pain. In severe OSDs, loss of vision can result from ocular surface failure, characterised by limbal stem cell deficiencies, corneal vascularisation, corneal opacification, and surface keratinisation. External and internal exposomes are measures of environmental factors that individuals are exposed to, and have been increasingly studied for their impact on ocular surface diseases. External exposomes consist of external environmental factors such as dust, pollution, and stress; internal exposomes consist of the surface microbiome, gut microflora, and oxidative stress. Concerning internal exposomes, alterations in the commensal ocular surface microbiome of patients with OSDs are increasingly reported due to advancements in metagenomics using next-generation sequencing. Changes in the microbiome may be a consequence of the underlying disease processes or may have a role in the pathogenesis of OSDs. Understanding the changes in the ocular surface microbiome and the impact of various other exposomes may also help to establish the causative factors underlying ocular surface inflammation and scarring, the hallmarks of OSDs. This review provides a summary of the current evidence on exposomes in various OSDs.

Pythium insidiosum Keratitis: Past, Present, and Future

Pythium insidiosum (PI) is an oomycete, a protist belonging to the clade Stramenopila. PI causes vision-threatening keratitis closely mimicking fungal keratitis (FK), hence it is also labeled as "parafungus". PI keratitis was initially confined to Thailand, USA, China, and Australia, but with growing clinical awareness and improvement in diagnostic modalities, the last decade saw a massive upsurge in numbers with the majority of reports coming from India. In the early 1990s, pythiosis was classified as vascular, cutaneous, gastrointestinal, systemic, and ocular. Clinically, morphologically, and microbiologically, PI keratitis closely resembles severe FK and requires a high index of clinical suspicion for diagnosis. The clinical features such as reticular dot infiltrate, tentacular projections, peripheral thinning with guttering, and rapid limbal spread distinguish it from other microorganisms. Routine smearing with Gram and KOH stain reveals perpendicular septate/aseptate hyphae, which closely mimic fungi and make the diagnosis cumbersome. The definitive diagnosis is the presence of dull grey/brown refractile colonies along with zoospore formation upon culture by leaf induction method. However, culture is time-consuming, and currently polymerase chain reaction (PCR) method is the gold standard. The value of other diagnostic modalities such as confocal microscopy and immunohistopathological assays is limited due to cost, non-availability, and limited diagnostic accuracy. PI keratitis is a relatively rare disease without established treatment protocols. Because of its resemblance to fungus, it was earlier treated with antifungals but with an improved understanding of its cell wall structure and absence of ergosterol, this is no longer recommended. Currently, antibacterials have shown promising results. Therapeutic keratoplasty with good margin (1 mm) is mandated for non-resolving cases and corneal perforation. In this review, we have deliberated on the evolution of PI keratitis, covered all the recently available literature, described our current understanding of the diagnosis and treatment, and the potential future diagnostic and management options for PI keratitis.

Infectious keratitis: A review

Globally, infectious keratitis is the fifth leading cause of blindness. The main predisposing factors include contact lens wear, ocular injury and ocular surface disease. Staphylococcus species, Pseudomonas aeruginosa, Fusarium species, Candida species and Acanthamoeba species are the most common causal organisms. Culture of corneal scrapes is the preferred initial test to identify the culprit organism. Polymerase chain reaction (PCR) tests and in vivo confocal microscopy can complement the diagnosis. Empiric therapy is typically commenced with fluoroquinolones, or fortified antibiotics for bacterial keratitis; topical natamycin for fungal keratitis; and polyhexamethylene biguanide or chlorhexidine for acanthamoeba keratitis. Herpes simplex keratitis is mainly diagnosed clinically; however, PCR can also be used to confirm the initial diagnosis and in atypical cases. Antivirals and topical corticosteroids are indicated depending on the corneal layer infected. Vision impairment, blindness and even loss of the eye can occur with a delay in diagnosis and inappropriate antimicrobial therapy.

Bacterial keratitis: identifying the areas of clinical uncertainty

Bacterial keratitis is a common corneal infection that is treated with topical antimicrobials. By the time of presentation there may already be severe visual loss from corneal ulceration and opacity, which may persist despite treatment. There are significant differences in the associated risk factors and the bacterial isolates between high income and low- or middle-income countries, so that general management guidelines may not be appropriate. Although the diagnosis of bacterial keratitis may seem intuitive there are multiple uncertainties about the criteria that are used, which impacts the interpretation of investigations and recruitment to clinical studies. Importantly, the concept that bacterial keratitis can only be confirmed by culture ignores the approximately 50% of cases clinically consistent with bacterial keratitis in which investigations are negative. The aetiology of these culture-negative cases is unknown. Currently, the estimation of bacterial susceptibility to antimicrobials is based on data from systemic administration and achievable serum or tissue concentrations, rather than relevant corneal concentrations and biological activity in the cornea. The provision to the clinician of minimum inhibitory concentrations of the antimicrobials for the isolated bacteria would be an important step forward. An increase in the prevalence of antimicrobial resistance is a concern, but the effect this has on disease outcomes is yet unclear. Virulence factors are not routinely assessed although they may affect the pathogenicity of bacteria within species and affect outcomes. New technologies have been developed to detect and kill bacteria, and their application to bacterial keratitis is discussed. In this review we present the multiple areas of clinical uncertainty that hamper research and the clinical management of bacterial keratitis, and we address some of the assumptions and dogma that have become established in the literature.