Case Study of Endogenous Streptococcal Endophthalmitis in the Critical Care Setting

Clinical features, treatment and prognosis of patients with endogenous infectious endophthalmitis

PURPOSE

To investigate whether the clinical characteristics, treatment and prognosis of endogenous infectious endophthalmitis (EIE) have changed over the past 5 years.

METHODS

Retrospectively analyze all articles about EIE published in the PubMed, Web of Science, and Embase databases from 2017 to 2021.

RESULTS

A total of 128 patients and 147 eyes (46 left and 60 right) were included in the study. The mean age at diagnosis was 51 ± 19 years. The most common risk factors were diabetes and intravenous drug use. From 2017 to 2021, Klebsiella was the most common pathogenic microorganism (22%), and vitreous culture had the highest positivity rate. The most common complaint was blurred vision. The mean visual acuity (logMAR) at onset was 2.84, and the clinical symptoms were vitreal inflammation and opacity (63%), ocular pain (37%), and conjunctival congestion (36%). The ocular inflammation could be reduced by intraocular antibiotics or vitrectomy. However, the visual prognosis, with a mean logMAR of 2.73; only 50% of the eyes reached a visual acuity level of finger count and above. Changes in diagnostics over the past 5 years have mainly manifested as more diverse microorganism culture methods. In addition to conventional culture methods, PCR, sputum culture and aqueous humour culture are also commonly used for the diagnosis of pathogenic bacteria, improving the positive culture rate and visual prognosis.

CONCLUSION

The prognosis of EIE is poor. It is recommended to pay attention to the pathogenic bacteria culture results and accompanying systemic diseases and to diagnose and treat patients as soon as possible.

Clinical Wide-Field Retinal Image Deep Learning Classification of Exudative and Non-Exudative Age-Related Macular Degeneration

Background: Age-related macular degeneration (AMD) is a disease that currently affects approximately 196 million individuals and is projected to affect 288 million in 2040. As a result, better and earlier detection methods for this disease are needed in an effort to provide a higher quality of care. One way to achieve this is through the utilization of machine learning. A deep neural network, specifically a convoluted neural network (CNN) can be trained to differentiate between different types of AMD images given the proper training data.

Methods: In this study, a CNN was trained on 420 Optos wide-field retinal images for 70 epochs in order to classify between exudative and non-exudative AMD. These images were obtained and labeled by ophthalmologists from the Martel Eye Clinic in Rancho Cordova, CA. 

Results: After completing the study, a model was created with 88% accuracy. Both the training and validation loss started above 1 and ended below 0.2. Despite only analyzing a single image at a time, the model was still able to accurately identify if the individual had AMD in both eyes or one eye only. The model had the most trouble with bilateral non-exudative AMD. Overall the model was fairly accurate in the other categories. It was noted that the neural network was able to further differentiate from a single image if the disease is present in left, right, or both eyes. This is a point of contention for further investigation as it is impossible for the artificial intelligence (AI) to extrapolate the condition of both eyes from only one image.

Conclusion: This research fostered the development of a CNN that was able to differentiate between exudative and non-exudative AMD. As well as determine if the disease is present in the right, left, or both eyes with a relatively high degree of accuracy. The model was trained on clinical data and can theoretically be used to classify other clinical images it has never encountered before. 

A Substantive Narrative Review on the Usage of Lidocaine in Cataract Surgery

Cataracts are a disease that causes a gradual decrease in visual prowess and requires surgery when the symptoms progress to an abhorrent state. This disease can be treated through surgical procedures that use anesthetics, such as lidocaine. Through inhibiting sensory nerve propagation to the brain, lidocaine plays an invaluable part in reducing pain for patients that undergo cataract surgery. Current clinical practice commonly utilizes 2% lidocaine with fentanyl as a combination agent. However, recent studies have reported that concentrations higher than 1% can cause substantial alteration to corneal epithelium cells. Additionally, fentanyl is cited as an extremely addictive opioid inappropriate for continual use in cataract surgeries. In this review, the authors examine the application and concentration of lidocaine, along with the various combination agents that were reported in several studies that describe the usage of the anesthetic during cataract surgery. Within the review, it was found that most surgeons generally only use lidocaine gel on the corneal epithelium tissue of patients during cataract surgery. Perhaps this standard could change over time as it is generally known that using intracameral injections in conjunction with topical anesthesia produces better patient outcomes. The authors find that although anesthetics and surgical treatment for cataracts are generally beneficial for patients, there are still many adjustments that could be implemented to enhance patient outcomes.