FRIDAY, Feb. 23, 2024 (HealthDay News) -- Fungal keratitis prevalence appears to be twice as high in rural versus nonrural areas, according to a research letter published online Feb. 15 in JAMA Ophthalmology.

Kaitlin Benedict, M.P.H., from the U.S. Centers for Disease Control and Prevention in Atlanta, and colleagues estimated fungal keratitis prevalence among commercially insured U.S. patients. The analysis included Merative MarketScan Commercial and Medicare Databases claims data (Jan. 1, 2016, through Jan. 31, 2023).

The researchers identified 870,810 individuals with continuous enrollment and a diagnostic code for keratitis. Of these, 0.8 percent had a natamycin prescription. Overall fungal keratitis prevalence was 1.8 per 100,000 enrollees but was higher among males (1.9), adults aged 65 years and older (6.6), and patients living in the South (2.7) and rural areas (3.6). Corneal ulcer was the most common associated condition (94.2 percent). Common medications included ophthalmic antibiotic (80.7 percent) or corticosteroid (43.5 percent). More than one in seven (15.0 percent) had contact lens-associated diagnostic codes. Nearly three-quarters (74.9 percent) underwent diagnostic testing, and 10.6 percent received a corneal transplant.

"Given the potential for poor vision outcomes and the possibility of climate change-associated geographic expansion of pathogenic fungi, monitoring fungal keratitis trends, improving rural eye care access, and promoting early diagnosis and treatment are crucial," the authors write.

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WEDNESDAY, Feb. 21, 2024 (HealthDay News) -- A novel protocol can be used to diagnose eye stroke and expedite treatment, according to a study published online Feb. 13 in Ophthalmology.

Gareth M.C. Lema, M.D., Ph.D., from the Icahn School of Medicine at Mount Sinai in New York City, and colleagues conducted a retrospective case series in adults who presented with painless monocular vision loss and were diagnosed with non-arteritic retinal artery occlusion. Optical coherence tomography (OCT) machines were placed in the stroke center or emergency department at three hospitals. Patients were evaluated by the stroke neurology service and underwent OCT; the images were interpreted remotely. Intra-arterial tissue plasminogen activator (IA-tPA) treatment was provided to eligible patients.

Fifty-nine patients were evaluated in the first 18 months since the protocol went live. Based on OCT and follow-up examination, the researchers found that 42 percent of patients had a confirmed retinal artery occlusion. Ten patients were eligible for treatment, and nine received IA-tPA. Within 24 hours after treatment, there was a significant improvement in mean visual acuity from LogMAR 2.14 to LogMAR 0.7; after four weeks, LogMAR was 1.04. Sixty-six percent of patients had clinically significant improvement within 24 hours, which was maintained through one month in 56 percent of treated patients. The mean time to treatment was 543 minutes from last known well and 146 minutes from presentation at the stroke center.

"We reported a novel protocol for the diagnosis of eye strokes that not only can save vision for these patients, but also demonstrates the potential to use remote consultation for time-sensitive ophthalmic emergencies," Lema said in a statement.

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TUESDAY, Feb. 13, 2024 (HealthDay News) -- Automated machine learning models may help identify eyes at risk for diabetic retinopathy (DR) progression based on ultra-widefield retinal images, according to a study published online Feb. 8 in JAMA Ophthalmology.

Paolo S. Silva, M.D., from Harvard University in Boston, and colleagues assessed whether automated machine learning models using ultra-widefield retinal images predict DR progression. The analysis included 1,179 deidentified ultra-widefield retinal images with mild or moderate nonproliferative DR (NPDR) with three years of longitudinal follow-up.

The researchers found that the model’s area under the precision-recall curve was 0.717 for baseline mild NPDR and 0.863 for moderate NPDR. In the validation set, for eyes with mild NPDR, sensitivity was 0.72, specificity was 0.63, and accuracy was 64.3 percent, while for eyes with moderate NPDR, performance was 0.80, 0.72, and 73.8 percent, respectively. The validation set identified six of nine eyes (75 percent) with mild NPDR and 35 of 41 eyes (85 percent) with moderate NPDR that progressed two steps or more. The model identified all four eyes with mild NPDR that progressed within six months and one year, as well as eight of nine (89 percent) with moderate NPDR that progressed within six months and 17 of 20 (85 percent) that progressed within one year.

"Potentially, the use of machine learning algorithms may refine the risk of disease progression and identify those at highest short-term risk, thus reducing costs and improving vision-related outcomes," the authors write.

Several authors disclosed ties to Optos.

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