Evidence map›Paper›PMID 40418487›Full record

ArticleJapanese journal of ophthalmology2025

Evaluation of factors relating to the development of multiple subepithelial corneal infiltrates of adenoviral keratoconjunctivitis using categorical data analysis program.

Nobuyoshi Kitaichi, Ryosuke Dei, Rikutaro Hinokuma, Ippei Yoshikawa, Miki Hiraoka, Kazuo Nakajima

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Article in Japanese journal of ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Nobuyoshi KitaichiDepartment of Ophthalmology, Health Sciences University of Hokkaido Hospital, Ainosato 2-5, Kita-ku, Sapporo, 002-8072, Japan. nobukita@hoku-iryo-u.ac.jp.
Ryosuke DeiResearch Institute of Community Care Management, Sapporo, Japan.
Rikutaro HinokumaHinokuma Eye Clinic, Kumamoto, Japan.
Ippei YoshikawaDepartment of Ophthalmology, Health Sciences University of Hokkaido Hospital, Ainosato 2-5, Kita-ku, Sapporo, 002-8072, Japan.
Miki HiraokaDepartment of Ophthalmology, Health Sciences University of Hokkaido Hospital, Ainosato 2-5, Kita-ku, Sapporo, 002-8072, Japan.
Kazuo NakajimaResearch Institute of Community Care Management, Sapporo, Japan.

Funding

Alcon Japan 2023-R-1003Health Sciences University of Hokkaido 2024 Research Project of the Research Institute of Health Sciences
6 · The paper itself

Abstract

purposeAdenoviral keratoconjunctivitis is the most common infectious disease in ophthalmology. Its clinical forms include epidemic keratoconjunctivitis (EKC) and pharyngoconjunctival fever (PCF). EKC can be complicated by multiple subepithelial infiltrates (MSI) of the cornea, which affect visual outcomes. As at present, artificial intelligence and machine learning are becoming increasingly important not only in the diagnostic imaging field but also in the clinical data science field, we investigated a predictive model for the development of corneal MSI in adenoviral keratoconjunctivitis. STUDY

designRetrospective cohort study.

methodsOne hundred and forty cases of adenovirus keratoconjunctivitis diagnosed at Hinokuma Eye Clinic were enrolled. The dependent variable was corneal MSI, and the independent variables were adenovirus genotype, bilaterality, subconjunctival hemorrhage, eyelid swelling, conjunctival edema, conjunctival opacity, pseudomembrane, corneal epithelial findings, preauricular lymphadenopathy, eye discharge, lacrimation, eye pain, foreign body sensation, and itchy eye, which were analyzed by a categorical data analysis program (CATDAP).

resultsFor single independent variables, corneal epithelial findings (Akaike information criterion, AIC=-6.46) and type 54 (AIC=-4.30) showed high predictive performance. In the combination of multiple independent variables, Type 56 or Type 37 with corneal epithelial damage also showed high predictive performance.

conclusionPredicting the occurrence of corneal MSI has until now been considered difficult. When a patient with EKC has marginal corneal epithelial or subepithelial opacities at initial presentation, it is presumed that the patient has a relatively high risk of developing MSI. Knowing the prevalent virus type in advance, would prove helpful in the diagnosis.

Indexed as

Adenoviruses, HumanAdenovirus Infections, HumanEpithelium, CornealEye Infections, ViralKeratoconjunctivitisAdolescentAdultAgedDNA, ViralFemaleFollow-Up StudiesHumansMaleMiddle AgedRetrospective StudiesYoung AdultDNA, ViralAdenovirusConjunctivitisEKCMachine learningMSI

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.