{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13(8)"],"submitter":["Das T"],"pubmed_abstract":["<h4>Purpose</h4>We constructed a clinical clue-based algorithm to identify the microbiology-positive post-cataract surgery endophthalmitis.<h4>Methods</h4>The Endophthalmitis Infectivity Measurement Algorithm (EIMA) was constructed using presenting Snellen vision (Letter score [LS]) and Inflammation Score (IS, from the cornea, anterior chamber, iris, and vitreous). Retrospective data (70% for training; 30% for testing) was fitted into CHAID (Chi-squared Automatic Interaction Detection). EIMA was validated with prospective data. EIMA-categorized disease severity was weighed against the symptom duration to detect infecting micro-organisms.<h4>Results</h4>EIMA was constructed from 1444 retrospective data. The average LS was 6.03 ± 12.11, median IS was 14 (8-24), and culture positivity was 38%"],"journal":["Translational vision science & technology"],"pagination":["10"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11316448"],"repository":["biostudies-literature"],"pubmed_title":["Design and Validation of Endophthalmitis Infectivity Measurement Algorithm in Post Cataract Acute Endophthalmitis: EMS Report No. 6."],"pmcid":["PMC11316448"],"pubmed_authors":["Behera UC","Joseph J","Dave VP","Das T","Pandya R","Kapoor A","Belenje A","Sahoo J","Pandey S"],"additional_accession":[]},"is_claimable":false,"name":"Design and Validation of Endophthalmitis Infectivity Measurement Algorithm in Post Cataract Acute Endophthalmitis: EMS Report No. 6.","description":"<h4>Purpose</h4>We constructed a clinical clue-based algorithm to identify the microbiology-positive post-cataract surgery endophthalmitis.<h4>Methods</h4>The Endophthalmitis Infectivity Measurement Algorithm (EIMA) was constructed using presenting Snellen vision (Letter score [LS]) and Inflammation Score (IS, from the cornea, anterior chamber, iris, and vitreous). Retrospective data (70% for training; 30% for testing) was fitted into CHAID (Chi-squared Automatic Interaction Detection). EIMA was validated with prospective data. EIMA-categorized disease severity was weighed against the symptom duration to detect infecting micro-organisms.<h4>Results</h4>EIMA was constructed from 1444 retrospective data. The average LS was 6.03 ± 12.11, median IS was 14 (8-24), and culture positivity was 38%","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Aug","modification":"2025-04-18T20:07:04.305Z","creation":"2025-04-07T07:56:34.042Z"},"accession":"S-EPMC11316448","cross_references":{"pubmed":["39110575"],"doi":["10.1167/tvst.13.8.10"]}}