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Classification Criteria for Cytomegalovirus Retinitis

Authors :
Douglas A. Jabs
Rubens Belfort
Alan G. Palestine
Neal Oden
Bahram Bodaghi
Elizabeth M. Graham
Russell N. Van Gelder
Susan Lightman
Jennifer E. Thorne
Gary N. Holland
Brett Trusko
Justine R. Smith
Source :
Am J Ophthalmol
Publication Year :
2021
Publisher :
Elsevier BV, 2021.

Abstract

PURPOSE: To determine classification criteria for cytomegalovirus (CMV) retinitis. DESIGN: Machine learning of cases with CMV retinitis and 4 other infectious posterior/panuveitides. METHODS: Cases of infectious posterior/panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior/panuveitides. The resulting criteria were evaluated on the validation set. RESULTS: Eight hundred three cases of infectious posterior/panuveitides, including 211 cases of CMV retinitis, were evaluated by machine learning. Key criteria for CMV retinitis included: 1) necrotizing retinitis with indistinct borders due to numerous small satellites; 2) evidence of immune compromise; and either 3) a characteristic clinical appearance or 4) positive polymerase chain assay for CMV from an intraocular specimen. Characteristic appearances for CMV retinitis included: 1) wedge-shaped area of retinitis; 2) hemorrhagic retinitis; or 3) granular retinitis. Overall accuracy for infectious posterior/panuveitides was 92.1% in the training set and 93.3% (95% confidence interval 88.2, 96.3) in the validation set. The misclassification rates for CMV retinitis were 6.9% in the training set and 6.3% in the validation set. CONCLUSIONS: The criteria for CMV retinitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Details

ISSN :
00029394
Volume :
228
Database :
OpenAIRE
Journal :
American Journal of Ophthalmology
Accession number :
edsair.doi.dedup.....65289d0a674b5c97356c0a47d738da8e
Full Text :
https://doi.org/10.1016/j.ajo.2021.03.051