Classification Criteria for Toxoplasmic Retinitis
To determine classification criteria for toxoplasmic retinitis. Machine learning of cases with toxoplasmic retinitis and 4 other infectious posterior uveitides / panuveitides. Cases of infectious posterior uveitides / panuveitides were collected in an informatics-designed preliminary database, and a...
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| Published in | American journal of ophthalmology Vol. 228; pp. 134 - 141 |
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| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.08.2021
Elsevier Limited |
| Subjects | |
| Online Access | Get full text |
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| Summary: | To determine classification criteria for toxoplasmic retinitis.
Machine learning of cases with toxoplasmic retinitis and 4 other infectious posterior uveitides / panuveitides.
Cases of infectious posterior uveitides / 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 uveitides / panuveitides. The resulting criteria were evaluated on the validation set.
Eight hundred three cases of infectious posterior uveitides / panuveitides, including 174 cases of toxoplasmic retinitis, were evaluated by machine learning. Key criteria for toxoplasmic retinitis included focal or paucifocal necrotizing retinitis and either positive polymerase chain reaction assay for Toxoplasma gondii from an intraocular specimen or the characteristic clinical picture of a round or oval retinitis lesion proximal to a hyperpigmented and/or atrophic chorioretinal scar. Overall accuracy for infectious posterior uveitides / 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 toxoplasmic retinitis were 8.2% in the training set and 10% in the validation set.
The criteria for toxoplasmic retinitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 CRediT roles: Douglas A. Jabs, MD, MBA: Conceptualization, Methodology, Validation, Investigation, Data curation, Writing--Review and editing, Visualization, Supervision, Project administration, Funding acquisition. Rubens Belfort, Jr., MD. PhD, MBA: Investigation, Writing--Review and editing. Bahram Bodaghi, PhD, FEBOph: Investigation, Writing--Review and editing. Elizabeth Graham, FRCP, DO, FRCOphth: Investigation, Writing--Review and editing. Gary N, Holland, MD: Investigation, Writing—Original draft. Writing--Review and editing. Susan L. Lightman, PhD, FRCP, FRCOphth: Investigation, Writing--Review and editing. Neal Oden, PhD: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing--Review and editing. Alan G. Palestine, MD: Investigation, Writing--Review and editing. Justine R. Smith, FRANZCO, PhD: Investigation, Writing--Review and editing. Jennifer E. Thorne, MD, PhD: Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing--Review and editing. Brett E. Trusko, PhD, MBA: Methodology, Software, Resources, Data curation, Investigation, Writing--Review and editing. Conflict of Interest: Douglas A. Jabs: none; Rubens Belfort, Jr.: none; Bahram Bodaghi: none; Elizabeth Graham: none; Gary Holland: none; Susan L. Lightman: none; Neal Oden: none; Alan G. Palestine: none; Justine R. Smith: none; Jennifer E. Thorne: Dr. Thorne engaged in a portion of this research as a consultant and was compensated for the consulting service; Brett E. Trusko: none. Writing committee: Douglas A. Jabs, MD, MBA2,3; Rubens Belfort, Jr., MD, PhD, MBA4; Bahram Bodaghi, PhD, FEBOph5; Elizabeth Graham, FRCP, DO, FRCOphth6; Gary N. Holland, MD7; Susan L. Lightman, PhD, FRCP, FRCOphth8,9; Neal Oden, PhD10; Alan G. Palestine, MD11; Justine R. Smith, FRANZCO, PhD12; Jennifer E. Thorne, MD, PhD2,3; Brett E. Trusko, PhD, MBA13 Affiliations: 1Members of the SUN Working Group are listed online at ajo.com. From 2the Department of Epidemiology, the Johns Hopkins University Bloomberg School of Public Health, and 3the Wilmer Eye Institute, the Department of Ophthalmology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA; 4the Department of Ophthalmology, Federal University of São Paulo, São Paulo, Brazil; 5the Department of Ophthalmology, Pitie-Salpetriere Hospital, IHU FOReSight, Sorbonne University, Paris, France; 6St. Thomas Hospital Medical Eye Unit, London, UK; 7the UCLA Stein Eye Institute and the Department of Ophthalmology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; 8Moorfields Eye Hospital, London, UK; 9Institute of Ophthalmology, University College London, London, UK; 10the Emmes Company, LLC, Rockville, MD, USA; 11the Department of Ophthalmology, University of Colorado School of Medicine, Aurora, Co, USA; 12Flinders University College of Medicine and Public Health, Adelaide, Australia; 13the Department of Medicine, Texas A&M University, College Station, TX, USA |
| ISSN: | 0002-9394 1879-1891 |
| DOI: | 10.1016/j.ajo.2021.03.042 |