Classification criteria for pars planitis

null The Standardization Of Uveitis Nomenclature Sun Working Group; Douglas A Jabs; Alastair K Denniston; Andrew D Dick; James P Dunn; Michal Kramer; Neal Oden; Annabelle A Okada; Alan G Palestine; Russell W Read; Jennifer E Thorne; Brett E Trusko; Steven Yeh

doi:10.1016/j.ajo.2021.03.045

Classification criteria for pars planitis

The Standardization Of Uveitis Nomenclature Sun Working Group, Douglas A Jabs, Alastair K Denniston, Andrew D Dick, James P Dunn, Michal Kramer, Neal Oden, Annabelle A Okada, Alan G Palestine, Russell W Read, Jennifer E Thorne, Brett E Trusko, Steven Yeh

Research output: Contribution to journal › Article (Academic Journal) › peer-review

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Abstract

PURPOSE: To determine classification criteria for pars planitis DESIGN: Machine learning of cases with pars planitis and 4 other intermediate uveitides.

METHODS: Cases of intermediate uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the 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 intermediate uveitides. The resulting criteria were evaluated on the validation set.

RESULTS: Five hundred eighty-nine cases of intermediate uveitides, including 226 cases of pars planitis, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval 96.1, 99.9). Key criteria for pars planitis included unilateral or bilateral intermediate uveitis with either 1) snowballs in the vitreous or 2) snowbanks on the pars plana. Key exclusions included: 1) multiple sclerosis, 2) sarcoidosis, and 3) syphilis. The misclassification rates for pars planitis were 0 % in the training set and 1.7% in the validation set, respectively.

CONCLUSIONS: The criteria for pars planitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Original language	English
Journal	American Journal of Ophthalmology
Early online date	8 Apr 2021
DOIs	https://doi.org/10.1016/j.ajo.2021.03.045
Publication status	Published - Aug 2021

Bibliographical note

Funding Information:
Funding/Support: The Standardization of Uveitis Nomenclature (SUN) Working Group was supported by National Eye Institute/National Institutes of Health grant R01 EY026593; the David Brown Fund; the Jillian M. and Lawrence A. Neubauer Foundation; and the New York Eye and Ear Foundation.

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© 2021 Elsevier Inc.

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@article{7de6a87ae0a94f90b9e285533c0c517c,

title = "Classification criteria for pars planitis",

abstract = "PURPOSE: To determine classification criteria for pars planitis DESIGN: Machine learning of cases with pars planitis and 4 other intermediate uveitides.METHODS: Cases of intermediate uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the 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 intermediate uveitides. The resulting criteria were evaluated on the validation set.RESULTS: Five hundred eighty-nine cases of intermediate uveitides, including 226 cases of pars planitis, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval 96.1, 99.9). Key criteria for pars planitis included unilateral or bilateral intermediate uveitis with either 1) snowballs in the vitreous or 2) snowbanks on the pars plana. Key exclusions included: 1) multiple sclerosis, 2) sarcoidosis, and 3) syphilis. The misclassification rates for pars planitis were 0 % in the training set and 1.7% in the validation set, respectively.CONCLUSIONS: The criteria for pars planitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.",

author = "{The Standardization Of Uveitis Nomenclature Sun Working Group} and Jabs, {Douglas A} and Denniston, {Alastair K} and Dick, {Andrew D} and Dunn, {James P} and Michal Kramer and Neal Oden and Okada, {Annabelle A} and Palestine, {Alan G} and Read, {Russell W} and Thorne, {Jennifer E} and Trusko, {Brett E} and Steven Yeh",

note = "Funding Information: Funding/Support: The Standardization of Uveitis Nomenclature (SUN) Working Group was supported by National Eye Institute/National Institutes of Health grant R01 EY026593; the David Brown Fund; the Jillian M. and Lawrence A. Neubauer Foundation; and the New York Eye and Ear Foundation. Publisher Copyright: {\textcopyright} 2021 Elsevier Inc.",

year = "2021",

month = aug,

doi = "10.1016/j.ajo.2021.03.045",

language = "English",

journal = "American Journal of Ophthalmology",

issn = "0002-9394",

publisher = "Elsevier Science",

}

TY - JOUR

T1 - Classification criteria for pars planitis

AU - The Standardization Of Uveitis Nomenclature Sun Working Group, null

AU - Jabs, Douglas A

AU - Denniston, Alastair K

AU - Dick, Andrew D

AU - Dunn, James P

AU - Kramer, Michal

AU - Oden, Neal

AU - Okada, Annabelle A

AU - Palestine, Alan G

AU - Read, Russell W

AU - Thorne, Jennifer E

AU - Trusko, Brett E

AU - Yeh, Steven

N1 - Funding Information: Funding/Support: The Standardization of Uveitis Nomenclature (SUN) Working Group was supported by National Eye Institute/National Institutes of Health grant R01 EY026593; the David Brown Fund; the Jillian M. and Lawrence A. Neubauer Foundation; and the New York Eye and Ear Foundation. Publisher Copyright: © 2021 Elsevier Inc.

PY - 2021/8

Y1 - 2021/8

N2 - PURPOSE: To determine classification criteria for pars planitis DESIGN: Machine learning of cases with pars planitis and 4 other intermediate uveitides.METHODS: Cases of intermediate uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the 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 intermediate uveitides. The resulting criteria were evaluated on the validation set.RESULTS: Five hundred eighty-nine cases of intermediate uveitides, including 226 cases of pars planitis, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval 96.1, 99.9). Key criteria for pars planitis included unilateral or bilateral intermediate uveitis with either 1) snowballs in the vitreous or 2) snowbanks on the pars plana. Key exclusions included: 1) multiple sclerosis, 2) sarcoidosis, and 3) syphilis. The misclassification rates for pars planitis were 0 % in the training set and 1.7% in the validation set, respectively.CONCLUSIONS: The criteria for pars planitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

AB - PURPOSE: To determine classification criteria for pars planitis DESIGN: Machine learning of cases with pars planitis and 4 other intermediate uveitides.METHODS: Cases of intermediate uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the 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 intermediate uveitides. The resulting criteria were evaluated on the validation set.RESULTS: Five hundred eighty-nine cases of intermediate uveitides, including 226 cases of pars planitis, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval 96.1, 99.9). Key criteria for pars planitis included unilateral or bilateral intermediate uveitis with either 1) snowballs in the vitreous or 2) snowbanks on the pars plana. Key exclusions included: 1) multiple sclerosis, 2) sarcoidosis, and 3) syphilis. The misclassification rates for pars planitis were 0 % in the training set and 1.7% in the validation set, respectively.CONCLUSIONS: The criteria for pars planitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

U2 - 10.1016/j.ajo.2021.03.045

DO - 10.1016/j.ajo.2021.03.045

M3 - Article (Academic Journal)

C2 - 33845006

JO - American Journal of Ophthalmology

JF - American Journal of Ophthalmology

SN - 0002-9394

ER -

Classification criteria for pars planitis

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