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.
I ntermediate uveitis refers to a class of uveitic diseases characterized by inflammation predominantly in the vitreous and an absence of retinitis and choroiditis. Intermediate uveitides may be caused by infections such as Lyme disease or syphilis; or they may be associated with systemic diseases, particularly sarcoidosis and multiple sclerosis (MS); or they may occur as an isolated, presumably immunity-mediated ocular disorder of unknown origin. Pars planitis represents a subset of intermediate uveitis characterized by fibroinflammatory material overlying the pars plana and peripheral retina (“snowbanks”). , Initially noted by Schepens in 1950 and termed “peripheral uveitis,” the features of what is now termed pars planitis were described nearly simultaneously in 1960 by Welch and associates and Brockhurst and associates. Also termed cyclitis by Hogan and Kimura, the name “pars planitis” was coined by Welch and associates, and pars planitis has remained the term most commonly used for this intermediate uveitic disease. Although snowbanks have been considered the conventional hallmark of pars planitis, a similar uveitic disorder occurs as an intermediate uveitis without snowbanks or “snowballs” (fibroinflammatory debris typically in the inferior vitreous), which now is termed intermediate uveitis, non-pars planitis type, and which also could be considered an “undifferentiated intermediate uveitis.” Case series which have included both pars planitis and non-pars planitis types of intermediate uveitis have made interpretations of the published studies more difficult. In 2005, the Standardization of Uveitis Nomenclature (SUN) Working Group at a consensus meeting agreed that the term pars planitis should apply to cases of non-infectious intermediate uveitis with vitritis and either inferior vitreous inflammatory condensates (“snowballs”) or pars plana “snowbanks,” unassociated with a systemic disease and that it should be distinguished from intermediate uveitis, non-pars planitis type. Furthermore, the group recognized that pars planitis may have peripheral retinal vascular sheathing and non-perfusion (more easily seen on wide-field fluorescein angiography) but should not have posterior pole or mid-peripheral occlusive retinal vasculitis.
Given the definitional variations in the disease, its frequency in referral center case series has been reported to vary from 2.4%-15.4% of uveitis cases, , and its incidence has been estimated at 2.08/100,000 population/year. Structural complications of intermediate uveitides include macular edema, epiretinal membrane formation, and, uncommonly, retinal neovascularization of either the disc or the snowbank. Anterior chamber inflammation typically is mild, and the eye is not acutely inflamed. Typical presenting symptoms are either floaters or blurred vision, most often due to macular edema.
The SUN Working Group is an international collaboration, which has developed classification criteria for 25 of the most common uveitides by using a formal approach to development and classification. , Among the intermediate uveitides studied was pars planitis.
METHODS
The SUN Developing Classification Criteria for the Uveitides project proceeded in 4 phases, as previously described: 1) informatics; 2) case collection; 3) case selection; and 4) machine learning. ,
Informatics
As previously described, the consensus-based informatics phase permitted development of a standardized vocabulary and development of a standardized, menu-driven hierarchical case collection instrument.
Case Collection and Case Selection
Information was entered into the SUN preliminary database by the 76 contributing investigators for each disease as previously described. , Cases in the preliminary database were reviewed by committees of 9 investigators for selection into the final database, using the formal consensus techniques described in the accompanying article. , Because the goal was to develop classification criteria, only cases with a supermajority agreement (>75%) that the case was the disease in question were retained in the final database (ie, were “selected”).
Machine Learning
The final database then was randomly separated into a training set (∼85% of cases) and a validation set (∼15% cases) for each disease, as described in the accompanying article. Machine learning was used in the training set to determine criteria that minimized misclassification. The criteria then were tested in the validation set; for both the training set and the validation set, the misclassification rate was calculated for each disease. The misclassification rate was the proportion of cases classified incorrectly by the machine learning algorithm compared to the consensus diagnosis. For pars planitis, the diseases against which it was evaluated were MS-associated intermediate uveitis; intermediate uveitis, non-pars planitis type (undifferentiated intermediate uveitis); sarcoidosis-associated intermediate uveitis; and syphilitic intermediate uveitis. There were too few cases of Lyme disease-associated uveitis collected in the data base for analysis by machine learning.
Comparison of Cases With and Without Snowbanks
Comparison of the characteristics of cases with and without snowbanks was performed using the χ 2 test for categorical variables or the Fisher exact test when the count of a variable was less than 5. Continuous variables were summarized as medians and compared using the Wilcoxon rank sum test. For characteristics with multiple categorical grades, values above and below the median were compared. P values were nominal and 2-sided.
The study adhered to the principles of the Declaration of Helsinki. Institutional Review Boards (IRBs) at each participating center reviewed and approved the study; the study typically was considered either a minimal risk or exempt by the individual IRBs.
RESULTS
A total of 308 cases of pars planitis were collected, and 226 cases (73%) achieved supermajority agreement on the diagnosis during the “selection” phase and were used in the machine learning phase. Those cases of pars planitis were compared to 363 cases of other intermediate uveitides including 112 cases of MS-associated intermediate uveitis; 114 cases of intermediate uveitis, non-pars planitis type; 52 cases of sarcoidosis-associated intermediate uveitis; and 85 cases of syphilitic intermediate uveitis. Details of the machine learning results for these diseases are outlined in the accompanying article. The characteristics at presentation to a SUN Working Group investigator of cases with pars planitis are listed in Table 1 . A comparison between cases with and without snowbanks is listed in Table 2 . The only significant differences between those with snowbanks and those without snowbanks were that those with snowbanks were younger. The criteria developed after machine learning are listed in Table 3 . The overall accuracy for
| Characteristics | Result |
|---|---|
| Number of cases | 226 |
| Demographics | |
| Median IQR age [25th, 75th percentile) | 22 [11, 36] |
| Sex (%) | |
| Men | 48 |
| Women | 52 |
| Race/ethnicity, % | |
| White, non-Hispanic | 72 |
| Black, non-Hispanic | 5 |
| Hispanic | 6 |
| Asian, Pacific Islander | 3 |
| Other | 6 |
| Missing | 8 |
| Uveitis history | |
| Uveitis course, % | |
| Acute, monophasic | 2 |
| Acute, recurrent | 2 |
| Chronic | 87 |
| Indeterminate | 9 |
| Laterality, % | |
| Unilateral | 15 |
| Unilateral, alternating | 0 |
| Bilateral | 85 |
| Ophthalmic examination | |
| Keratic precipitates, % | |
| None | 83 |
| Fine | 15 |
| Round | 2 |
| Stellate | 0 |
| Mutton fat | 0 |
| Other | 0 |
| Anterior chamber cells, % | |
| Grade 0 | 44 |
| ½+ | 27 |
| 1+ | 19 |
| 2+ | 9 |
| 3+ | 1 |
| 4+ | 0 |
| Hypopyon, % | 0 |
| Anterior chamber flare, % | |
| Grade 0 | 75 |
| 1+ | 21 |
| 2+ | 3 |
| 3+ | 1 |
| 4+ | 0 |
| Iris (%) | |
| Normal | 88 |
| Posterior synechiae | 12 |
| Sectoral iris atrophy | 0 |
| Patchy iris atrophy | 0 |
| Diffuse iris atrophy | 0 |
| Heterochromia | 0 |
| IOP of the involved eyes | |
| Median IQR, mm Hg [25 th , 75 th percentile] | 14 [12, 17] |
| Proportion of patients with IOP >24 mm Hg in either eye, % | 4 |
| Vitreous cells (%) * | |
| Grade 0 | 4 |
| ½+ | 8 |
| 1+ | 35 |
| 2+ | 39 |
| 3+ | 13 |
| 4+ | 1 |
| Vitreous haze (%) * | |
| Grade 0 | 31 |
| ½+ | 15 |
| 1+ | 27 |
| 2+ | 23 |
| 3+ | 3 |
| 4+ | 1 |
| Vitreous snowballs † | 83 |
| Pars plana snowbanks † | 44 |
| Peripheral retinal vascular sheathing or leakage | 25 |
| Macular edema | 43 |
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