Classification Criteria for Sarcoidosis-Associated Uveitis





Purpose


The purpose of this study was to determine classification criteria for sarcoidosis-associated uveitis.


Design


Machine learning of cases with sarcoid uveitis and 15 other uveitides.


Methods


Cases of anterior, intermediate, and panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed including cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were analyzed by anatomic class, and each class was split into a training set and a validation set. Machine learning using multinomial logistic regression was used in the training sets to determine a parsimonious set of criteria that minimized the misclassification rate among the uveitides. The resulting criteria were evaluated in the validation sets.


Results


A total of 1,083 cases of anterior uveitides, 589 cases of intermediate uveitides, and 1,012 cases of panuveitides, including 278 cases of sarcoidosis-associated uveitis, were evaluated by machine learning. Key criteria for sarcoidosis-associated uveitis included a compatible uveitic syndrome of any anatomic class and evidence of sarcoidosis, either 1) tissue biopsy results demonstrating non-caseating granulomata or 2) bilateral hilar adenopathy on chest imaging. The overall accuracy of the diagnosis of sarcoidosis-associated uveitis in the validation set was 99.7% (95% confidence interval: 98.8-99.9). The misclassification rates for sarcoidosis-associated uveitis in the training sets were 3.2% in anterior uveitis, 2.6% in intermediate uveitis, and 1.2% in panuveitis; in the validation sets, the misclassification rates were 0% in anterior uveitis, 0% in intermediate uveitis, and 0% in panuveitis.


Conclusions


The criteria for sarcoidosis-associated uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.


T he American Thoracic Society, the European Respiratory Society, and the World Association of Sarcoidosis and Other Granulomatous Diseases have defined sarcoidosis as a multisystem disease of unknown causes characterized by granuloma formation and with a predilection for pulmonary involvement. They further note that “the presence of non-caseating granulomata in a single organ…does not establish the diagnosis of sarcoidosis,” and that the diagnosis of sarcoidosis requires a compatible clinical syndrome. Sarcoidosis is present worldwide. In the United States, the incidence has been estimated at 5.9/100,000 population/year for men and 6.3/100,000 population/year for women. In the United States, sarcoidosis is more common among African Americans than whites. The cumulative lifetime risk has been estimated at 0.85% for whites and 2.4% for blacks, and the prevalence as 10.9/100,000 population for whites and 35.5/100,000 population for blacks. Pulmonary disease is the most common abnormality, with bilateral hilar adenopathy the most characteristic feature on chest imaging (either chest radiography or computed tomography [CT]) and parenchymal lung disease having the most negative effect on pulmonary function. In multidisciplinary clinical settings, pulmonary involvement is seen in ∼85%-95% of patients. Involvement of the liver, spleen, or lymph nodes is reported in 25%-35% of patients and in 12%-25% of the skin. The presence of erythema nodosum is reported in 4%-30% of cases but is not specific for a diagnosis of sarcoidosis, as it occurs with other diseases. Neurologic involvement is present in only ∼5%. It is likely that some of that variation represents regional and racial and ethnic variation and that some of the variation represents referral bias. Ocular disease typically is reported as present in ∼12%-25% of patients with documented sarcoidosis, with variable frequencies reported depending on the extent of examination (eg, whether aqueous tear deficiency is evaluated). , Uveitis typically is the most common ocular manifestation of ocular sarcoidosis. In a population-based study in Olmstead County, Minnesota, USA, 7% of patients with sarcoidosis had ocular involvement, uveitis was the most common form of ocular sarcoid (61%), and anterior uveitis (71% of uveitis) was the most common anatomic class of uveitis. Conversely, sarcoidosis-associated uveitis accounts for ∼5%-10% of uveitis presenting to tertiary care eye centers in the United States. , ,


Although anterior uveitis is the most common anatomic class of uveitis seen with sarcoidosis-associated uveitis in the United States, any anatomic class of uveitis may be seen with sarcoidosis, including intermediate, a mixed anterior and intermediate type, posterior, and panuveitis, , and in some parts of the world, intermediate uveitis and panuveitis may be more common. , Vitreous inflammatory manifestations include “snowballs” and “string of pearls” inflammatory debris. Posterior segment clinical findings include choroidal nodules, optic nerve nodules, multifocal choroiditis, and perivascular sheathing (eg, “candle wax drippings”), occasionally with vascular occlusion. , Among patients with sarcoidosis-associated uveitis, the reported frequencies of ocular manifestations typically are 65%-70% anterior uveitis; 11%-16% iris nodules; 3%-25% vitritis; 10%-17% periphlebitis; 4%-5% paucifocal, typically elevated, choroidal nodules (sometimes inappropriately termed “sarcoid granulomas”); and ∼11% multifocal choroiditis. Among patients with sarcoidosis-associated anterior uveitis, both acute anterior uveitis and chronic anterior uveitis have been reported.


The Standardization of Uveitis Nomenclature (SUN) Working Group is an international collaboration, which has developed classification criteria for the leading 25 uveitides using a formal approach to development and classification. Among the uveitides studied was sarcoidosis-associated uveitis.


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 the development of a standardized vocabulary and the development of a standardized, menu-driven hierarchical case collection instrument.


Case Collection and Case Selection


De-identified 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 formal consensus techniques described in the accompanying articles. , 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 was analyzed by anatomic class; cases for each class were randomly separated into a training set (∼85% of the cases) and a validation set (∼15% of the cases) for each disease, as described in the accompanying article. Relevant cases of sarcoidosis-associated uveitis were analyzed in the anterior uveitides, intermediate uveitides, and panuveitides. Machine learning was used in the training sets to determine criteria that minimized misclassification. The criteria then were tested in the validation sets; for both the training sets and the validation sets, 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.


Cases of sarcoidosis-associated anterior, intermediate, and panuveitis were evaluated in the machine learning for anterior uveitides (cytomegalovirus anterior uveitis, herpes simplex virus anterior uveitis, juvenile idiopathic arthritis-associated anterior uveitis, syphilitic anterior uveitis, spondyloarthritis/HLA-B7-associated anterior uveitis, tubulointerstitial nephritis with uveitis, and varicella zoster virus anterior uveitis), intermediate uveitides (multiple-sclerosis-associated intermediate uveitis, pars planitis, intermediate uveitis, non-pars planitis type, and syphilitic intermediate uveitis), and panuveitides (Behçet disease, syphilitic panuveitis, sympathetic ophthalmia, Vogt-Koyanagi-Harada disease, tuberculous panuveitis), respectively. Although “isolated” posterior sarcoidosis-associated uveitis cases were included in the machine learning of posterior uveitides, there were too few cases (n = 12) for reliable statistical inferences.


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 minimal risk or exempt by the individual IRBs.


RESULTS


A total of 383 cases of sarcoidosis-associated uveitis were collected, and 278 cases (73%) achieved supermajority agreement in the diagnosis during the “selection” phase and were used in the machine learning phase. They were compared to 971 other anterior uveitides, 537 other intermediate uveitides, and 910 other panuveitides. Details of the machine learning results for these diseases are outlined in the accompanying article. The characteristics of cases with sarcoid-associated uveitis are listed in Table 1 . Biopsy confirmation of the diagnosis of sarcoidosis was obtained in 58%, and 79% had bilateral hilar adenopathy on chest imaging. Bilateral hilar adenopathy was detected in 72% of 242 cases with reported chest radiography results and 82% of 164 cases with reported chest CT scan results. Of 156 cases with both chest radiography and chest CT results reported, 116 had bilateral hilar adenopathy on both imaging modalities, 24 cases had no evidence of bilateral hilar adenopathy of both imaging modalities, and 16 cases had bilateral hilar adenopathy identified on chest CT imaging but not chest radiography. The characteristics of cases of sarcoid-associated uveitis by anatomic class are listed in Table 2 . The criteria developed after machine learning are listed in Table 3 . The key features of the criteria are a compatible uveitic syndrome and evidence of sarcoidosis. Compatible uveitic syndromes included anterior uveitis ( Figure 1 ), intermediate uveitis ( Figure 2 ), posterior uveitis with either focal choroidal nodule ( Figure 3 ) or multifocal choroiditis ( Figure 4 ), and panuveitis with either choroiditis or retinal vascular sheathing ( Figure 5 ) and/or occlusion. Evidence of sarcoidosis was either biopsy results demonstrating non-caseating granulomata or chest imaging (either chest radiography or chest CT) demonstrating bilateral hilar adenopathy. The overall accuracies by anatomic class were anterior uveitides, training set 97.5% and validation set 96.7% (95% confidence interval [CI]: 92.4-98.6); intermediate uveitides, training set 99.8% and validation set 99.3% (95% CI: 96.1-99.9); and panuveitides, training set 96.3% and validation set 94.0% (95% CI: 89.0-96.8). The overall accuracy of the diagnosis of sarcoidosis-associated uveitis in the validation set was 99.6% (95% CI: 98.8-99.9). The following misclassification rates for sarcoid-associated uveitis in the training set were: 3.2% against anterior uveitides, 2.6% intermediate uveitides, and 1.2% non-infectious panuveitides. There were too few cases of isolated posterior sarcoidosis-associated uveitis for formal testing, although they were included in the testing against the other diseases. In the validation set, the misclassification rates were 0% against anterior uveitides, 0% intermediate uveitides, and 0% non-infectious panuveitides.



Table 1

Characteristics of Patients With Sarcoid Uveitis



































































































































































































































































Characteristic Result
Number of cases 278
Demographics
Median IQR (25th 75th), y 49 (39, 61)
Men, % 29
Women, % 71
Race/ethnicity, %
White, non-Hispanic 37
Black, non-Hispanic 26
Hispanic 1
Asian, Pacific Islander 24
Other 9
Missing 3
Uveitis history
Uveitis course, %
Acute, monophasic 5
Acute, recurrent 7
Chronic 80
Indeterminate 8
Laterality, %
Unilateral 18
Unilateral, alternating 1
Bilateral 82
Ophthalmic examination
Keratic precipitates, %
None 52
Fine 18
Round 6
Stellate 0
Mutton-fat 23
Anterior chamber cells, %
Grade 0 15
½+ 24
1+ 28
2+ 25
3+ 7
4+ 1
Hypopyon, % 1
Anterior chamber flare, %
Grade 0 60
1+ 30
2+ 9
3+ 1
4+ 0
Iris, %
Normal 64
Posterior synechiae 27
Iris nodules 12
Sectoral iris atrophy 0
Patchy iris atrophy 1
Diffuse iris atrophy 0
Heterochromia 0
IOP-involved eyes
Median IQR (25th, 75th), mm Hg 16 (13, 19)
Proportion of patients with IOP>24 mm Hg in either eye, % 10
Vitreous cells, %
Grade 0 31
½+ 21
1+ 31
2+ 14
3+ 3
4+ 0
Vitreous haze, %
Grade 0 61
½+ 11
1+ 20
2+ 5
3+ 2
4+ 0
Vitreous snowballs, % 17
Pars plana snowbanks, % 1
Choroidal nodule, % 2
Multifocal choroiditis, % 30
Retinal vascular sheathing, % 18
Anatomic class, %
Anterior uveitis 40
Intermediate uveitis 19
Posterior uveitis 4
Panuveitis 37
Evidence of sarcoidosis, %
Non-caseating granuloma on tissue biopsy a 58
Bilateral hilar adenopathy of chest imaging b 79
Non-specific tests for sarcoidosis, %
ACE 52
Lysozyme 12

ACE = angiotensin-converting enzyme; IOP = intraocular pressure; IQR = interquartile range.

a 161 of 161 patients’ biopsy results were positive, demonstrating non-caseating granulomata.


b 174 of 242 patients (72%) had a chest radiograph with bilateral hilar adenopathy, and 134 of 164 patients (82%) undergoing computerized tomography had bilateral hilar adenopathy.



Table 2

Characteristics of Sarcoid Uveitis by Anatomic Class of the Uveitis












































































































































































































































































































































































































































































Characteristic/Anatomic Class Anterior Uveitis Intermediate Uveitis Posterior Uveitis Panuveitis
Number of cases 112 52 12 102
Demographics
Median IQR (25th 75th) age, y 46 (37, 55) 52 (43, 67) 53 (50, 64) 51 (35, 63)
Men, % 24 29 33 33
Women, % 76 71 67 67
Race/ethnicity, %
White, non-Hispanic 30 63 42 31
Black, non-Hispanic 49 6 0 15
Hispanic 0 2 0 2
Asian, Pacific Islander 7 13 33 45
Other 7 12 25 4
Missing 7 4 0 3
Uveitis history
Uveitis course, %
Acute, monophasic 10 0 0 3
Acute, recurrent 14 2 0 3
Chronic 63 96 92 78
Indeterminate 13 2 8 16
Laterality, %
Unilateral 24 19 42 7
Unilateral, alternating 2 0 0 0
Bilateral 74 81 58 93
Ophthalmic examination
Keratic precipitates, %
None 46 75 92 43
Fine 19 15 8 20
Round 8 0 0 8
Stellate 1 0 0 0
Mutton-fat 27 10 0 29
Anterior chamber cells, %
Grade 0 4 35 75 12
½+ 25 27 17 24
1+ 32 13 8 32
2+ 30 19 0 24
3+ 7 6 0 8
4+ 2 0 0 1
Hypopyon, % 1 0 0 0
Anterior chamber flare, %
Grade 0 63 81 100 42
1+ 29 15 0 39
2+ 6 2 0 18
3+ 2 0 0 1
4+ 0 2 0 0
Iris, %
Normal 61 60 100 66
Posterior synechiae 33 29 0 23
Iris nodules 13 10 0 16
Sectoral iris atrophy 0 0 0 0
Patchy iris atrophy 1 2 0 2
IOP-involved eyes
Median IQR (25th, 75th) mm Hg 16 (13, 19) 16 (14, 18) 15 (14, 17) 16 (13, 18)
Percentage of patients with IOP >24 mm Hg in either eye 8 8 0 16
Vitreous cells, %
Grade 0 55 10 25 17
½+ 27 21 17 14
1+ 14 52 33 39
2+ 3 15 25 24
3+ 1 2 0 6
Vitreous haze, %
Grade 0 86 46 42 44
½+ 6 17 17 14
1+ 5 29 33 28
2+ 1 4 8 11
3+ 1 4 0 3
Vitreous snowballs, % 0 58 8 26
Pars plana snowbanks, % 0 4 0 0
Choroidal nodule, % 0 0 17 5
Multifocal choroiditis, % 0 0 92 73
Retinal vascular sheathing, % 0 27 49 28
Evidence of sarcoidosis, %
Non-caseating granuloma on tissue biopsy 60 54 58 59
Bilateral hilar adenopathy of chest imaging 82 85 75 74
Non-specific tests for sarcoidosis, %
ACE 45 51 58 59
Lysozyme 14 0 0 17

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Nov 5, 2021 | Posted by in OPHTHALMOLOGY | Comments Off on Classification Criteria for Sarcoidosis-Associated Uveitis

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