Purpose
To determine classification criteria for Fuchs’ uveitis syndrome.
Design
Machine learning of cases with Fuchs’ uveitis syndrome and 8 other anterior uveitides.
Methods
Cases of anterior 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 anterior uveitides. The resulting criteria were evaluated on the validation set.
Results
One thousand eighty-three cases of anterior uveitides, including 146 cases of Fuchs’ uveitis syndrome, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for Fuchs’ uveitis syndrome included unilateral anterior uveitis with or without vitritis and either: 1) heterochromia or 2) unilateral diffuse iris atrophy and stellate keratic precipitates. The misclassification rates for Fuchs’ uveitis syndrome were 4.7% in the training set and 5.5% in the validation set, respectively.
Conclusions
The criteria for Fuchs’ uveitis syndrome had a low misclassification rate and appeared to perform well enough for use in clinical and translational research.
F uchs uveitis syndrome, also known as Fuchs heterochromic iridocyclitis, was described by Fuchs in 1906. In a case series of patients with uveitis, Fuchs uveitis syndrome accounted for 1%-3% of cases. Patients present with the insidious onset of floaters and/or glare and decreased vision due to cataract formation, or may be asymptomatic and had uveitis detected on routine examination. Typical features of the Fuchs uveitis syndrome include anterior chamber inflammation, characteristic stellate keratic precipitates, and iris atrophy, most often resulting in heterochromia; vitritis also may be present. When heterochromia is present, the involved eye appears “bluer,” but heterochromia may be difficult to assess in patients with dark brown irides. Posterior synechiae and peripheral anterior synechiae do not occur and suggest an alternative diagnosis. The uveitis follows a chronic course and is unilateral in nearly all cases. Elevated intraocular pressure often occurs, but typically it is not present at the initial visit. Nevertheless, with follow-up it has been estimated that more than 50% of patients with Fuchs uveitis syndrome will develop elevated intraocular pressure. Over time posterior subcapsular cataracts develop in more than 80% of eyes. , , Correct identification of Fuchs uveitis syndrome is important for management, as corticosteroid therapy makes little difference to the outcome and typically is not needed. Furthermore, because posterior synechiae do not form and the uveitis is not painful, cycloplegia is not needed. ,
The Standardization of Uveitis Nomenclature (SUN) Working Group is an international collaboration which has developed classification criteria for 25 of the most common uveitides. One of the diseases for which classification criteria were developed was the Fuchs uveitis syndrome.
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
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 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% of cases) for each disease as described in the accompanying article. Machine learning was used on 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 Fuchs uveitis syndrome, the diseases against which it was evaluated were: cytomegalovirus (CMV) anterior uveitis; herpes simplex virus (HSV) anterior uveitis; varicella zoster virus (VZV) anterior uveitis; juvenile idiopathic arthritis (JIA)-associated anterior uveitis; spondyloarthritis/HLA-B27-associated anterior uveitis; tubulointerstitial nephritis with uveitis (TINU); sarcoidosis-associated anterior uveitis; and syphilitic anterior uveitis.
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 249 cases of Fuchs uveitis syndrome were collected, and 146 cases (59%) achieved supermajority agreement on the diagnosis during the “selection” phase and were used in the machine learning. These cases of Fuchs uveitis syndrome were compared to cases of other anterior uveitides, including 89 cases of CMV anterior uveitis, 123 cases of VZV anterior uveitis, 184 cases of spondyloarthritis/HLA-B27-associated anterior uveitis, 202 cases of JIA-associated anterior uveitis, 101 cases of HSV anterior uveitis, 94 cases of TINU, 112 cases of sarcoidosis-associated anterior uveitis, and 32 cases of syphilitic anterior 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 Fuchs uveitis syndrome are listed in Table 1 . The relatively low proportion of cases with elevated intraocular pressure likely related to the fact that data were collected for the initial presentation and not over time. The criteria developed after machine learning are listed in Table 2 . Key clinical features for diagnosing Fuchs included evidence of an anterior uveitis with or without an accompanying vitritis, and either heterochromia ( Figure 1 ) or both stellate keratic precipitates ( Figure 2 ) and unilateral diffuse iris atrophy in the affected eye. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval: 92.4-98.6). The misclassification rate for Fuchs uveitis syndrome in the training set was 4.7% and 5.5% in the validation set. The disease with which it most often was confused was HSV anterior uveitis.
| Characteristic | Result |
|---|---|
| Number cases | 146 |
| Demographics | |
| Age, median, years (25 th 75 th percentile) | 35 (27, 45) |
| Age category, years (%) | |
| ≤16 | 5 |
| 17-50 | 82 |
| 51-60 | 8 |
| >60 | 5 |
| Gender (%) | |
| Men | 51 |
| Women | 49 |
| Race/ethnicity (%) | |
| White, non-Hispanic | 75 |
| Black, non-Hispanic | 0 |
| Hispanic | 3 |
| Asian, Pacific Islander | 11 |
| Other | 7 |
| Missing | 4 |
| Uveitis History | |
| Uveitis course (%) | |
| Acute, monophasic | 0 |
| Acute, recurrent | 0 |
| Chronic | 92 |
| Indeterminate | 8 |
| Laterality (%) | |
| Unilateral | 98 |
| Unilateral, alternating | 0 |
| Bilateral | 2 |
| Ophthalmic examination | |
| Cornea | |
| Normal | 99 |
| Keratitis | 1 |
| Keratic precipitates (%) | |
| None | 1 |
| Fine | 25 |
| Round | 7 |
| Stellate | 68 |
| Mutton Fat | 0 |
| Other | 0 |
| Anterior chamber cells (%) | |
| Grade ½+ | 49 |
| 1+ | 26 |
| 2+ | 10 |
| 3+ | 1 |
| 4+ | 0 |
| Hypopyon (%) | 0 |
| Anterior chamber flare (%) | |
| Grade 0 | 66 |
| 1+ | 32 |
| 2+ | 1 |
| 3+ | 0 |
| 4+ | 0 |
| Iris (%) | |
| Normal | 6 |
| Posterior synechiae | 0 |
| Sectoral iris atrophy | 0 |
| Patchy iris atrophy | 3 |
| Diffuse iris atrophy | 45 |
| Heterochromia | 76 |
| Intraocular pressure (IOP), involved eyes | |
| Median, mm Hg (25 th , 75 th percentile) | 14 (12, 16) |
| Proportion patients with IOP>24 mm Hg either eye (%) | 8 |
| Vitreous cells (%) | |
| Grade 0 | 25 |
| ½+ | 25 |
| 1+ | 32 |
| 2+ | 16 |
| 3+ | 3 |
| 4+ | 0 |
| Vitreous haze (%) | |
| Grade 0 | 49 |
| ½+ | 13 |
| 1+ | 24 |
| 2+ | 13 |
| 3+ | 1 |
| 4+ | 0 |
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