Abstract
Purpose:
To determine classification criteria for tubulointerstitial nephritis with uveitis (TINU)
Design:
Machine learning of cases with TINU 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 94 cases of TINU, 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 TINU included anterior chamber inflammation and evidence of tubulointerstitial nephritis with either 1) a positive renal biopsy or 2) evidence of nephritis (elevated serum creatinine and/or abnormal urine analysis) and an elevated urine β-2 microglobulin. The misclassification rates for TINU were 1.2% in the training set and 0% in the validation set, respectively.
Conclusions:
The criteria for TINU had a low misclassification rate and appeared to perform well enough for use in clinical and translational research.
PRECIS
Using a formalized approach to developing classification criteria, including informatics-based case collection, consensus-technique-based case selection, and machine learning, classification criteria for tubulointerstitial nephritis with uveitis were developed. Key criteria included anterior chamber inflammation and evidence of tubulointerstitial nephritis, either on renal biopsy or with an elevated urine β-2 microglobulin. The resulting criteria had a low misclassification rate.
The syndrome of tubulointerstitial nephritis with uveitis (TINU) was first described as a distinct entity in 1975.1 It is considered a rare condition with approximately 200 cases described in the literature through 2018.2–10 Tubulointerstitial nephritis with uveitis accounts for 0.2 to 2% of case series of uveitis, but it accounts for ~10% to 20% of cases presenting with bilateral simultaneous acute anterior uveitis.2,3,5 Over 80% of cases present as an anterior uveitis, and 77% are bilateral at presentation.2 Retinal (e.g. macular edema) and optic nerve (e.g. disc edema) structural complications of the uveitis in TINU may occur, but in addition an anterior/intermediate uveitis and a panuveitis with either small choroidal lesions or retinal vascular findings (e.g. cotton wool spots, vascular sheathing, intraretinal hemorrhages) have been described.2,3,8 Although the review by Mandeville et al described posterior findings in 17% of cases, 100% of cases had evidence of an anterior segment inflammation (anterior chamber cells and flare). Although typically presenting as an acute-onset anterior uveitis, chronic disease requiring long-term therapy, including immunosuppression, may occur.2,5,9
The syndrome of TINU is one of many diseases with tubulointerstitial nephritis (TIN) as the renal disease manifestation. The most commonly reported etiology for TIN is drug reaction with antibiotics, non-steroidal anti-inflammatory drugs, and proton pump inhibitors most often implicated. Other rheumatic diseases, such as systemic lupus erythematosus, Sjögren syndrome, systemic vasculitis, and IgG4 disease also may have TIN as their renal manifestation.7 Despite the implication of drug reaction with TIN in general, none of the cases of TINU reported by Mackensen et al3 appeared to be drug-related, suggesting that TINU may be distinct from drug-induced TIN.
Definitive diagnosis of TIN is made on renal biopsy.2,4,6,7 However, renal biopsy is not always performed, especially when the renal disease is mild. Other renal laboratory findings reported in TINU include elevated serum creatinine in ~90%,2 abnormal urine analysis, and elevated urine β2-microglobulin. Urinary abnormalities include proteinuria in ~78 to 86%, microscopic hematuria in ~42%, and aseptic leukocyturia in ~55 to 70%.2,4 Elevated urine β2-microglobulin has been reported to be present in nearly all patients tested at presentation and may correlate with the activity of the disease.2,,4,10 Signs and symptoms of a systemic illness are reported in slightly over one-half of cases, including fever, fatigue and malaise, and weight loss, but are non-specific.2,4
The Standardization of Uveitis Nomenclature (SUN) Working Group is an international collaboration, which has developed classification criteria for 25 of the most common uveitides using a formal approach to development and classification.11–17 Among the anterior uveitides studied was TINU.
Methods
The SUN Developing Classification Criteria for the Uveitides project proceeded in four phases as previously described: 1) informatics, 2) case collection, 3) case selection, and 4) machine learning.12,14,15
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.12
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.5,7 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.15,17 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 (i.e. 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.17 Machine learning was used on the training set to determine criteria that minimized misclassification. The criteria then were tested on 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 when compared to the consensus diagnosis. For TINU, 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, spondylitis/HLA-B27-associated anterior uveitis, Fuchs uveitis syndrome, sarcoidosis-associated anterior uveitis and syphilitic anterior uveitis.
Comparison of cases with and without a renal biopsy result reported.
Comparison of the characteristics of cases with and without renal biopsy results reported was performed with the chi-square test for categorical variables or the Fisher’s exact test when the count of a variable was less than 5. Continuous variables were summarized as medians and compared with the Wilcoxon rank sum test.
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
One hundred twenty-five cases of TINU were collected, and 94 (75%) achieved supermajority agreement on the diagnosis during the “selection” phase and were used in the machine learning phase. These cases of TINU uveitis were compared to 989 cases of other anterior uveitides, including 89 cases of CMV anterior uveitis, 101 cases of HSV anterior uveitis, 146 cases of Fuchs Uveitis Syndrome, 202 cases of JIA-associated anterior uveitis, 184 cases of spondylitis/HLA-B27-associated anterior uveitis, 123 cases of VZV anterior uveitis, 112 cases of sarcoidosis-associated anterior uveitis, and 32 cases of syphilitic anterior uveitis. The characteristics of cases with TINU at presentation to a SUN Working Group Investigator are listed in Table 1. A comparison of cases with and without renal biopsy data reported is listed as Table 2. There were no significant differences between the two groups in the clinical characteristics of the uveitis. However, cases without a biopsy were younger, particularly <16 years of age. Patients without a biopsy were significantly more likely to have an elevated urine β-2-microglobulin reported, suggesting that it may be substituting for a renal biopsy in some patients or that when a positive biopsy is obtained, the test was deemed unnecessary or not reported.
Table 1.
Characteristics of Cases with Tubulointerstitial Nephritis with Uveitis
| Characteristic | Result |
|---|---|
| Number cases | 94 |
| Demographics | |
| Age, median, years (25th 75th percentile) | 17 (13, 42) |
| Age category, years (%) | |
| ≤16 | 46 |
| 17–50 | 33 |
| 51–59 | 7 |
| ≥60 | 12 |
| Missing | 2 |
| Gender (%) | |
| Men | 36 |
| Women | 64 |
| Race/ethnicity (%) | |
| White, non-Hispanic | 70 |
| Black, non-Hispanic | 5 |
| Hispanic | 7 |
| Asian, Pacific Islander | 4 |
| Other | 4 |
| Missing/unknown | 8 |
| Uveitis History | |
| Uveitis course (%) | |
| Acute, monophasic | 14 |
| Acute, recurrent | 6 |
| Chronic | 60 |
| Indeterminate | 20 |
| Laterality (%) | |
| Unilateral | 14 |
| Unilateral, alternating | 0 |
| Bilateral | 86 |
| Ophthalmic examination | |
| Cornea | |
| Normal | 100 |
| Keratitis | 0 |
| Keratic precipitates (%) | |
| None | 49 |
| Fine | 40 |
| Round | 4 |
| Stellate | 2 |
| Mutton Fat | 4 |
| Other | 0 |
| Anterior chamber cells (%) | |
| Grade ½+ | 16 |
| 1+ | 30 |
| 2+ | 31 |
| 3+ | 16 |
| 4+ | 7 |
| Hypopyon (%) | 0 |
| Anterior chamber flare (%) | |
| Grade 0 | 55 |
| 1+ | 28 |
| 2+ | 14 |
| 3+ | 2 |
| 4+ | 1 |
| Iris (%) | |
| Normal | 68 |
| Posterior synechiae | 32 |
| Sectoral iris atrophy | 0 |
| Patchy iris atrophy | 0 |
| Diffuse iris atrophy | 0 |
| Heterochromia | 0 |
| Intraocular pressure (IOP), involved eyes | |
| Median, mm Hg (25th, 75th percentile) | 14 (12, 17) |
| Proportion patients with IOP>24 mm Hg either eye (%) | 1 |
| Vitreous cells (%) | |
| Grade 0 | 30 |
| ½+ | 27 |
| 1+ | 32 |
| 2+ | 5 |
| 3+ | 6 |
| 4+ | 0 |
| Vitreous haze (%) | |
| Grade 0 | 81 |
| ½+ | 7 |
| 1+ | 4 |
| 2+ | 6 |
| 3+ | 1 |
| 4+ | 1 |
| Vitreous snowballs | 0 |
| Choroidal lesions | 2 |
| Laboratory (%) | |
| Elevated serum creatinine | 58 |
| Elevated serum creatinine among cases with results reported | 89 |
| Elevated urine β-2 microglobulin | 23 |
| Elevated urine β-2 microglobulin among cases with results reported | 88 |
| Abnormal urine analysis | 58 |
| Abnormal urine analysis among cases with results reported | 89 |
| Positive renal biopsy | 31 |
| Positive renal biopsy* among cases with biopsy results reported | 100 |
Abnormal renal biopsy present in 29/29 cases with biopsy results reported.
Table 2.
Characteristics of Cases of Tubulointerstitial Nephritis with Uveitis with and without Renal Biopsy Confirmation
| Characteristic | Positive Renal Biopsy | No Renal Biopsy | P-value |
|---|---|---|---|
| Number cases | 29 | 65 | |
| Demographics | |||
| Age, median, years (25th 75th percentile) | 41 (16, 55) | 16 (13, 30) | 0.004 |
| Age category, years (%) | 0.003 | ||
| ≤16 | 28 | 54 | |
| 17–50 | 31 | 34 | |
| 51–59 | 21 | 1 | |
| ≥60 | 21 | 11 | |
| Gender (%) | 0.49 | ||
| Men | 31 | 38 | |
| Women | 69 | 62 | |
| Race/ethnicity (%) | 0.76 | ||
| White, non-Hispanic | 79 | 69 | |
| Black, non-Hispanic | 7 | 5 | |
| Hispanic | 7 | 8 | |
| Asian, Pacific Islander | 0 | 6 | |
| Other | 0 | 7 | |
| Missing/unknown | 7 | 5 | |
| Uveitis History | |||
| Uveitis course (%) | 0.51 | ||
| Acute, monophasic | 7 | 15 | |
| Acute, recurrent | 7 | 8 | |
| Chronic | 72 | 54 | |
| Indeterminate | 14 | 23 | |
| Laterality (%) | 0.98 | ||
| Unilateral | 14 | 14 | |
| Bilateral | 86 | 86 | |
| Ophthalmic examination | |||
| Keratic precipitates (%) | 0.55 | ||
| None | 55 | 46 | |
| Fine | 31 | 45 | |
| Other | 14 | 9 | |
| Anterior chamber cells (%) | 0.10 | ||
| Grade ½+ | 17 | 14 | |
| 1+ | 48 | 22 | |
| 2+ | 24 | 34 | |
| 3+ | 7 | 20 | |
| 4+ | 3 | 9 | |
| Anterior chamber flare (%) | 0.59 | ||
| Grade 0 | 48 | 58 | |
| 1+ | 38 | 23 | |
| 2+ | 14 | 14 | |
| 3+ | 0 | 3 | |
| 4+ | 0 | 2 | |
| Iris (%) | |||
| Normal | 76 | 57 | 0.08 |
| Posterior synechiae | 21 | 37 | 0.12 |
| Intraocular pressure (IOP), involved eyes | |||
| Median, mm Hg (25th, 75th percentile) | 15 (13, 17) | 14 (12, 16) | 0.07 |
| Percent patients with IOP>24 mm Hg either eye | 3 | 0 | 0.43 |
| Vitreous cells (%) | 0.44 | ||
| Grade 0 | 35 | 28 | |
| ½+ | 35 | 23 | |
| 1+ | 27 | 34 | |
| 2+ | 0 | 8 | |
| 3+ | 3 | 8 | |
| Vitreous haze (%) | 0.63 | ||
| Grade 0 | 86 | 79 | |
| ½+ | 10 | 6 | |
| 1+ | 0 | 6 | |
| 2+ | 3 | 8 | |
| 3+ | 1 | 0 | |
| Laboratory (% * ) | |||
| Elevated serum creatinine | 76 | 51 | 0.06 |
| Abnormal urine analysis | 62 | 57 | 0.59 |
Data on urine β-2-microglobulin either not performed or not reported in all cases with positive renal biopsy.
The criteria developed after machine learning are listed in Table 3. The key features are the presence of an anterior uveitis and evidence of TIN. Although an anterior/intermediate or panuveitis may be present, anterior chamber inflammation should be present. Tubulointerstitial nephritis is best diagnosed by renal biopsy, but TIN can be inferred with appropriate other renal/urinary findings. 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).17 The misclassification rate for TINU in the training set was 1.2% and in the validation set 0%.
Table 3.
Classification Criteria for Tubulointerstitial Nephritis with Uveitis Syndrome
| Criteria |
| 1. Evidence of anterior uveitis |
| a. anterior chamber cells |
| b. if vitritis or choroiditis or retinal vascular changes are present, anterior chamber inflammation also should be present |
| AND |
| 2. Evidence of tubulointerstitial nephritis, either |
| a. Positive renal biopsy OR |
| b. Elevated urine β-microglobulin and either abnormal urine analysis or elevated serum creatinine |
| Exclusions |
| 1. Positive serology for syphilis using a treponemal test |
| 2. Evidence of sarcoidosis (either bilateral hilar adenopathy on chest imaging or tissue biopsy demonstrating non-caseating granulomata) |
Discussion
The classification criteria outlined in Table 3 appear to perform well with acceptably low misclassification rates.
The criteria selected herein are similar to those proposed by Mandeville et al,2 but do have differences: the SUN criteria are simpler, eliminate the concepts of probable and possible TINU, and do not include the non-specific characteristics of fever, weight loss, fatigue and malaise, etc. Nevertheless, the SUN Criteria for TINU appear to perform acceptably well with a low misclassification rate.
Although histological evidence of TIN on renal biopsy is the definitive method of diagnosing TIN, a renal biopsy may not always be performed. Therefore, other laboratory evidence of TIN used to make the diagnosis was included in the criteria. The comparison of cases with and without renal biopsy confirmation revealed no substantial differences other than the younger age of cases without a biopsy and the apparent use of urinary β-2-microglobulin for diagnosis in cases without a biopsy. The retrospective nature of the SUN data collection did not permit the evaluation of the rate of urine β-2-microglobulin elevation among patients with a positive renal biopsy. Nevertheless, in a small case series by Goda et al,18 92% of cases renal biopsy-confirmed TINU had an elevated urine β-2-microglobulin, suggesting good overlap of these findings.
In small case series, TINU has been reported to have HLA-DQ and HLA-DR risk factor associations, particularly with HLA-DQA1*01, HLA-DQB1*05, and HLA-DRB1*01 with reported relative risks of ~16 to 26.19 HLA-DRB1*0102 has been reported to be associated with TINU and bilateral simultaneous acute anterior uveitis but not with TIN without uveitis, suggesting a possible genetic risk factor for the uveitis component.20 Our database did not have HLA data for TINU, so that we could not evaluate its usefulness. Nevertheless, given the relatively low frequency of TINU in uveitis series and even in the subset of bilateral simultaneous acute anterior uveitis, the positive predictive value of these alleles can be estimated21 to be in the 0.04 to 0.4 range (data not shown), and therefore may not contribute substantially to the diagnostic criteria at this time.
The presence of any of the exclusions in Table 3 suggests an alternate diagnosis, and the diagnosis of TINU should not be made in their presence. In prospective studies many of these tests will be performed routinely, and the alternative diagnoses excluded. However, in retrospective studies based on clinical care, not all of these tests may have been performed. Hence the presence of an exclusionary criterion excludes TINU, but the absence of such testing does not exclude the diagnosis of TINU if the criteria for the diagnosis are met.
Classification criteria are employed to diagnose individual diseases for research purposes.16 Classification criteria differ from clinical diagnostic criteria, in that although both seek to minimize misclassification, when a trade-off is needed, diagnostic criteria typically emphasize sensitivity, whereas classification criteria emphasize specificity,16 in order to define a homogeneous group of patients for inclusion in research studies and limit the inclusion of patients without the disease in question that might confound the data. The machine learning process employed did not explicitly use sensitivity and specificity; instead it minimized the misclassification rate. Because we were developing classification criteria and because the typical agreement between two uveitis experts on diagnosis is moderate at best,15 the selection of cases for the final database (“case selection”) included only cases which achieved supermajority agreement on the diagnosis. Therefore, it is possible that there may be some cases in clinical care that the clinician believes have TINU that will not meet classification criteria.
In conclusion, the criteria for TINU outlined in Table 3 appear to perform sufficiently well for use as classification criteria in clinical research.
Acknowledgments
Grant support: Supported by grant R01 EY026593 from the National Eye Institute, the National Institutes of Health, Bethesda, MD, USA; the David Brown Fund, New York, NY, USA; the Jillian M. And Lawrence A. Neubauer Foundation, New York, NY, USA; and the New York Eye and Ear Foundation, New York, NY, USA.
Footnotes
Writing committee: Douglas A. Jabs, MD, MBA2,3; Alastair K. Denniston, PhD, MRCP, FRCOphth4; Anat Galor, MD, MSPH5; Susan Lightman, FRCP (UK), PhD, FRCOphth6; Peter McCluskey, MD7; Neal Oden, PhD8; Alan G. Palestine, MD9; James T. Rosenbaum, MD10,11; Sophia M. Saleem, MD12; Jennifer E. Thorne, MD, PhD2,3; Brett E. Trusko, PhD, MBA.13
Affiliations: 1Members of the SUN Working Group are listed online at ajo.com. From 2the Department of Epidemiology, the Johns Hopkins University School of Public Health, and the Wilmer Eye Institute, the Department of Ophthalmology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA; 4the Academic Unit of Ophthalmology, University of Birmingham, Birmingham, UK; 5the Department of Ophthalmology, The University of Miami Miller School of Medicine, Miami, FL, USA; 6the Institute of Ophthalmology, University College London and Moorfields Eye Hospital, London, UK; 7the Save Sight Institute, Department of Ophthalmology, University of Sydney School of Medicine, Sydney, NSW, Australia; 8the Emmes Company, LLC, Rockville, MD, USA; 9the Department of Ophthalmology, University of Colorado School of Medicine, Aurora, Co, USA; 10the Departments of Medicine and Ophthalmology, Oregon Health and Science University, Portland, OR, USA; 11the Legacy Devers Eye Institute, Portland, OR, USA; 12the Department of Ophthalmology, the Icahn School of Medicine at Mount Sinai, New York, NY, USA; 13the Department of Medicine, Texas A&M University, College Station, TX, USA.
Conflict of Interest: Douglas A. Jabs: none; Alastair Denniston: none; Anat Galor: none; Susan Lightman: none; Peter McCluskey: none; Neal Oden: none; Alan G. Palestine: none; James T. Rosenbaum: consultant: AbbVie, Eyevensys, Gilead, Horizon, Janssen, Novartis, Roche, Santen, UCB; grant support: Pfizer; Sophia M. Saleem: 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.
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REFERENCES
- 1.Dobrin RS, Vernier RL, Fish AL. Acute eosinophilic interstitial nephritis and renal failure with bone marrow-lymph node granulomas and anterior uveitis. A new syndrome. Am J Med 1975;59:325–33. [DOI] [PubMed] [Google Scholar]
- 2.Mandeville JT, Levinson RD, Holland GN. The tubulointerstitial nephritis and uveitis syndrome. Surv Ophthalmol 2001;46:195–208. [DOI] [PubMed] [Google Scholar]
- 3.Mackensen F, Smith JR, Rosenbaum JT. Enhanced recognition, treatment, and prognosis of tubulointerstitial nephritis and uveitis syndrome. Ophthalmology 2007;114:995–9. [DOI] [PubMed] [Google Scholar]
- 4.Legendre M, Devillers H, Perard L, et al. Clinicopathologic characteristics, treatment, and outcomes of tubulointerstitial nephritis and uveitis syndrome in adults: A national retrospective strobe-compliant study. Medicine 2016:95:e3964. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Okafor LO, Hewins P, Murray PI, Denniston AK. Tubulointerstitial nephritis and uveitis (TINU) syndrome: a systematic review of its epidemiology, demographic and risk factors. Orphanet J Rare Dis 2017;12:128. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Clive DM, Vanguri VK. The syndrome of tubulointerstitial nephritis with uveitis (TINU). Am J Kidney Dis 2018;72:118–128. [DOI] [PubMed] [Google Scholar]
- 7.Oliva-Damaso N, Oliva-Damaso E, Payan J. Acute and chronic tubulointerstitial nephritis of rheumatic causes. Rheum Dis Clin North Am 2018;44:619–33. [DOI] [PubMed] [Google Scholar]
- 8.Nagashima T, Ishihara M,Shibuya E, Nakamura S, Mizuki N. Three cases of tubulointerstitial nephritis and uveitis syndrome with different clinical manifestations. Int Ophthalmol 2017;37:753–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Gion N, Stavrou P, Foster CS. Immunomodulatory therapy for chronic tubulointerstitial nephritis-associated uveitis. Am J Ophthalmol 2000;129:764–8. [DOI] [PubMed] [Google Scholar]
- 10.Provencher LM, Fairbanks AM, Abramoff MD, Syed NA. Urinary β2-microglobulin and disease activity in patients with tubulointerstitial nephritis and uveitis syndrome. J Ophthalmic Inflamm Infect 2018;8:24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Jabs DA, Rosenbaum JT, Nussenblatt RB, the Standardization of Uveitis Nomenclature (SUN) Working Group. Standardization of uveitis nomenclature for reporting clinical data. Report of the first international workshop. Am J Ophthalmol 2005;140:509–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Trusko B, Thorne J, Jabs D, et al. Standardization of Uveitis Nomenclature Working Group. The SUN Project. Development of a clinical evidence base utilizing informatics tools and techniques. Methods Inf Med 2013;52:259–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Jabs DA, Busingye J. Approach to the diagnosis of the uveitides. Am J Ophthalmol 2013;156:228–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Okada AA, Jabs DA. The SUN Project. The future is here. Arch Ophthalmol 2013;131:787–9. [DOI] [PubMed] [Google Scholar]
- 15.Jabs DA, Dick A, Doucette JT, Gupta A, Lightman S, McCluskey P, Okada AA, Palestine AG, Rosenbaum JT, Saleem SM, Thorne J, Trusko B for the Standardization of Uveitis Nomenclature Working Group. Interobserver agreement among uveitis experts on uveitic diagnoses: the Standard of Uveitis Nomenclature Experience. Am J Ophthalmol 2018; 186:19–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Aggarwal R, Ringold S, Khanna D, et al. Distinctions between diagnostic and classification criteria. Arthritis Care Res 2015;67(7):891–897. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.The Standardization of Uveitis Nomenclature (SUN) Working Group. Development of classification criteria for the uveitides. Am J Ophthalmol 2020l;ume:pp. [DOI] [PubMed] [Google Scholar]
- 18.Goda C, Kotake S, Ichiishi A, Namba K, Kitaichi N, Ohno S. Clinical features in tunulointerstitial nephritis and uveitis. Am J Ophthalmol 2005;140:637–41. [DOI] [PubMed] [Google Scholar]
- 19.Levinson RD, Park MS, Rikkers SM, et al. Strong associations between specific HLA-DQ adn HLA-DR alleles and the tubulointerstitial nephritis and uveitis syndrome. Invest Ophthalmol Vis Sci 2003;44:653–7. [DOI] [PubMed] [Google Scholar]
- 20.Mackensen F, David F, Schenger V, et al. HLA=DRB1*0102 is associated with TINU syndrome and bilateral sudden-onset anterior uveitis but not with interstitial nephritis. Br J Ophthalmol 2011;95:971–5. [DOI] [PubMed] [Google Scholar]
- 21.Zamecki KJ, Jabs DA. HLA typing in uveitis: use and misuse. Am J Ophthalmol 2010;149:189–93. [DOI] [PubMed] [Google Scholar]