Abstract
Purpose:
To determine classification criteria for punctate inner choroiditis (PIC).
Design:
Machine learning of cases with PIC and 8 other posterior uveitides.
Methods:
Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on 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 posterior uveitides. The resulting criteria were evaluated on the validation set.
Results:
One thousand sixty-eight cases of posterior uveitides, including 144 cases of PIC, were evaluated by machine learning. Key criteria for PIC included: 1) “punctate” appearing choroidal spots <250 μm in diameter; 2) absent to minimal anterior chamber and vitreous inflammation; and 3) involvement of the posterior pole with or without mid-periphery. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for PIC were 15% in the training set and 9% in the validation set.
Conclusions:
The criteria for PIC had a reasonably low misclassification rate and appeared to perform sufficiently well 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 punctate inner choroiditis were developed. Key criteria included “punctate”-appearing choroidal lesions <250 μm in diameter, absent to minimal anterior chamber and vitreous inflammation, and posterior pole and/or mid periphery chorioretinal involvement. The resulting classification criteria had a low misclassification rate.
Punctate inner choroiditis (PIC), originally termed punctate inner choroidopathy, was first described by Watzke et al1 in 1984 as a distinct type of multifocal choroiditis, occurring typically in young adult myopic women and characterized by small, “punctate” lesions of the inner choroid and/or retinal pigment epithelium. Lesions often had overlying subretinal fluid, and on fluorescein angiography in the acute phase were hyperfluorescent and leaked fluorescein dye. Anterior chamber and vitreous inflammation typically were absent. The lesions healed into atrophic scars, and choroidal neovascularization developed in 6 of 10 patients. Although often symptomatic with blurred vision, flashing lights, or paracentral scotomata, vision was minimally affected unless choroidal neovascularization developed.
Punctate inner choroiditis is an uncommon disease, accounting for ≤10% of posterior uveitides presenting to a tertiary care referral ophthalmology center,2 and its incidence has been estimated at 0.4 cases/1,000,000/year.3,4 The etiology and pathogenesis of PIC are unknown; PIC is an eye-limited disease unassociated with a systemic disease. It has been speculated that PIC is an autoimmune process,5 and the association of PIC with polymorphisms in the interleukin-10 and tumor necrosis factor-α genes has been taken as evidence of an autoimmune process.6 However, it is unclear whether these genetic risk factors are risk factors for the disease itself or for the associated choroidal neovascularization.
Subsequent larger case series2,7–10 have confirmed the clinical picture described by Watzke et al.1 Although there is a wide range of age at presentation, the mean age is ~32–33 years. Over 90% of reported cases are women, and ~85% to 92% are myopic. Active lesions are yellow to yellowish white, estimated at 100 to 300 μm in size, and largely located in the posterior pole. At presentation ~55% will have bilateral disease, but bilateral disease has been reported to occur in as many as 88%.10 Data from one study can be used to estimate the rate of bilateral disease as ~0.03/person-year.7 Over 90% have no anterior chamber or vitreous inflammation, and other signs of inflammation (e.g. posterior synechiae) typically are not present.8 Approximately 50% of cases will present with choroidal neovascularization in at least one eye,2,7–10 but with follow-up, estimates of choroidal neovascularization run as high as ~75% to 80%.9,10 One study estimated the incidence of new and recurrent choroidal neovascularization as 0.02/eye-year (EY) and 0.04/EY, respectively.11 Other structural complications of uveitis (e.g. macular edema, disc edema) typically are not present.8
The active “punctate” lesions of PIC are distinct, yellow-white or cream-colored, typically round or oval, and <150 μm in size. There may be an overlying serous elevation, and the lesions may resolve or heal with atrophic scarring. Fluorescein angiography demonstrates early hyperfluorescence of active lesions with late staining. Late-stage atrophic scars are seen as window defects on fluorescein angiography.3 Indocyanine angiography demonstrates hypofluorescent lesions throughout the angiogram.3,12 More lesions may be evident on imaging than are appreciated on ophthalmoscopy. Optical coherence tomography (OCT) demonstrates focal hyperreflective elevation of the retinal pigment epithelium with corresponding disruption of the inner and outer segment photoreceptor interface.13 Enhanced depth imaging OCT of acute lesions may demonstrate increased choroidal thickening.14 Choroidal neovascularization, when present, is seen on fluorescein angiography, OCT, and OCT angiography.3,13,15 Fundus autofluorescence demonstrates hyper-autofluorescence of active lesions and may be useful in following the response to treatment.15,17
The course of PIC is variable. Active inflammatory lesions may spontaneously involute to small atrophic scars. Bilateral disease may occur simultaneously or asynchronously, with the disease in the second eye occurring years later. Rarely choroidal neovascularization may spontaneously involute, but now nearly all choroidal neovascularization is treated with antivascular endothelial growth factor (VEGF) agents (e.g. bevacizumab, ranibizumab, aflibercept). Patients with recurrent disease, chronic disease, bilateral choroidal neovascularization, or choroidal neovascularization requiring multiple injections of anti-VEGF agents typically are treated with oral corticosteroids and immunosuppression.11,18,19 Case series suggest that with appropriate use of immunosuppression, disease control, preservation of vision, and decreased or no need for anti-VEGF injections can be accomplished along with successful corticosteroidsparing (dose prednisone ≤7.5 mg/day) in the majority of patients.11,19 Rates of visual impairment (worse than 20/40) and blindness (20/200 or worse) have been estimated at 0.06/EY and 0.006/EY, respectively, with preservation of good vision in at least one eye typically seen.11
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. Among the diseases studied was PIC.20–26
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.22–25
Informatics.
As previously described, the consensus-based informatics phase permitted the development of a standardized vocabulary and the development of a standardized, menudriven hierarchical case collection instrument.22
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.24,25 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.24,25 Because the goal was to develop classification criteria,26 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”).24,25
Machine learning.
The final database then was randomly separated into a learning set (~85% of the cases) and a validation set (~15% of the cases) for each disease as described in the accompanying article.25 Machine learning was used on the learning set to determine criteria that minimized misclassification. The criteria then were tested on the validation set; for both the learning 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 PIC, the diseases against which it was evaluated included: acute posterior multifocal placoid pigment epitheliopathy (APMPPE), birdshot chorioretinitis (BSCR) multifocal choroiditis with panuveitis (MFCPU), multiple evanescent white dot syndrome (MEWDS), serpiginous choroiditis, sarcoidosisassociated posterior uveitis, syphilitic posterior uveitis, and tubercular (TB) 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
Two hundred fifty cases of PIC were collected, and 144 (58%) achieved supermajority agreement on the diagnosis during the “selection” phase and were used in the machine learning phase. These cases of PIC were compared to cases of posterior uveitides, including 82 cases of APMPPE, 207 cases of BSCR, 51 cases of MEWDS, 138 cases of MFCPU, 122 cases of serpiginous choroiditis, 12 cases of sarcoid posterior uveitis, 35 cases of syphilitic posterior uveitis, and 277 cases of tubercular posterior or panuveitis (including 96 cases of serpiginous-like tubercular choroiditis). The details of the machine learning results for these diseases are outlined in the accompanying article.25 The characteristics of cases with PIC are listed in Table 1, and the classification criteria developed after machine learning are listed in Table 2. Key features of the criteria include: 1) characteristic punctate choroidal lesions (Figure 1); 2) absent or minimal anterior chamber and vitreous inflammation, and 3) posterior pole involvement. The overall accuracies for posterior uveitides were 93.9% in the learning set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rate for PIC in the learning set was 15%, and in the validation set it was 9%. The disease most often confused with PIC was MFCPU.
Table 1.
Characteristics of Cases with Punctate Inner Choroiditis
| Characteristic | Result |
|---|---|
| Number cases | 144 |
| Demographics | |
| Age, median, years (25th 75th percentile) | 32 (25, 39) |
| Gender (%) | |
| Men | 13 |
| Women | 87 |
| Race/ethnicity (%) | |
| White, non-Hispanic | 81 |
| Black, non-Hispanic | 6 |
| Hispanic | 1 |
| Asian, Pacific Islander | 2 |
| Other | 4 |
| Missing | 6 |
| Uveitis History | |
| Uveitis course (%) | |
| Acute, monophasic | 26 |
| Acute, recurrent | 6 |
| Chronic | 65 |
| Indeterminate | 3 |
| Laterality (%) | |
| Unilateral | 41 |
| Unilateral, alternating | 0 |
| Bilateral | 59 |
| Ophthalmic examination | |
| Keratic precipitates (%) | |
| None | 100 |
| Anterior chamber cells (%) | |
| Grade 0 | 99 |
| ½+ | 1 |
| ≥1+ | 0 |
| Anterior chamber flare (%) | |
| Grade 0 | 100 |
| Iris (%) | |
| Normal | 100 |
| Intraocular pressure (IOP), involved eyes | |
| Median, mm Hg (25th, 75th percentile) | 15 (13, 17) |
| Proportion patients with IOP>24 mm Hg either eye (%) | 2 |
| Vitreous cells (%) | |
| Grade 0 | 91 |
| ½+ | 9 |
| ≥1+ | 0 |
| Vitreous haze (%) | |
| Grade 0 | 99 |
| ½+ | 1 |
| ≥1+ | 0 |
| Chorioretinitis characteristics | |
| Lesion number (%) | |
| Unifocal (1 lesion) | 0 |
| Paucifocal (2–4) | 28 |
| Multifocal (≥5) | 72 |
| Lesion shape & character (%) | |
| Ameboid or serpentine | 0 |
| Oval or round | 18 |
| Placoid | 0 |
| Atrophic | 38 |
| Punctate | 82 |
| Inflammatory lesion/scar location (%)* | |
| Posterior pole involved | 78 |
| Posterior pole and periphery/mid-periphery involved | 21 |
| Mid-periphery and periphery only | 1 |
| Typical lesion size (%) | |
| <125 μm | 54 |
| 125–250 μm | 33 |
| 250–500 μm | 9 |
| >500 μm | 3 |
| Missing | 1 |
| Other features (%) | |
| Retinal vascular sheathing | 3 |
| Retinal vascular leakage | 17 |
| Choroidal neovascularization | 19 |
Based on 130 cases with retinal photographs
Table 2.
Classification Criteria for Punctate Inner Choroiditis
| Criteria |
| 1. Multifocal choroidal inflammatory lesions |
| a. Predominant lesion size <250 μm AND |
| b. Punctate lesion appearance |
| AND |
| 2. Lesion involvement of posterior pole with or without mid-periphery |
| AND |
| 3. Absent to minimal anterior chamber and vitreous inflammation |
| Exclusions |
| 1. Positive serologic test for syphilis using a treponemal test |
| 2. Evidence of sarcoidosis (either bilateral hilar adenopathy on chest imaging or tissue biopsy demonstrating non-caseating granulomata) |
Figure 1.
Fundus photograph of a case of punctate inner choroiditis, demonstrating “punctate” chorioretinal lesions in the posterior pole.
Discussion
The classification criteria developed by the SUN Working Group for PIC have a moderate misclassification rate, indicating reasonable discriminatory performance against other posterior uveitides.25
Because of the rare occurrence of PIC-like lesions in one eye and MFCPU in the other, and because of a similar appearance on multi-modal imaging (other than lesion size), some investigators have considered PIC and MFCPU to be variants of the same disease.27,28 Conversely, other investigators, classifying the two diseases based solely on chorioretinal morphology have found clear cut differences, namely the absence of anterior chamber and vitreous inflammation and the absence of uveitis-related structural complications other than choroidal neovascularization in PIC,.8 and differences in course with prognostic import.29 There also is a difference in the distribution of lesions between the two diseases; lesions in PIC are most often in the posterior pole, whereas lesions in MFCPU typically involve the mid-periphery and periphery with or without posterior pole involvement.30 A study of cases of MFCPU and PIC using cluster analysis determined that two distinct clusters existed, conforming to the diagnoses of PIC and MFCPU and that the two distinguishing features were anterior chamber and vitreous inflammation (largely absent in PIC) and lesion location (posterior in PIC and peripheral in MFCPU).30
In the series by Shimada et al,31 histological evaluation of surgically-removed choroidal neovascular membranes demonstrated inflammatory infiltrates in some cases of MFCPU but not in PIC, suggesting that they might be distinct diseases. Conversely, in the case series by Olsen et al32 histological evaluation of surgically-removed choroidal neovascular membranes from patients with PIC demonstrated the occasional lymphocyte, suggesting that the pathology may not be completely dissimilar from that of MFCPU. A genetic risk factor association study suggested similar haplotype associations in the IL-10 and TNF loci for MFCPU and PIC, suggesting possible similarities in pathogenesis, but allowing for different inciting events or epigenetic factors to influence phenotype.29 Finally, if they were a single disease, one might expect the clinical presentation to be a Gaussian distribution with the overlap syndrome to be the most common presentation, which is not the case. The paradigms are the most common presentations, and overlap is uncommon.8,27,30 Hence, the SUN Working Group has elected to define the diseases separately, recognizing that there will be a small number of cases with an overlap appearance. Most such cases behave more like MFCPU than PIC and might be classified as MFCPU, but probably should be classified as an overlap syndrome at this time.
The presence of any of the exclusions in Table 2 suggests an alternate diagnosis, and the diagnosis of PIC should not be made in their presence. In prospective studies, many of these tests will be performed routinely and the alternative diagnoses excluded, especially syphilis. However, in retrospective studies based on clinical care, not all of these tests may have been performed. In these studies the presence of an exclusionary criterion excludes PIC, but the absence of such testing does not always exclude the diagnosis of PIC if the criteria for the diagnosis are met.
Classification criteria are employed to diagnose individual diseases for research purposes.26 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,26 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,24 the selection of cases for the final database (“case selection”) included only cases which achieved supermajority agreement on the diagnosis. As such, some cases which clinicians would diagnose with PIC may not be so classified by classification criteria.
In conclusion, the criteria for PIC outlined in Table 2 appear to perform sufficiently well for use as classification criteria in clinical research.25
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
Conflict of Interest: Douglas A. Jabs: none; Antoine P. Brezin: none; Ralph D. Levinson: none; Susan L. Lightman: none; Peter McCluskey: none; Neal Oden: none; Alan G. Palestine: none; Narsing A. Rao: 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; Albert Vitale: none; Susan E. Wittenberg: none.
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