Susan Lightman’s research while affiliated with Moorfields Eye Hospital NHS Foundation Trust and other places

Background/aims Acute posterior multifocal placoid pigment epitheliopathy is a rare but important disease that can be associated with life-threatening complications due to cerebral vasculitis. The primary objective was to determine the incidence of neurological complications and risk factors for stroke and transient ischaemic attack (TIA) associated with acute posterior multifocal placoid pigment epitheliopathy. Secondary objectives included the clinical presentation, visual outcomes and recurrence rates. Methods This was a multicentre retrospective case series including 111 eyes from 60 subjects presenting from January 2009 to June 2020. Results Median age at presentation was 29 years (IQR 24.7–35.1) and 36 subjects (60.0%) were male. 20 subjects (33.3%) reported a viral prodrome. Stroke and TIA were observed in seven subjects (11.7%). Older age was the only significant risk factor for stroke/TIA (p=0.042). Vision loss occurred in seven eyes, with four eyes (3.6%) having final visual acuity 6/15–6/60 and three eyes (2.7%) having visual acuity of 6/60 or worse. Recurrence occurred in 10 subjects (16.7%). Conclusions The presence of headache cannot reliably predict those at risk of stroke/TIA. Individuals presenting with acute posterior multifocal pigment epitheliopathy should therefore undergo a clinical neurological review and work-up for cerebral vasculitis as deemed appropriate by the treating ophthalmologist and collaborating neurologist.

Objectives To report patients with systemic lymphoma and cytomegalovirus (CMV) retinitis, treated with a combination of oral and intravitreal antiviral agents on an outpatient basis. Methods Retrospective cases series. Information was gathered from the database of the Uveitis clinics at Moorfields Eye Hospital, United Kingdom from December 2014 to December 2018. The inclusion criteria comprised the diagnosis of systemic lymphoma, associated with a diagnosis of CMV retinitis. Exclusion criteria were alternative ocular diagnosis, human immunodeficiency virus (HIV), primary intraocular lymphoma, or other causes of immunosuppression. Results All seven subjects had been under oncologist care for systemic lymphoma. CMV retinitis presented with a median of 61 months after the systemic lymphoma diagnosis. Five patients underwent a vitreous biopsy, and four of them returned PCR positive for CMV and the fifth patient had PCR positive in a blood sample. All patients were treated with oral Valganciclovir, with an induction dose of 900 mg every 12 h for up to 3 weeks until disease resolution and a maintenance dose thereafter. All but one received additional intravitreal Foscarnet injections, with a dose of 2.4 mg /0.1 ml. Conclusions The management of patients with systemic lymphoma and CMV retinitis with oral and intravitreal antiviral agents, resulted in effective disease control.

Purpose To determine classification criteria for syphilitic uveitis Design Machine learning of cases with syphilitic uveitis and 24 other uveitides. Methods Cases of anterior, intermediate, posterior, and panuveitides 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 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 on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the different uveitic classes. The resulting criteria were evaluated on the validation set. Results Two hundred twenty-two cases of syphilitic uveitis were evaluated by machine learning with cases evaluated against other uveitides in the relevant uveitic class. Key criteria for syphilitic uveitis included a compatible uveitic presentation, (1) anterior uveitis, 2) intermediate uveitis, or 3) posterior or panuveitis with retinal, retinal pigment epithelial, or retinal vascular inflammation) and evidence of syphilis infection with a positive treponemal test. The Centers for Disease Control and Prevention reverse screening algorithm for syphilis testing is recommended. The misclassification rates for syphilitic uveitis in the training sets were: anterior uveitides 0%, intermediate uveitides 6.0%, posterior uveitides 0%, panuveitides 0%, and infectious posterior/panuveitides 8.6%. The overall accuracy of the diagnosis of syphilitic uveitis in the validation set was 100% (99% CI 99.5, 100) – i.e. the validation sets misclassification rates were 0% for each uveitic class. Conclusions The criteria for syphilitic uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Purpose: 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 of 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 on the training sets to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation sets. Results: One thousand eighty-three anterior uveitides, 589 intermediate uveitides, and 1012 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) a tissue biopsy 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: anterior uveitis 3.2%, intermediate uveitis 2.6%, and panuveitis 1.2%; in the validation sets the misclassification rates were: anterior uveitis 0%, intermediate uveitis 0%, and panuveitis 0%, respectively. 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.

Purpose: To determine classification criteria for tubercular uveitis DESIGN: Machine learning of cases with tubercular uveitis and 14 other uveitides. Methods: Cases of non-infectious posterior or panuveitis, and of infectious posterior or panuveitis 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 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 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 sets. Results: Two hundred seventy-seven cases of tubercular uveitis were evaluated by machine learning against other uveitides. Key criteria for tubercular uveitis were a compatible uveitic syndrome, including: 1) anterior uveitis with iris nodules, 2) serpiginous-like tubercular choroiditis, 3) choroidal nodule (tuberculoma), 4) occlusive retinal vasculitis, and 5) in hosts with evidence of active systemic tuberculosis, multifocal choroiditis; and evidence of tuberculosis, including: 1) histologically- or microbiologically-confirmed infection, 2) positive interferon-Ɣ release assay test, or 3) positive tuberculin skin test. The overall accuracy of the diagnosis of tubercular uveitis versus other uveitides in the validation set was 98.2% (95% CI 96.5, 99.1). The misclassification rates for tubercular uveitis were: training set 3.4%; and validation set 3.6%. Conclusions: The criteria for tubercular uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

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.

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.