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Aims To study the risk factors associated with blindness after treatment of acute primary angle closure (APAC), and to identify the critical time window to decrease rate of blindness. Methods In this multicentre retrospective case series, 1030 consecutive subjects (1164 eyes) with APAC in China were recruited. The rates of blindness were analysed up to 3 months after treatment of APAC. A logistic regression was used to identify the risk factors associated with blindness, including age, gender, distance to hospital, rural or urban settings, treatment method, education level, time from symptom to treatment (TST, hours) and presenting intraocular pressure (IOP). The critical time window associated with a blindness rate of ≤1% was calculated based on a cubic function by fitting TST to the rate of blindness at each time point. Results The rate of blindness after APAC was 12.54% after treatment. In multivariate regression, education level, TST and presenting IOP were risk factors for blindness (p=0.022, 0.004 and 0.001, respectively). The critical time window associated with a blindness rate of ≤1% was 4.6 hours. Conclusions and relevance Education level, TST and presenting IOP were risk factors for blindness after APAC. Timely medical treatment is key in reducing blindness after APAC.

By 2040, ~100 million people will have glaucoma. To date, there are a lack of high-efficiency glaucoma diagnostic tools based on visual fields (VFs). Herein, we develop and evaluate the performance of 'iGlaucoma', a smartphone application-based deep learning system (DLS) in detecting glaucomatous VF changes. A total of 1,614,808 data points of 10,784 VFs (5542 patients) from seven centers in China were included in this study, divided over two phases. In Phase I, 1,581,060 data points from 10,135 VFs of 5105 patients were included to train (8424 VFs), validate (598 VFs) and test (3 independent test sets-200, 406, 507 samples) the diagnostic performance of the DLS. In Phase II, using the same DLS, iGlaucoma cloud-based application further tested on 33,748 data points from 649 VFs of 437 patients from three glaucoma clinics. With reference to three experienced expert glaucomatologists, the diagnostic performance (area under curve [AUC], sensitivity and specificity) of the DLS and six ophthalmologists were evaluated in detecting glaucoma. In Phase I, the DLS outperformed all six ophthalmologists in the three test sets (AUC of 0.834-0.877, with a sensitivity of 0.831-0.922 and a specificity of 0.676-0.709). In Phase II, iGlaucoma had 0.99 accuracy in recognizing different patterns in pattern deviation probability plots region, with corresponding AUC, sensitivity and specificity of 0.966 (0.953-0.979), 0.954 (0.930-0.977), and 0.873 (0.838-0.908), respectively. The 'iGlaucoma' is a clinically effective glaucoma diagnostic tool to detect glaucoma from humphrey VFs, although the target population will need to be carefully identified with glaucoma expertise input. ispartof: NPJ Digit Med vol:3 issue:1 pages:123- ispartof: location:England status: Published online