PurposeTo report the investigation of an outbreak of multidrug-resistant (MDR) Pseudomonas aeruginosa endophthalmitis in 13 patients after cataract surgery and to emphasize on the importance of clinical profile, risk factors, and treatment outcomesMethodsThis was a hospital-based, retrospective case study with 13 consecutive patients who had man- ual small-incision cataract surgery with intraocular lens (IOL) implantation and developed acute postoperative Pseudomonas aeruginosa endophthalmitis. The anterior chamber taps, vitreous aspirates, and environ- mental surveillance specimens were inoculated for culturing. Antibiotic susceptibility testing was performed using the agar diffusion method. Pulsed-field gel electrophoresis (PFGE) was used to determine the relation- ship between bacterial isolates recovered from study patients and contaminated surveillance samples.ResultsPseudomonas aeruginosa was isolated from all 13 eyes with acute postoperative endophthalmitis and the trypan blue solutions used during surgery. Sensitivity tests revealed that all isolates had an identical resistance to multiple drugs and were only susceptible to imipenem. Genomic DNA typing of Pseudomonas aeruginosa isolates recovered from patients and trypan blue solutions showed an identical banding pattern on the PFGE. Despite the prompt use of intravitreal antibiotics and early vitrectomy with IOL explantation in some patients, the outcome was poor in about 50% of patients.ConclusionPositive microbiology and genomic DNA typing results proved that the contaminated trypan blue solutions were the source of infection in this outbreak. Postoperative endophthalmitis caused by Pseudomonas aeruginosa is often associated with a poor visual prognosis despite prompt treatment with intravitreal antibiotics.

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Screening effectively identifies patients at risk of sight-threatening diabetic retinopathy (STDR) when retinal images are captured through dilated pupils. Pharmacological mydriasis is not logistically feasible in non-clinical, community DR screening, where acquiring gradable retinal images using handheld devices exhibits high technical failure rates, reducing STDR detection. Deep learning (DL) based gradability predictions at acquisition could prompt device operators to recapture insufficient quality images, increasing gradable image proportions and consequently STDR detection. Non-mydriatic retinal images were captured as part of SMART India, a cross-sectional, multi-site, community-based, house-to-house DR screening study between August 2018 and December 2019 using the Zeiss Visuscout 100 handheld camera. From 18,277 patient eyes (40,126 images), 16,170 patient eyes (35,319 images) were eligible and 3261 retinal images (1490 patient eyes) were sampled then labelled by two ophthalmologists. Compact DL model area under the receiver operator characteristic curve was 0.93 (0.01) following five-fold cross-validation. Compact DL model agreement (Kappa) were 0.58, 0.69 and 0.69 for high specificity, balanced sensitivity/specificity and high sensitivity operating points compared to an inter-grader agreement of 0.59. Compact DL gradability model performance was favourable compared to ophthalmologists. Compact DL models can effectively classify non-mydriatic, handheld retinal image gradability with potential applications within community-based DR screening.