A Survey of Neonatal Nurses on Mydriatic Regimens Used in Neonatal Retinopathy of Prematurity Eye Examinations.

OBJECTIVE: This study was aimed to determine mydriatic regimen(s) used in neonatal units in Aotearoa, New Zealand (NZ), and Australia and to estimate the frequency of adverse drug events following mydriatic administration in preterm neonates. STUDY DESIGN: A cross-sectional survey was sent to neonatal nursing staff listed in the Australian and New Zealand Neonatal Network contact list. Participants were asked to state what mydriatic regimen they use, and to estimate the frequency of adverse drug events when eye drops were administered for retinopathy of prematurity eye examinations (ROPEE). RESULTS: Thirteen different mydriatic regimens were identified; phenylephrine 2.5% and cyclopentolate 0.5% (1 standard drop of each) was the most commonly used regimen. Two of the regimens exceeded adult doses and five regimens included a mydriatic that is equivalent to an adult dose. Following mydriatic instillation, the three most common adverse effects were apnea, tachycardia, and periorbital pallor. CONCLUSION: Low-concentration single-microdrop regimens are currently in use and resulting in successful ROPEE, yet doses exceeding adult doses are in use throughout Aotearoa, NZ, and Australian units. We know from this dataset that neonates are experiencing unwanted and potentially preventable, adverse effects associated with mydriatics, and every effort should be made to minimize this risk. KEY POINTS: · Thirteen different regimens are in use in Aotearoa, NZ, and Australia.. · Three regimens use doses in excess of adult doses.. · Phenylephrine 2.5% and cyclopentolate 0.5% (one standard drop of each) is the most common regimen.

Diabetic retinopathy screening using deep neural network.

IMPORTANCE: There is a burgeoning interest in the use of deep neural network in diabetic retinal screening. BACKGROUND: To determine whether a deep neural network could satisfactorily detect diabetic retinopathy that requires referral to an ophthalmologist from a local diabetic retinal screening programme and an international database. DESIGN: Retrospective audit. PARTICIPANTS: Diabetic retinal photos from Otago database photographed during October 2016 (485 photos), and 1200 photos from Messidor international database. METHODS: Receiver operating characteristic curve to illustrate the ability of a deep neural network to identify referable diabetic retinopathy (moderate or worse diabetic retinopathy or exudates within one disc diameter of the fovea). MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve, sensitivity and specificity. RESULTS: For detecting referable diabetic retinopathy, the deep neural network had an area under receiver operating characteristic curve of 0.901 (95% confidence interval 0.807-0.995), with 84.6% sensitivity and 79.7% specificity for Otago and 0.980 (95% confidence interval 0.973-0.986), with 96.0% sensitivity and 90.0% specificity for Messidor. CONCLUSIONS AND RELEVANCE: This study has shown that a deep neural network can detect referable diabetic retinopathy with sensitivities and specificities close to or better than 80% from both an international and a domestic (New Zealand) database. We believe that deep neural networks can be integrated into community screening once they can successfully detect both diabetic retinopathy and diabetic macular oedema.