Surgical Management of Diabetic Macular Edema.

PURPOSE OF REVIEW: Diabetic macular edema (DME) is the accumulation of fluid in the extracellular space within the macula and is a major cause of visual impairment among patients with diabetes. First-line treatment for DME includes pharmacotherapy with intravitreal anti-vascular endothelial growth factor medications and intravitreal corticosteroids. Alternative therapeutic strategies include laser photocoagulation for non-center involving DME, and surgical options such as pars plana vitrectomy (PPV) with or without internal limiting membrane (ILM) peel in cases with vitreoretinal interface anomalies or DME refractory to pharmacotherapy, and the Port Delivery System (PDS) for sustained release of anti-vascular endothelial growth factor (VEGF) medication. Our aim is to review the existing literature on surgical management of DME including imaging changes in chronic DME and the clinical relevance of surgical intervention. RECENT FINDINGS: Imaging changes associated with DME and a worse prognosis include disorganization of the retinal layer, disruption of both the external limiting membrane (ELM) and ellipsoid zone, and vitreomacular interface abnormalities. Studies involving pars plana vitrectomy with and without ILM peel show anatomic improvement but may not always be associated with significant change in visual outcomes. Early studies lacked detailed imaging of the retinal layers and PPV was likely performed as a last resort. In addition, the novel PDS is surgically implanted into the pars plana and works as a drug reservoir with controlled release of drug. However, it has been recalled in patients with wet age-related macular degeneration due to issues with dislodgement. Surgical interventions for DME include pars plana vitrectomy with and without ILM peel and new surgical therapies for DME such as the PDS and subretinal gene therapy have the potential to reduce the risk of DME progression.

Virtual Standardized Patient Simulation: Case Development and Pilot Application to High-Value Care.

INTRODUCTION: High-value care (HVC) suggests that good history taking and physical examination should lead to risk stratification that drives the use or withholding of diagnostic testing. This study describes the development of a series of virtual standardized patient (VSP) cases and provides preliminary evidence that supports their ability to provide experiential learning in HVC. METHODS: This pilot study used VSPs, or natural language processing-based patient avatars, within the USC Standard Patient platform. Faculty consensus was used to develop the cases, including the optimal diagnostic testing strategies, treatment options, and scored content areas. First-year resident physician learners experienced two 90-minute didactic sessions before completing the cases in a computer laboratory, using typed text to interview the avatar for history taking, then completing physical examination, differential diagnosis, diagnostic testing, and treatment modules for each case. Learners chose a primary and 2 alternative "possible" diagnoses from a list of 6 to 7 choices, diagnostic testing options from an extensive list, and treatments from a brief list ranging from 6 to 9 choices. For the history-taking module, both faculty and the platform scored the learners, and faculty assessed the appropriateness of avatar responses. Four randomly selected learner-avatar interview transcripts for each case were double rated by faculty for interrater reliability calculations. Intraclass correlations were calculated for interrater reliability, and Spearman ρ was used to determine the correlation between the platform and faculty ranking of learners' history-taking scores. RESULTS: Eight VSP cases were experienced by 14 learners. Investigators reviewed 112 transcripts (4646 learner query-avatar responses). Interrater reliability means were 0.87 for learner query scoring and 0.83 for avatar response. Mean learner success for history taking was scored by the faculty at 57% and by the platform at 51% (ρ correlation of learner rankings = 0.80, P = 0.02). The mean avatar appropriate response rate was 85.6% for all cases. Learners chose the correct diagnosis within their 3 choices 82% of the time, ordered a median (interquartile range) of 2 (2) unnecessary tests and completed 56% of optimal treatments. CONCLUSIONS: Our avatar appropriate response rate was similar to past work using similar platforms. The simulations give detailed insights into the thoroughness of learner history taking and testing choices and with further refinement should support learning in HVC.