Seizure occurrences among hypoglycemic patients in the emergency department.

Aim: Symptomatic hypoglycemia is a common problem in the emergency department (ED). However, without appropriate recognition and management, hypoglycemia remains a potentially fatal condition. The cause of sudden death associated with hypoglycemia might be attributed to cardiac arrhythmias and hypoxia with seizures. Despite advances in diabetes mellitus management and social background, the frequency and characteristics of patients with hypoglycemia-related seizures have remained unknown. Hence, our study aimed to investigate the frequency and characteristics of patients with hypoglycemia presenting with seizures in the ED. Methods: This retrospective observational study was conducted in a single tertiary care center. Patient information was retrieved from the final diagnostic records in the ED. We reviewed all medical records and included patients with symptomatic hypoglycemia aged 16 years or older. The primary outcome was the frequency of seizures in patients with hypoglycemia. We also compared the initial blood sugar levels of the patients with and without seizures. Results: We included a total of 380 patients (median age, 72 years, IQR 64-80 years; median initial blood sugar, 34 mg/dL, IQR 24-46; 62.9% male). Nineteen of 380 patients (5.0%) had seizures. Although 16 of the 19 patients had diabetes mellitus, none of the 19 patients had a history of epilepsy. The initial blood sugar levels of the patients with and without seizures were not significantly different (p = 0.97). Conclusion: Approximately 5% of the patients with hypoglycemia presented with seizures. Blood glucose levels of hypoglycemic patients with and without seizures did not differ.

Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images.

BACKGROUND: Artificial intelligence (AI) in medical imaging diagnostics has huge potential, but human judgement is still indispensable. We propose an AI-aided teaching method that leverages generative AI to train students on many images while preserving patient privacy. METHODS: A web-based course was designed using 600 synthetic ultra-widefield (UWF) retinal images to teach students to detect disease in these images. The images were generated by stable diffusion, a large generative foundation model, which we fine-tuned with 6285 real UWF images from six categories: five retinal diseases (age-related macular degeneration, glaucoma, diabetic retinopathy, retinal detachment and retinal vein occlusion) and normal. 161 trainee orthoptists took the course. They were evaluated with two tests: one consisting of UWF images and another of standard field (SF) images, which the students had not encountered in the course. Both tests contained 120 real patient images, 20 per category. The students took both tests once before and after training, with a cool-off period in between. RESULTS: On average, students completed the course in 53 min, significantly improving their diagnostic accuracy. For UWF images, student accuracy increased from 43.6% to 74.1% (p<0.0001 by paired t-test), nearly matching the previously published state-of-the-art AI model's accuracy of 73.3%. For SF images, student accuracy rose from 42.7% to 68.7% (p<0.0001), surpassing the state-of-the-art AI model's 40%. CONCLUSION: Synthetic images can be used effectively in medical education. We also found that humans are more robust to novel situations than AI models, thus showcasing human judgement's essential role in medical diagnosis.

Foveal Thickness Fluctuations in Anti-VEGF Treatment for Central Retinal Vein Occlusion.

Purpose: The aim of this study was to examine the effects of foveal thickness (FT) fluctuation (FTF) on 2-year visual and morphological outcomes of eyes with central retinal vein occlusion (CRVO) undergoing anti-VEGF treatment for recurrent macular edema (ME) based on a pro re nata regimen. Design: Retrospective, observational case series. Participants: We analyzed 141 treatment-naive patients (141 eyes) with CRVO-ME at a multicenter retinal practice. Methods: We assessed FT using OCT at each study visit. Patients were divided into groups 0, 1, 2, and 3 according to increasing FTF. Main Outcome Measures: We evaluated the logarithm of the minimal angle of resolution (logMAR) best-corrected visual acuity (BCVA), the length of the foveal ellipsoid zone (EZ) band defect measured using OCT, and the association of FTF with VA and EZ band defect length. Results: The mean baseline logMAR BCVA and FT were 0.65 ± 0.52 (Snellen equivalent range: 20/20-20/2000) and 661.1 ± 257.4 µm, respectively. The mean number of anti-VEGF injections administered was 5.6 ± 3.6. At the final examination, the mean logMAR BCVA and FT values were significantly improved relative to the baseline values (both P < 0.01). During the observation, BCVA longitudinally improved in Groups 0 and 1, remained unchanged in Group 2, and worsened in Group 3. Likewise, the length of the foveal EZ band defect did not increase in Group 0; however, it gradually increased in Groups 1, 2, and 3. Foveal thickness fluctuation was significantly and positively associated with the logMAR BCVA and length of the foveal EZ band defect at the final examination (P < 0.01). The final logMAR BCVA of patients developing neovascular complications was 1.27 ± 0.72 (Snellen equivalent range: 20/50-counting fingers), which was significantly poorer than that of patients without complications (P < 0.001). There was no significant difference in the neovascular complication rate among the FTF groups (P = 0.106, Fisher exact test). Conclusions: In eyes receiving anti-VEGF treatment for CRVO-ME, FTF can longitudinally impair the visual acuity and foveal photoreceptor status during the observation period, thus influencing the final outcomes. However, neovascular complications, which would also lead to a poor visual prognosis, may not be associated with FTF. Financial Disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article.

Revolutionizing Patient Monitoring in Age-Related Macular Degeneration: A Comparative Study on the Necessity and Efficiency of the AMD VIEWER.

(1) Background: Age-related Macular Degeneration (AMD) is a critical condition leading to blindness, necessitating lifelong clinic visits for management, albeit with existing challenges in monitoring its long-term progression. This study introduced and assessed an innovative tool, the AMD long-term Information Viewer (AMD VIEWER), designed to offer a comprehensive display of crucial medical data-including visual acuity, central retinal thickness, macular volume, vitreous injection treatment history, and Optical Coherent Tomography (OCT) images-across an individual eye's entire treatment course. (2) Methods: By analyzing visit frequencies of patients with a history of invasive AMD treatment, a comparative examination between a Dropout group and an Active group underscored the clinical importance of regular visits, particularly highlighting better treatment outcomes and maintained visual acuity in the Active group. (3) Results: The efficiency of AMD VIEWER was proven by comparing it to manual data input by optometrists, showing significantly faster data display with no errors, unlike the time-consuming and error-prone manual entries. Furthermore, an elicited Net Promoter Score (NPS) of 70 from 10 ophthalmologists strongly endorsed AMD VIEWER's practical utility. (4) Conclusions: This study underscores the importance of regular clinic visits for AMD patients. It suggests the AMD VIEWER as an effective tool for improving treatment data management and display.