Evaluating the Short-Term Environmental and Clinical Effects of a Radiation Oncology Department's Response to the COVID-19 Pandemic (STEER COVID-19).

BACKGROUND: During the COVID-19 pandemic, many radiation oncology departments worldwide adopted the use of shorter and more intense hypofractionated regimens. Hospital foot traffic was reduced through virtual care. This study's primary objective was to assess the collective environmental impact of these strategic changes by identifying sources of carbon dioxide equivalents (CO2e). The rate of radiation-related adverse event from the increased use of hypofractionated treatments was assessed. METHODS: All patients treated with external beam radiation therapy from April 1, 2019 to March 31, 2021 at our single institution were identified (n=10,175) along with their radiotherapy visits (176,423 fractions), and unplanned visits to the radiation nursing clinic (RNC) or emergency (ER) department. Out-patient hospital and virtual visits (n=75,853) during this same period were also analyzed. Environmental impact measures, including linear accelerator power usage, patient travel distances, and personal protection equipment (PPE) consumption were all converted into CO2e. RESULTS: The use of curative hypofractionated regimens increased from 17% to 27% during the pandemic year. Carbon footprint was reduced by 39% during the pandemic year (1,332,388 kg CO2e) as compared to the pre-pandemic year (2,024,823 kg CO2e). Comparing patients in the pre-pandemic vs. pandemic year, there was a significant reduction in the proportion of hypofractionated patients who needed a visit to either the RNC (39% vs. 25%; p<0.001) or ER (6% vs. 2%; p<0.001) during and within 90 days of radiotherapy. DISCUSSION: This is the first study to demonstrate the environmental benefits of increased use of hypofractionated regimens and virtual care, while assuring that there was no added acute radiation-related adverse event. Our findings support their continued use as one of many long-term strategies to reduce the environmental footprint of healthcare delivery.

Comparing the diagnostic accuracy of telemedicine utilization versus in-person clinical examination for retinopathy of prematurity in premature infants: a systematic review.

PURPOSE: To synthesize the literature assessing the diagnostic accuracy of telemedicine evaluation compared with clinical examination for retinopathy of prematurity (ROP) in premature infants. METHODS: Covidence software was used to conduct a systematic literature search from September 14, 2020, through September 27, 2020, on MEDLINE (Ovid), EMBASE (Ovid), CINAHL, and the gray literature to identify studies relevant to telemedicine utilization for ROP detection. After duplicate removal and two-levels of screening, studies comparing telemedicine evaluation with binocular indirect ophthalmoscopic examination were included. Risk of bias assessment was conducted for the included studies following data extraction. A qualitative review was performed to summarize estimates of accuracy of ROP evaluation by telemedicine. RESULTS: A total of 507 studies were reviewed, of which 323 were found in EMBASE, 115 in MEDLINE, and 79 in CINAHL. Three possibly relevant conference abstracts were found. Following duplicate removal, 410 studies were reviewed based on titles and abstracts. Subsequently, 19 articles were thoroughly examined, and 14 studies (2,655 participants) were included. Most studies found that telemedicine performance for detecting ROP was comparable to ophthalmic examination, especially with regard to identifying treatment-requiring ROP. CONCLUSIONS: Telemedicine evaluation can reliably detect ROP. Incorporation of telemedicine into conventional neonatal care has the potential to improve access to ROP care.

Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis.

BACKGROUND AND OBJECTIVE: The objective of this study was to systematically review and meta-analyze the diagnostic accuracy of current machine learning classifiers for age-related macular degeneration (AMD). Artificial intelligence diagnostic algorithms can automatically detect and diagnose AMD through training data from large sets of fundus or OCT images. The use of AI algorithms is a powerful tool, and it is a method of obtaining a cost-effective, simple, and fast diagnosis of AMD. METHODS: MEDLINE, EMBASE, CINAHL, and ProQuest Dissertations and Theses were searched systematically and thoroughly. Conferences held through Association for Research in Vision and Ophthalmology, American Academy of Ophthalmology, and Canadian Society of Ophthalmology were searched. Studies were screened using Covidence software and data on sensitivity, specificity and area under curve were extracted from the included studies. STATA 15.0 was used to conduct the meta-analysis. RESULTS: Our search strategy identified 307 records from online databases and 174 records from gray literature. Total of 13 records, 64,798 subjects (and 612,429 images), were used for the quantitative analysis. The pooled estimate for sensitivity was 0.918 [95% CI: 0.678, 0.98] and specificity was 0.888 [95% CI: 0.578, 0.98] for AMD screening using machine learning classifiers. The relative odds of a positive screen test in AMD cases were 89.74 [95% CI: 3.05-2641.59] times more likely than a negative screen test in non-AMD cases. The positive likelihood ratio was 8.22 [95% CI: 1.52-44.48] and the negative likelihood ratio was 0.09 [95% CI: 0.02-0.52]. CONCLUSION: The included studies show promising results for the diagnostic accuracy of the machine learning classifiers for AMD and its implementation in clinical settings.

The Prevalence of Occupational Burnout Among Ophthalmologists: A Systematic Review and Meta-Analysis.

OBJECTIVE: To investigate the prevalence of occupational burnout among ophthalmologists in order to better understand the mental and physical well-being of eye physicians and surgeons in the professional workplace. STUDY DESIGN: A systematic review and meta-analysis. METHODS: Online computer databases MEDLINE, EMBASE, CINAHL, and ProQuest Dissertations and Theses were searched systematically and thoroughly. Conferences held through Association for Research in Vision and Ophthalmology, American Academy of Ophthalmology, and Canadian Society of Ophthalmology were searched. Studies were screened using Covidence software. Data on reported burnout prevalence was extracted. STATA 15.0 was used to conduct meta-analysis.Synthesis: Our search strategy identified 318 records from online databases and 11 records from grey literature search, which were screened at 2-levels. Title and abstracts of each record were screened resulting in 24 records moving to full-text screening. Total of 9 records were utilized for quantitative analysis in the data extraction stage. Our results indicated significant professional burnout among ophthalmologists (ES = 0.41; CI: [0.26, 0.56]) with significant emotional exhaustion (ES = 0.43; CI: [0.33, 0.53]), depersonalization (ES = 0.29; CI: [0.13, 0.46]), and a low sense of personal accomplishment (ES = 0.36; CI: [0.08, 0.63]). CONCLUSIONS: Significant occupational burnout among ophthalmologists is concerning because burnout can have a negative effect on the physical and mental health of eye physicians and surgeons. It could impact productivity, cutbacks in work hours, or lead to early retirement from the profession. Contributing factors in ophthalmologist burnout including work overload need to be addressed in a timely manner.