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On March 11, 2020, the World Health Organization declared COVID-19—the infectious disease caused by SARS-CoV-2—a pandemic. Since then, the majority of countries—including Spain—have imposed strict restrictions in order to stop the spread of the virus and the collapse of the health systems. People’s health care–seeking behavior has exhibited a change, not only in those months when the COVID-19 control measures were strictest, but also in the months that followed. We aimed to examine how the trends in ophthalmological emergencies changed during the COVID-19 pandemic in one of the largest tertiary referral hospitals in Spain. To this end, data from all the patients that attended the ophthalmological emergency department during the pandemic period—March 2020 to February 2021—were retrospectively collected and compared with data from the previous year. Moreover, a comparison between April 2020—when the restrictions were most severe—and April 2019 was made. A total of 90,694 patients were included. As expected, there was a decrease in the number of consultations. There was also a decrease in the frequency of conjunctival pathology consultations. These changes may bring to light not only the use that people make of the emergency department, but also the new trends in ophthalmological conditions derived from the hygienic habits that the COVID-19 pandemic has established.

PurposeTo describe findings for orbital magnetic resonance imaging (MRI) in patients with age-related distance esotropia (ARDE).MethodsWe compared 31 orbital MRI from patients with ARDE (77 ± 7 SD years) with 2 control groups: 32 orbits from individuals aged 18–50 years (33 ± 8 SD years) and 16 orbits from individuals aged >60 years (77 ± 7 SD years). MRI scans were acquired using 3D fast field echo in T1 sequence without fat saturation. Exclusion criteria for all groups were neurological or thyroid disease and a relevant ophthalmological history (e.g., high myopia, diplopia from another etiology, complicated cataract surgery, etc.). Muscle displacement and characteristics of the lateral rectus–superior rectus (LR–SR) intermuscular band were analyzed.ResultsThe analysis of the muscles and angles revealed a series of statistically significant differences (p < 0.07) between the groups. Subjects with ARDE had LR pulley positions 1.32 ± 0.19 mm lower than in younger controls, and the medial rectus (MR) pulley positions were 0.68 ± 0.19 mm lower than in younger. Older controls had LR and MR pulley positions 0.85 ± 0.20 mm and 0.49 ± 0.23 mm lower than in younger. ARDE subjects had LR pulley positions 0.46 ± 0.26 mm lower than in older control group. The LR–SR band was absent in 35.5% of ARDE patients and in 12.5% of older control group (p = 0.168).ConclusionsMRI showed that displacements of LR and LR–SR band degeneration could facilitate the diagnosis of patients with ARDE.

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Purpose/Objective(s): PET/CT has a key role in the management of esophageal cancer; however, improved prediction models would be useful for clinical decision making. In particular, ability to predict pathologic complete response (pCR) to chemoradiation would help spare patients from an esophagectomy, a high-risk procedure with significant morbidity. Machine learning is a promising approach to develop models that can predict unseen data, but the efficacy of these models in predicting the onset of esophageal cancer has not been established. We assessed the classification accuracy of prediction models for pathologic complete response based on commonly used machine learning techniques of quantitative pretreatment PET/CT features. Materials/Methods: We identified 39 patients with esophageal cancer who completed neoadjuvant chemoradiation followed by surgery (trimodality therapy) and had pretreatment PET/CT at our institution from 2007-2015. PET/CT images were rigidly fused with radiation planning CT scans based on spine anatomy at the level of gross disease. The following quantitative PET/CT features were evaluated in the clinical target volume (CTV): mean uptake, max uptake, total lesion glycolysis (TLG), and volume. Based on these 4 features, 5 commonly used machine learning techniques (i.e., k nearest neighbors, decision tree, support vector machines, Naive Bayes, decision analysis) were employed to predict the pathologic response (binary classification). Using a leave-one-out cross-validation, the resulting prediction accuracies of these techniques were compared. Results: There were 29 male (74%) and 10 female patients included in this study (median age 61 years, 85% adenocarcinoma, 15% squamous cell carcinoma, all patients stage IIB-IIIC). Median follow-up was 17 months. Pathologic complete response was achieved in 10 patients (26%). Most disease recurrences were distant (13 patients), with 1 local recurrence. Median overall survival was 17 months. Across the 5 machine learning techniques, the average classification accuracy of predicting the pathological response was 0.60 (range, 0.47-0.74) with k nearest neighbors having greatest accuracy at 0.74. Conclusion: Prediction models with fair-to-good prediction accuracy can be developed using simple quantitative pretreatment PET/CT features for patients undergoing trimodality therapy for esophageal cancer. K nearest neighbors may be an especially appropriate method due to robustness to nonlinear data. These data suggest promise in applying machine learning algorithms on quantitative PET data to aid clinical decision making in larger patient populations. Further prospective studies are needed to validate this dataset.