Impact of the economic crisis, COVID-19 and the Beirut explosion on ophthalmology training in Lebanon: an observational cohort survey-based study.

OBJECTIVES: The objective of the study is to investigate the effects of the COVID-19 pandemic, the economic crisis and the Beirut explosion on the training and work of ophthalmology residents and faculty in Lebanon. DESIGN: This is an observational cohort survey-based research conducted between January and December 2022. SETTING: The study targeted all ophthalmology residents and core faculty in Lebanon. PARTICIPANTS: A total of 52 participants, including 27 residents and 25 core faculty members, completed the survey. PRIMARY OUTCOME MEASURE: Primary outcomes comprised the subjectively reported effect of the three major external stressors on the training and well-being of ophthalmology trainees and educators in Lebanon. RESULTS: The study found that the majority of ophthalmology residents and core faculty members were significantly affected by the COVID-19 pandemic, Beirut explosion and the economic crisis in Lebanon. Significant percentage reported financial burden, decrease in outpatient and surgical load and educational activities. Furthermore, most participants reported higher levels of stress, anxiety and depression during the time of crises. CONCLUSIONS: This study emphasises the need to support healthcare professionals during times of crisis, as they are on the frontlines and can experience high levels of stress, anxiety and depression. By providing support and resources to healthcare professionals, they can better cope with the challenges they face and continue to provide essential care to their patients.

Predicting incomplete occlusion of intracranial aneurysms treated with flow diverters using machine learning models.

OBJECTIVE: Intracranial saccular aneurysms are vascular malformations responsible for 80% of nontraumatic brain hemorrhage. Recently, flow diverters have been used as a less invasive therapeutic alternative for surgery. However, they fail to achieve complete occlusion after 6 months in 25% of cases. In this study, the authors built a tool, using machine learning (ML), to predict the aneurysm occlusion outcome 6 months after treatment with flow diverters. METHODS: A total of 667 aneurysms in 616 patients treated with the Pipeline embolization device at a tertiary referral center between January 2011 and December 2017 were included. To build the predictive tool, two experiments were conducted. In the first experiment, six ML algorithms (support vector machine [SVM], decision tree, random forest [RF], k-nearest neighbor, XGBoost, and CatBoost) were trained using 26 features related to patient risk factors and aneurysm morphological characteristics, and the results were compared with logistic regression (LR) modeling. In the second experiment, the models were trained using the top 10 features extracted by Shapley additive explanation (SHAP) analysis performed on the RF model. RESULTS: The results showed that the authors' tool can better predict the occlusion outcome than LR (accuracy of 89% for the SVM model vs 62% for the LR model), even when trained using a subset of the features (83% accuracy). SHAP analysis revealed that age, hypertension, smoking status, branch vessel involvement, aneurysm neck, and larger diameter dimensions were among the most important features contributing to accurate predictions. CONCLUSIONS: In this study, an ML-based tool was developed that successfully predicts outcome in intracranial aneurysms treated with flow diversion, thus helping neurosurgeons to practice a more refined approach and patient-tailored medicine.

Can machine learning models predict maternal and newborn healthcare providers' perception of safety during the COVID-19 pandemic? A cross-sectional study of a global online survey.

BACKGROUND: Maternal and newborn healthcare providers are essential professional groups vulnerable to physical and psychological risks associated with the COVID-19 pandemic. This study uses machine learning algorithms to create a predictive tool for maternal and newborn healthcare providers' perception of being safe in the workplace globally during the pandemic. METHODS: We used data collected between 24 March and 5 July 2020 through a global online survey of maternal and newborn healthcare providers. The questionnaire was available in 12 languages. To predict healthcare providers' perception of safety in the workplace, we used features collected in the questionnaire, in addition to publicly available national economic and COVID-19-related factors. We built, trained and tested five machine learning models: Support Vector Machine (SVM), Random Forest (RF), XGBoost, CatBoost and Artificial Neural Network (ANN) for classification and regression. We extracted from RF models the relative contribution of features in output prediction. RESULTS: Models included data from 941 maternal and newborn healthcare providers from 89 countries. ML models performed well in classification and regression tasks, whereby RF had 82% cross-validated accuracy for classification, and CatBoost with 0.46 cross-validated root mean square error for regression. In both classification and regression, the most important features contributing to output prediction were classified as three themes: (1) information accessibility, clarity and quality; (2) availability of support and means of protection; and (3) COVID-19 epidemiology. CONCLUSION: This study identified salient features contributing to maternal and newborn healthcare providers perception of safety in the workplace. The developed tool can be used by health systems globally to allow real-time learning from data collected during a health system shock. By responding in real-time to the needs of healthcare providers, health systems could prevent potential negative consequences on the quality of care offered to women and newborns.

Characteristics of a COVID-19 Cohort With Large Vessel Occlusion: A Multicenter International Study.

BACKGROUND: The mechanisms and outcomes in coronavirus disease (COVID-19)-associated stroke are unique from those of non-COVID-19 stroke. OBJECTIVE: To describe the efficacy and outcomes of acute revascularization of large vessel occlusion (LVO) in the setting of COVID-19 in an international cohort. METHODS: We conducted an international multicenter retrospective study of consecutively admitted patients with COVID-19 with concomitant acute LVO across 50 comprehensive stroke centers. Our control group constituted historical controls of patients presenting with LVO and receiving a mechanical thrombectomy between January 2018 and December 2020. RESULTS: The total cohort was 575 patients with acute LVO; 194 patients had COVID-19 while 381 patients did not. Patients in the COVID-19 group were younger (62.5 vs 71.2; P < .001) and lacked vascular risk factors (49, 25.3% vs 54, 14.2%; P = .001). Modified thrombolysis in cerebral infarction 3 revascularization was less common in the COVID-19 group (74, 39.2% vs 252, 67.2%; P < .001). Poor functional outcome at discharge (defined as modified Ranklin Scale 3-6) was more common in the COVID-19 group (150, 79.8% vs 132, 66.7%; P = .004). COVID-19 was independently associated with a lower likelihood of achieving modified thrombolysis in cerebral infarction 3 (odds ratio [OR]: 0.4, 95% CI: 0.2-0.7; P < .001) and unfavorable outcomes (OR: 2.5, 95% CI: 1.4-4.5; P = .002). CONCLUSION: COVID-19 was an independent predictor of incomplete revascularization and poor outcomes in patients with stroke due to LVO. Patients with COVID-19 with LVO were younger, had fewer cerebrovascular risk factors, and suffered from higher morbidity/mortality rates.

Clipping Could Be the Best Treatment Modality for Recurring Anterior Communicating Artery Aneurysms Treated Endovascularly.

BACKGROUND: The anterior communicating artery (AcoA) is the most common location for intracranial aneurysms. OBJECTIVE: To present occlusion outcomes, complication rate, recurrence rate, and predictors of recurrence in a large cohort with AcoA aneurysms treated primarily with endosaccular embolization. We also attempt to present data on the most effective treatment modality for recurrent AcoA aneurysms. METHODS: This is a retrospective, single-center study, reviewing the outcomes of 463 AcoA aneurysms treated endovascularly between 2003 and 2018. RESULTS: The study cohort consisted of 463 patients. Adequate immediate occlusion was achieved in 418 (90.3%). Independent functional status at discharge was observed in 269 patients (58.0%), and the mortality rate was 6.8% (31). At 6 months, adequate occlusion was achieved in 418 (90.4%). Of all the patients, recurrence was observed in 101 cases (21.8%), and of those, 98 (22.4%) underwent retreatment. The combined frequency of retreatment for the coiling group was 42.4%, which was significantly higher than the 0 incident of retreatment in the clipping group (P < .0001). Among the retreatment cohort, there was a significantly higher subsequent retreatment rate in the endovascular group (0% in the clipping group vs 42.4% in the endovascular group, P < .0001). CONCLUSION: Coiling with and without stent/balloon assistance is a relatively safe and effective modality for the treatment of AcoA aneurysms; however, in the setting of recurrence, microsurgical reconstruction leads to improved outcomes regarding durable occlusion, thus avoiding the potential for multiple interventions in the future.