Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, in late 2019 and created a global pandemic that overwhelmed healthcare systems. COVID-19, as of July 3, 2021, yielded 182 million confirmed cases and 3.9 million deaths globally according to the World Health Organization. Several patients who were initially diagnosed with mild or moderate COVID-19 later deteriorated and were reclassified to severe disease type. OBJECTIVE: The aim is to create a predictive model for COVID-19 ventilatory support and mortality early on from baseline (at the time of diagnosis) and routinely collected data of each patient (CXR, CBC, demographics, and patient history). METHODS: Four common machine learning algorithms, three data balancing techniques, and feature selection are used to build and validate predictive models for COVID-19 mechanical requirement and mortality. Baseline CXR, CBC, demographic, and clinical data were retrospectively collected from April 2, 2020, till June 18, 2020, for 5739 patients with confirmed PCR COVID-19 at King Abdulaziz Medical City in Riyadh. However, of those patients, only 1508 and 1513 have met the inclusion criteria for ventilatory support and mortalilty endpoints, respectively. RESULTS: In an independent test set, ventilation requirement predictive model with top 20 features selected with reliefF algorithm from baseline radiological, laboratory, and clinical data using support vector machines and random undersampling technique attained an AUC of 0.87 and a balanced accuracy of 0.81. For mortality endpoint, the top model yielded an AUC of 0.83 and a balanced accuracy of 0.80 using all features with balanced random forest. This indicates that with only routinely collected data our models can predict the outcome with good performance. The predictive ability of combined data consistently outperformed each data set individually for intubation and mortality. For the ventilator support, chest X-ray severity annotations alone performed better than comorbidity, complete blood count, age, or gender with an AUC of 0.85 and balanced accuracy of 0.79. For mortality, comorbidity alone achieved an AUC of 0.80 and a balanced accuracy of 0.72, which is higher than models that use either chest radiograph, laboratory, or demographic features only. CONCLUSION: The experimental results demonstrate the practicality of the proposed COVID-19 predictive tool for hospital resource planning and patients' prioritization in the current COVID-19 pandemic crisis.

Sustainability of Ophthalmology Practice and Training During and Post the Pandemic of Coronavirus (COVID-19): A Review.

PURPOSE: To review and analyse the globally established ophthalmic practice protocols during the coronavirus disease (COVID-19). METHODS: A literature review using search strategy was conducted to identify appropriate publications relevant to COVID-19 and ophthalmology practice and training. The safety and feasibility of the protocols were illustrated and discussed. RESULTS: Challenges in different eye care settings at various international ophthalmology departments have identified and analysed to introduce solutions. Several clinical protocols were established and concerned for screening procedures, waiting area, clinical flow (ie, patients' registration, personal (patients and healthcare workers) protection), and equipment safety in the clinics and operation rooms. DISCUSSION: In the review of this protocol, the strategic and operational missions of the Academic Medical Centers (AMCs) are demonstrated and discussed. This is in addition to the sustainability of the established protocols for cataract surgeries and glaucoma clinics and training during and after COVID-19. CONCLUSION: All the protocols have established for temporary circumstances, such as postponing elective appointments and surgeries as well as applying the technology for regular follow-ups (transmission of image, video, and face-to-face interactions via widely available applications). Only, one protocol was stronger for the sustainability. Accordingly, recommendations are suggested for clinical sustainability during and after COVID-19.

Ocular transmission and manifestation for coronavirus disease: a systematic review.

BACKGROUND: There is currently a lack of information regarding ocular tropism and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Globally, the cumulative number of coronavirus disease 2019 (COVID-19) cases is increasing daily. Thus the potential for ocular transmission and manifestations of SARS-CoV-2 requires more investigation. METHODS: A systematic search of electronic databases for ocular transmission and manifestations of SARS-CoV-2 was performed. Pooled cross-sectional studies were used for conducting a meta-analysis to estimate the prevalence of ocular transmission of SARS-CoV-2 to the respiratory system and ocular manifestations (associated symptoms) of SARS-CoV-2. RESULTS: The highest prevalence of SARS-CoV-2-positive tears using reverse transcription polymerase chain reaction was found to be 7.5%. However, the highest prevalence of ocular conjunctivitis associated with SARS-CoV-2 was 32%. Thus, SARS-CoV-2 can evidently infect the eye, as revealed in the conjunctival secretions of COVID-19 patients. CONCLUSION: The available data reflect the influence of the ocular structure on SARS-CoV-2. The analysis showed that ocular manifestation is an indication for SARS-CoV-2, particularly conjunctivitis. Moreover, there is no evidence that the ocular structure can be an additional path of transmission for SARS-CoV-2, however, it warrants further investigation.