Paediatric myopia shift during the COVID-19 pandemic home quarantine: a systematic review and meta-analysis.

BACKGROUND: Outdoor activities were restricted during the COVID-19 outbreak, although digital learning grew. Concerns have been raised about the impact of these environmental changes on myopia status. This research aimed to examine myopia shift during the COVID-19 pandemic and offer the community evidence-based data. METHODS: The literature search was undertaken in PubMed, SCOPUS, Science Direct, Web of Science and Google Scholar databases on published papers before 17 May 2022. The main outcome was mean spherical equivalent refraction (SER) before, at the onset and at the end of follow-up during the COVID-19 pandemic. RESULTS: Among 518 articles, 10 studies were included in the meta-analysis. The mean SER differences during the COVID-19 pandemic follow-up (mean follow-up time was 10 months) compared with before the pandemic was 0.15 dioptre (D) (95% CI -0.39 to 0.69, p=0.58). After age adjustment using meta-regression, the mean SER differences during the COVID-19 follow-up compared with before the pandemic was - 0.46 D (95% CI -0.59 to -0.34, p<0.001). Over the mean follow-up time during the COVID-19 pandemic, the SER mean difference was -0.55 D (95% CI -0.78 to -0.32, p<0.001), showing that the mean SER had decreased significantly during the COVID-19 pandemic. The mean SER differences in myopic patients before COVID-19 compared with during the pandemic follow-up was -0.49 D (95% CI -0.53 to -0.45, p=0.00). So the prior pandemic myopic patients became more myopic during the pandemic follow-up time. CONCLUSION: During home quarantine, the mean SRE shifting in paediatrics accelerated. This phenomenon should be given more attention by policymakers, eyecare experts, educators and parents.

Paediatric myopia shift during the COVID-19 pandemic home quarantine: a systematic review and meta-analysis

Background Outdoor activities were restricted during the COVID-19 outbreak, although digital learning grew. Concerns have been raised about the impact of these environmental changes on myopia status. This research aimed to examine myopia shift during the COVID-19 pandemic and offer the community evidence-based data. Methods The literature search was undertaken in PubMed, SCOPUS, Science Direct, Web of Science and Google Scholar databases on published papers before 17 May 2022. The main outcome was mean spherical equivalent refraction (SER) before, at the onset and at the end of follow-up during the COVID-19 pandemic. Results Among 518 articles, 10 studies were included in the meta-analysis. The mean SER differences during the COVID-19 pandemic follow-up (mean follow-up time was 10 months) compared with before the pandemic was 0.15 dioptre (D) (95% CI −0.39 to 0.69, p=0.58). After age adjustment using meta-regression, the mean SER differences during the COVID-19 follow-up compared with before the pandemic was – 0.46 D (95% CI −0.59 to −0.34, p<0.001). Over the mean follow-up time during the COVID-19 pandemic, the SER mean difference was −0.55 D (95% CI −0.78 to −0.32, p<0.001), showing that the mean SER had decreased significantly during the COVID-19 pandemic. The mean SER differences in myopic patients before COVID-19 compared with during the pandemic follow-up was −0.49 D (95% CI −0.53 to −0.45, p=0.00). So the prior pandemic myopic patients became more myopic during the pandemic follow-up time. Conclusion During home quarantine, the mean SRE shifting in paediatrics accelerated. This phenomenon should be given more attention by policymakers, eyecare experts, educators and parents.

A Deep Learning Approach to Identify Chest Computed Tomography Features for Prediction of SARS-CoV-2 Infection Outcomes.

There is still an urgent need to develop effective treatments to help minimize the cases of severe COVID-19. A number of tools have now been developed and applied to address these issues, such as the use of non-contrast chest computed tomography (CT) for evaluation and grading of the associated lung damage. Here we used a deep learning approach for predicting the outcome of 1078 patients admitted into the Baqiyatallah Hospital in Tehran, Iran, suffering from COVID-19 infections in the first wave of the pandemic. These were classified into two groups of non-severe and severe cases according to features on their CT scans with accuracies of approximately 0.90. We suggest that incorporation of molecular and/or clinical features, such as multiplex immunoassay or laboratory findings, will increase accuracy and sensitivity of the model for COVID-19 -related predictions.

Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features.

Background and aims Non-contrast chest computed tomography (CT) scanning is one of the important tools for evaluating of lung lesions. The aim of this study was to use a deep learning approach for predicting the outcome of patients with COVID-19 into two groups of critical and non-critical according to their CT features. Methods This was carried out as a retrospective study from March to April 2020 in Baqiyatallah Hospital, Tehran, Iran. From total of 1078 patients with COVID-19 pneumonia who underwent chest CT, 169 were critical cases and 909 were non-critical. Deep learning neural networks were used to classify samples into critical or non-critical ones according to the chest CT results. Results The best accuracy of prediction was seen by the presence of diffuse opacities and lesion distribution (both=0.91, 95% CI: 0.83-0.99). The largest sensitivity was achieved using lesion distribution (0.74, 95% CI: 0.55-0.93), and the largest specificity was for presence of diffuse opacities (0.95, 95% CI: 0.9-1). The total model showed an accuracy of 0.89 (95% CI: 0.79-0.99), and the corresponding sensitivity and specificity were 0.71 (95% CI: 0.51-0.91) and 0.93 (95% CI: 0.87-0.96), respectively. Conclusions The results showed that CT scan can accurately classify and predict critical and non-critical COVID-19 cases.

Global Incidence and Mortality Rate of COVID-19; Special Focus on Iran, Italy and China.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a new coronavirus, was diagnosed in China in December 2019. Around the globe, a total of 71429 were infected up to February 17, 2020, with 98.9% of cases in China. On March 11, 2020, the World Health Organization (WHO) characterized the COVID-19 as 'pandemic'. Rapid positive worldwide incidence was the motivation behind this study to investigate the incidence and mortality globally. METHODS: We used the data published by the WHO until March 9, 2020. Non-parametric tests and change point analysis were used for inferences. RESULTS: Change point analysis for Iran and China and the world excluding China for the first 20 days revealed around 78, 195 and 2 further new cases per day, respectively. Italy had a big jump in incidence on the 36th day. Similarly, a sharp rise of positive cases was reported for the world on the 35th day. China successfully controlled the ascending reports of incidence on the 23rd day. Mortality in China and the world were almost similar for the first 20 days. There was an ascending incidence trend with two change points in Italy (30th and 36th days) and one change point in Iran on the 17th day. Mortality in the world jumped remarkably after day 42 with an estimation of almost more than 25 deaths per day. CONCLUSION: The incidence of COVID-19 varied by regions; however, after March 11, it became 'pandemic'. It was observed that after about 6 days with an emergence of sharp increase in incidences, there would be a mutation in mortality rate. On the other hand, the importance of 'on-time' quarantine programs in controlling this virus was confirmed.