Hypercoagulability and the need for prioritizing coagulation markers for prognostic abilities have been highlighted in COVID-19. We aimed to quantify the associations of D-dimer with disease progression in patients with COVID-19. This systematic review and meta-analysis was registered with PROSPERO, CRD42020186661.We included 113 studies in our systematic review, of which 100 records (n = 38,310) with D-dimer data) were considered for meta-analysis. Across 68 unadjusted (n = 26,960) and 39 adjusted studies (n = 15,653) reporting initial D-dimer, a significant association was found in patients with higher D-dimer for the risk of overall disease progression (unadjusted odds ratio (uOR) 3.15; adjusted odds ratio (aOR) 1.64). The time-to-event outcomes were pooled across 19 unadjusted (n = 9743) and 21 adjusted studies (n = 13,287); a strong association was found in patients with higher D-dimers for the risk of overall disease progression (unadjusted hazard ratio (uHR) 1.41; adjusted hazard ratio (aHR) 1.10). The prognostic use of higher D-dimer was found to be promising for predicting overall disease progression (studies 68, area under curve 0.75) in COVID-19. Our study showed that higher D-dimer levels provide prognostic information useful for clinicians to early assess COVID-19 patients at risk for disease progression and mortality outcomes. This study, recommends rapid assessment of D-dimer for predicting adverse outcomes in COVID-19.

We used a publicly available data of 44,672 patients reported by China’s centre for disease control to study the role of age, sex, co-morbidities and health-care related occupation on COVID-19 mortality. The data is in the form of absolute numbers and proportions. Using the percentages, retrospective synthetic data of 100 survivors and 100 deaths were generated using random number libraries so that proportions of ages, genders, co-morbidities, and occupations were constant as in the original data. Logistic regression of the four predictor factors of age, sex, co-morbidities and occupation revealed that only age and comorbidities significantly affected mortality. Sex and occupation when adjusted for other factors in the equation were not significant predictors of mortality. Age and presence of co-morbidities correlated negatively with survival with co-efficient of -1.23 and -2.33 respectively. Odds ratio (OR) for dying from COVID-19 for every 10-year increase in age was 3.4 compared to the previous band of 10 years. OR for dying of COVID-19 was 10.3 for the presence of any of the co-morbidities. Our findings could help in triaging the patients in the emergency room and emphasize the need to protect the elderly and those with comorbidities from getting exposed.

Coronavirus disease 2019, i.e. COVID-19, started as an outbreak in a district of China and has engulfed the world in a matter of 3 months. It is posing a serious health and economic challenge worldwide. However, case fatality rates (CFRs) have varied amongst various countries ranging from 0 to 8.91%. We have evaluated the effect of selected socio-economic and health indicators to explain this variation in CFR. Countries reporting a minimum of 50 cases as on 14th March 2020, were selected for this analysis. Data about the socio-economic indicators of each country was accessed from the World bank database and data about the health indicators were accessed from the World Health Organisation (WHO) database. Various socioeconomic indicators and health indicators were selected for this analysis. After selecting from univariate analysis, the indicators with the maximum correlation were used to build a model using multiple variable linear regression with a forward selection of variables and using adjusted R-squared score as the metric. We found univariate regression results were significant for GDP (Gross Domestic Product) per capita, POD 30/70 (Probability Of Dying Between Age 30 And Exact Age 70 From Any of Cardiovascular Disease, Cancer, Diabetes or Chronic Respiratory Disease), HCI (Human Capital Index), GNI(Gross National Income) per capita, life expectancy, medical doctors per 10000 population, as these parameters negatively corelated with CFR (rho = -0.48 to -0.38 , p<0.05). Case fatality rate was regressed using ordinary least squares (OLS) against the socio-economic and health indicators. The indicators in the final model were GDP per capita, POD 30/70, HCI, life expectancy, medical doctors per 10,000, median age, current health expenditure per capita, number of confirmed cases and population in millions. The adjusted R-squared score was 0.306. Developing countries with a poor economy are especially vulnerable in terms of COVID-19 mortality and underscore the need to have a global policy to deal with this on-going pandemic. These trends largely confirm that the toll from COVID-19 will be worse in countries ill-equipped to deal with it. These analyses of epidemiological data are need of time as apart from increasing situational awareness, it guides us in taking informed interventions and helps policy-making to tackle this pandemic.

No abstract

Endobronchial ultrasound guided transbronchial needle aspiration (EBUS-TBNA) has become the standard of care for sampling mediastinal and hilar lesions and is finding increased acceptance for diagnostic as well as staging purposes [1]. EBUS-TBNA is an expensive procedure due to the high cost of equipment [2]. A repeat procedure in case of an inconclusive outcome adds to the burgeoning expenditure. To circumvent this, rapid on-site examination (ROSE) has been adopted to reduce the number of needle punctures and decrease the requirement for additional procedures [3]. However, ROSE requires presence of a pathologist or cyto-technicians in the bronchoscopy suite.Artificial intelligence (AI), and deep neural networks in particular, have come a long way since their inception. AI is being used for microscopic examination of pathology images, with PAPNET being the most well-known application being used for screening of cervical cancer [4]. To the best of our knowledge, no study has been published to date on the application of AI during ROSE of EBUS-TBNA.We evaluated the performance of an AI model, consisting of an open-sourced convolutional neural network (CNN) using transfer learning, for its ability to accurately classify images of ROSE of EBUS-TBNA smears in the bronchoscopy suite. This study was approved by the institutional ethics committee (IEC number AIIMS/IEC/2020/3214).A total of 441 cytology images of ROSE of EBUS-TBNA smears were collected retrospectively from medical records of patients who underwent bronchoscopy from June 2019 to June 2020. The smears were stained with modified Giemsa stain and images were obtained using Olympus microscope at 400× magnification. Slides were examined by a pathologist who assigned them to classes based on the criteria adopted from ALSHARIF et al. [5], according to which a smear was labelled as adequate if either there were >40 lymphocytes per high power field (HPF) or presence of granulomas or malignant cells were noted; otherwise, it was categorised as inadequate. Consequent to this classification, the smears were categorised into the following four classes: 1) "granulomas" ( presence of one or more granulomas), 2) "adequate lymphocytes" (>40 lymphocytes per HPF), 3) "malignant cells" ( presence of malignant cells), 4) "inadequate" (meets none of the criteria above) (figure 1).All the images were downsized to 224×224 pixel size in JPG format, which is compatible with different open-sourced CNNs. 15% of the images from each category of smears were separated to form a test set (n=66) and rest of the images constituted the training set (n=375) for creation of a trained AI model and its subsequent evaluation. Different open-sourced convolutional neural networks of varying depths were assessed for their performance on the training dataset. The 19-layered VGG19 model released by the Visual Geometry Group (VGG), Oxford University was found to be the best in terms of accuracy and was used subsequently in this study [6]. VGG19 was implemented using Python through PyTorch. We used stoc...

No abstract

Introduction: Chloroquine and its analogues are currently being investigated for the treatment and post exposure prophylaxis of COVID-19 due to its antiviral activity and immunomodulatory activity. Material and methods: Confirmed symptomatic cases of COVID-19 were included in the study. Patients were supposed to receive chloroquine (CQ) 500 mg twice daily for 7 days. Due to a change in institutional protocol, initial patients received chloroquine and subsequent patients who did not receive chloroquine served as negative controls. Clinical effectiveness was determined in terms of timing of symptom resolution and conversion rate of reverse transcriptase polymerase chain reaction (RT-PCR) on day 14 and day 15 of admission. Results: Twelve COVID-19 patients formed the treatment arm and 17 patients were included in the control arm. The duration of symptoms among the CQ treated group (6.3 ± 2.7 days) was significantly (p-value = 0.009) lower than that of the control group (8.9 ± 2.2 days). There was no significant difference in the rate of RT-PCR negativity in both groups. 2 patients out of 12 developed diarrhea in the CQ therapy arm. Conclusion: The duration of symptoms among the treated group (with chloroquine) was significantly lower than that of the control group. RT-PCR conversion was not significantly different between the 2 groups.

Introduction: Machine learning algorithms have been used to develop prediction models in various infectious and non-infectious settings including interpretation of images in predicting the outcome of diseases. We demonstrate the application of one such simple automated machine learning algorithm to a dataset obtained about COVID-19 spread in South Korea to better understand the disease dynamics. Material and methods: Data from 20th January 2020 (when the first case of COVID-19 was detected in South Korea) to 4th March 2020 was accessed from Korea’s centre for disease control (KCDC). A future time-series of specified length (taken as 7 days in our study) starting from 5th March 2020 to 11th March 2020 was generated and fed to the model to generate predictions with upper and lower trend bounds of 95% confidence intervals. The model was assessed for its ability to reliably forecast using mean absolute percentage error (MAPE) as the metric. Results: As on 4th March 2020, 145,541 patients were tested for COVID-19 (in 45 days) in South Korea of which 5166 patients tested positive. The predicted values approximated well with the actual numbers. The difference between predicted and observed values ranged from 4.08% to 12.77% . On average, our predictions differed from actual values by 7.42% (MAPE) over the same period. Conclusion: Open source and automated machine learning tools like Prophet can be applied and are effective in the context of COVID-19 for forecasting spread in naïve communities. It may help countries to efficiently allocate healthcare resources to contain this pandemic.