A review of venous thromboembolism risk assessment models for different patient populations: What we know and don't!

Venous thromboembolism (VTE) is a common cause of morbidity and mortality in hospitalized patients. Globally, it is also the third leading vascular disease, after myocardial infarction and stroke. The incidence of VTE is reportedly higher in Western countries than in Asian countries. However, recent reports suggest an increasing incidence of VTE in Asian countries, including India. Since VTE is largely a preventable disease, early identification of risk factors can lead to disease prevention or the adoption of appropriate prophylactic measures. To this end, several VTE risk assessment models (RAMs) have been developed and validated for different populations who are at risk of developing VTE, such as hospitalized patients with medical illness/surgical indication, patients with cancer, and pregnant women. Evidence indicates that the systematic use of RAMs improves prophylaxis rates and lowers the burden of VTE. Given the increasing burden of VTE in the Indian population and poor prophylaxis rates, the implementation of systematic RAMs in routine clinical practice might ameliorate the disease burden in the country. We have assessed the evidence-based utilities of available RAMs and have delineated the most common and suitable RAMs for different populations including coronavirus disease 2019 affected patients. This review depicts the current status of implementation and validation of RAMs in the Indian scenario. It also highlights the need for additional validation studies, improved awareness, and implementation of RAMs in clinical practice for lowering the burden of VTE.

Dysglycemia in ICU Patients

Dysglycemia has emerged as a very common challenge in critically ill patients, especially with regard to current coronavirus disease 2019 pandemic. Prediabetes, poorly controlled diabetes, pharmaceutical intervention in intensive care unit (ICU) with glucocorticoids, catecholamines and other medicines, and stress response all contribute to dysglycemia in critically ill patients. Early identification and management are the key to prevent further complications. Patient prognosis in terms of clinical outcome, length of ICU stay, and in-hospital morbidity/mortality are adversely affected by patient's dysglycemic status. Apart from hyperglycemia, the other three important pillars of dysglycemia are discussed in this article. Synopsis of early intervention have been captured from India-specific practice guidelines. Important landmark trials have also been captured in this article to provide a clarity on certain aspects of managing dysglycemia in ICUs. Hence, this review article is an attempt to bring forth the salient aspects in diagnosing and managing dysglycemia in critical care settings.

Smart Gateway Screening for COVID-19

Novel Corona-virus is spreading all over the world. Millions of people have been infected with this disease and unfortunately thousands have lost their lives. Countless others have experienced the impact of Covid-19 on their health as well as in their day-to-day life. For the past one and half year as of May - 2021, the world balance and the economics have changed. Entire workflow has been shifted online, with people continuing to face a lot of difficulties due to this sudden change. But there will be a moment when normalcy will be restored, businesses bereopened and all the offices as well as other institutions start functioning as normal. This will be the most vulnerable momentas people will come in contact with each other and hence there will be a danger of this disease spreading in mass again. Hence, a system needs to be adopted to curb its spread and this can be achieved by identifying people showing the symptoms and thereby preventing them from entering organizations with a lot of attendees. The proposed system implements mask and temperature detection, sanitation while also providing a touch- less Attendance Management system for taking the attendance. © 2021 IEEE

Seroconversion in COVID-19: A tool for risk assessment in frontline COVID warriors

Objectives : To analyse the seroconversion rate of COVID-19 antibody and its association with risk of future infection in frontline COVID warriors. Methodology: Longitudinal cohort study conducted in 218 frontline COVID warriors. Baseline anti-spike IgG antibodies estimated and followed up for RT-PCR positive infection. Seropositive subjects were followed up with serial titres at 4 visits upto 6 months. Statistical analysis: SPSS ver. 22.0 used employing chi-square test for association of seropositivity with RT-PCR outcome. Antibody kinetics was calculated using a mathematical linear regression model. Incidence rate was calculated per 10000 person years at risk and relative risk was calculated. Results: IgG positivity was seen in 93.8% subjects who had COVID infection in past 30 days. Significantly higher incidence of infection was observed in seronegative subjects who were at 10 times higher risk of infection than seropositive cases (p<0.001). A sharp rise in infectivity was seen in August 2020 which declined after 2 months. Antibody titres peaked during 1.5-3months followed by steep decline till 4th-5th month and gradual decline till 6 months. Only 2 asymptomatic cases turned seronegative by the end of study. Conclusion: Analysis of natural antibody response postulated a persistence of antibodies till 6 months post RT PCR confirmed infection. The seronegative subjects were 10 times more prone to COVID infection due to lack of innate immunity.

Dynamics of anti SARS CoV2 IGG antibody response in health care workers: A prospective cohort study from India

Objectives: To analyse COVID-19 antibody response, duration of protection, half-life kinetics and its association with disease severity in health care workers (HCW's). Methodology: Prospective cohort study conducted in 230 HCW's of a 1250-bedded tertiary care hospital in India. Baseline IgG antibodies estimated and followed up for RT-PCR positive infection. Seropositive HCWs were followed up with serial titres every 45 days uptil 6 months. Statistical analysis: SPSS ver. 22.0 used employing chi-square test for association of seropositivity with PCR outcome. Assuming log-normal distribution of antibody-titres, half-life estimated using linear regression model. Adjusted risk ratio was computed for incidence rate in baseline seronegative versus seropositive cases. Results: Seroconversion rate was 93.8% in seropositive HCWs at 30 days post infection. Incidence rate of infection was 12.96 (in seronegatives) and 1.29 (in seropositives) per 10,000 person days. Adjusted risk ratio was 8.12 (95% CI 1.068 61.755, p<0.001). Incidence of future infection was inversely associated with baseline seropositivity (p=0.018) with spike in infectivity seen during Aug-Sep 2020 and again in Nov.2020. A peak in mean titres seen at 46-90 day follow-up followed by steep decline till 135 days and gradual waning till 180 days. Median half-life was 125 days (62-155 days). 95.7% seropositive cases sustained seropositivity till the end of study and only 2 asymptomatic ones demonstrated complete seroreversion. Conclusion: Dynamics of humoral immune response revealed an 8-times higher risk of infection in seronegative HCW's. Anti SARS CoV2 IgG antibodies persist for at least 6 months post-infection, offering significant protective immunity against reinfection.

Dynamics of SARS-CoV-2 Antibody Response in a Longitudinal Cohort of Healthcare Workers from India

Introduction: Coronavirus Disease 2019 (COVID-19) pandemic has affected healthcare systems worldwide. Healthcare Workers (HCWs) form one of the most at-risk population groups for acquiring infection. Trend analysis of anti Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) antibody titres in vaccination naïve HCWs will give an insight into the role of natural protective immunity against re-infection. Aim: To understand the dynamics of anti SARS-CoV-2 antibody response and its protective role against re-infection in a cohort of HCWs. Materials and Methods: This observational longitudinal cohort study was conducted in a tertiary care hospital in Gurugram, North India from June to December 2020. The study was approved by the Institutional Ethics Committee. Serum specimens from 230 HCWs were tested for anti-spike protein Immunogloublin G (IgG) antibodies by chemiluminescence immunoassay. The HCWs with positive antibody status and previous Polymerase Chain Reaction (PCR) confirmed infection (n=47) were followed-up over 180 days for serial antibody titres at four visits, each at a gap of 30-45 days. Participants were classified into asymptomatic (n=18), mild (n=17) and moderate (n=12) disease categories based on severity of previous COVID-19 illness. SPSS version 22.0 was used for statistical analysis. Intergroup comparison of means was done using Kruskal-Wallis test and chi-square test. p<0.05 was considered statistically significant. Results: Positivity rate for anti SARS-CoV-2 IgG antibodies was 25.7%. Seroconversion rate was 90.74% in HCWs with history of previous Real Time-Polymerase Chain Reaction (RT-PCR) confirmed COVID-19 infection. Incidence of infection in seronegative group (n=171) was 12.96 per 10,000 person days while in seropositive group, it was 1.29 per 10,000 person days. Risk ratio for infection (baseline seronegative vs baseline seropositive) was determined to be 8.12 [95% Confidence Interval (CI) 1.068-61.755]. Incidence of PCR confirmed SARS-CoV-2 re-infection was inversely associated with antibody titres (p=0.018). Antibody response trend showed a peak in mean titres in the 46-90 days period followed by steep decline till 135 days and a gradual waning till 180 days. Conclusion: Significant postinfection immunity is offered by even low to moderate amounts of antibodies and this occurs regardless of whether a seropositive HCW had previous asymptomatic or symptomatic infection. These findings have significant implications in establishing the protective role of anti-spike protein antibodies against subsequent infection.

COVID-Inflicted Coagulopathy: Expert Consensus on Management with Novel Oral Anticoagulants in India

Coronavirus disease 2019 (COVID-19) is a highly hypercoagulable viral infection complicated as COVID-inflicted coagulopathy (CIC), that is associated with increased risk of morbidity and mortality. International guidelines recommend low molecular weight heparin (LMWH) to treat CIC in both in-hospital and in-home settings. However, in India, using subcutaneous LMWH may not be a feasible option for a vast majority of patients under home management. Additionally, while some evidence advocates the use of novel oral anticoagulants (NOACs), in hospitalized settings, most guidelines find no role of NOACs in hospital settings. On the other hand, the resource crunch faced in recent COVID-19 pandemic in India forced physicians to treat many patients in home settings. These patients had been usually prescribed NOACs for ease of administration and adherence. Therefore, there is a need to form a consensus on the use of NOACs to manage CIC in India. © 2021 Journal of Association of Physicians of India. All rights reserved.

Smart Approach for Identification of Pneumonia Using Real-Time Convolutional Neural Networks

Pneumonia is one of symptom cause for one of the most widespread Contagious diseases emerged as Coronavirus 2019 known as CoVID-19. This diseases is sharply spreading in most of part of the world effecting small aged and elderly aged people which is causing more than Nine Thousand deaths from all parts of the world. Identification of Pneumonia in Covid cases is happened by analysis of Posteroanterior views (P.A.) X-Rays. Thus, developing a smart and efficient system for detecting Pneumonia must-have utilities even in the future. In Recent Researches, Convolutional neural networks were found to be more accurate and efficient. Nowadays, Features are pre-trained in CNN models on a vast scale of datasets that make medical image identification more efficient. However, analyzing the medical image for Pneumonia’s detection is hard to assess because professional expertise is needed to label them. This Paper shows how a convolutional neural network can be used to detect Pneumonia using any generic python notebook with computer vision support. © 2021, Springer Nature Singapore Pte Ltd.

Neutrophil-to-lymphocyte ratio and D-dimer assay in prediction of disease severity in covid positive Indian patients in a tertiary care centre

Aims & Objectives: To evaluate role of hematological parameters in prediction of disease severity and also analyze the trends of NLR and D-Dimer during its course. Patients/Materials & Methods: A retrospective analysis of 83 patients diagnosed with COVID-19 by RT-PCR at Medanta-the Medicity hospital in June 2020 was done. The data included neutrophil- to-lymphocyte ratio (NLR), D-Dimer, PT/APTT and platelet count. The patients were divided into 20 critical patients and 63 Noncritical patients group, based on disease severity. The parameters were compared and trends analyzed. Results: The COVID positive cases had a mean age of 56.7 years (Range: 7-84 years) with a male:female ratio of 2.6:1. The critical group had mean age of 64.7 years (Range: 42-76), versus 54.1 years (Range: 7-84) in non-critical group. At admission, the mean NLR in the critical and non-critical group was 12.26 and 5.7. Further, the critical and non-critical group had NLR>3.13 in 19 cases (95%) and 49 cases (77.8%), respectively. On receiver operating characteristic curve (ROC) analysis, predictive ability of NLR for detection of critical patients was significant(p value = 0.0001;AUC:0.779) with optimal cut-off value of 6.01, having 85% sensitivity, 68.9% specificity and ∼ 93.5% negative predictable value (NPV). The mean D-Dimer value in critical and non-critical group on Day-4 of admission was 18.89 mg/L and 2.48 mg/L. Moreover, the D-Dimer>0.55 mg/L were seen in 17 critical cases (85%) in contrast with 35 non-critical cases (55.5%). On ROC analysis, the ability of D-Dimer in predicting disease severity was significant( p value = 0.0001;AUC:0.896) with optimal cut-off value of 2.27 mg/L, having 85% sensitivity, 76.2% specificity and NPV ∼ 94.1%. On trend analysis, it was observed that the D-Dimer and NLR showed a progressive upward trend in critical patients, whereas there were more of plateau/declining values in non-critical patients. PT was mildly prolonged in 14 critical patients (70%) and 13 non- critical patients (40.6%). The mean platelet counts were similar in both the groups. Discussion & Conclusion: The study shows that the severity of the disease is more in elderly (Mean age: 64.7 years). Also, at admission NLR>6.05 and Day-4 D-Dimer>2.27 mg/L are significantly predictive of disease severity and such patients should receive prompt treatment to minimize further sequel and morbidities.

Management of Coronavirus 2019

Coronavirus 2019 (COVID-19) disease is the most recent global public health problem. It is caused by SARS-CoV-2 (severe acute respiratory syndrome related coronavirus 2), which is a RNA virus with a high mutation rate, belonging to the genus Coronavirus. The objective of this communication is to provide an initial understanding regarding pathophysiology, clinical manifestations, management, and prevention of this devastating disease.

An Non-Invasive Approach of Corona Genome Detection

Corona virus is formulated from large group of the viruses that causes illness range from common cold to more severe diseases. This virus has transmitted from animals to human beings. Millions of people throughout the world are affected to this deadly virus. So it is vital to detect this threatening virus. The Genomic Signal processing techniques is a constructive tool to detect infection as it deals with the advance research in the genetics. In this article we delineate the Genomic sequences of Corona Virus infected samples and convert the genomic data into digital signal and on applying Discrete Wavelet Transform and Fast Fourier Transform we performed mathematical modeling of the Corona-Virus gene so that it becomes effortless to predict the Corona-Virus Gene statistically. The motive of this research is to provide predicted results to drug designer for better response to this life threating Virus. We have tested these algorithms on most significant genes of the Corona Virus as well as Normal Cells of Homosapiens. The dataset of all the genomes are available on the National Center of Biotechnology Information (NCBI) website. © 2020 IEEE.