Approach to Thromboprophylaxis for Prevention of Venous Thromboembolism in COVID-19: Global Updates and Clinical Insights from India.

Venous thromboembolism (VTE) frequently occurs in patients with coronavirus disease-19 (COVID-19) and is associated with increased mortality. Several global guidelines recommended prophylactic-intensity anticoagulation rather than intermediate-intensity or therapeutic-intensity anticoagulation for patients with COVID-19-related acute or critical illness without suspected or confirmed VTE. Even though standard doses of thromboprophylaxis are received, many cases of thrombotic complications are reported; hence, appropriate and adequate thromboprophylaxis is critical for the prevention of VTE in COVID-19. In spite of an increased prevalence of VTE in Indian patients, sufficient data on patient characteristics, diagnosis, and therapeutic approach for VTE in COVID is lacking. In this article, we review the available global literature (search conducted up to 31 May 2021) and provide clinical insights into our approach towards managing VTE in patients with COVID-19. Furthermore, in this review, we summarize the incidence and risk factors for VTE with emphasis on the thromboprophylaxis approach in hospitalized patients and special populations with COVID-19 and assess clinical implications in the Indian context.

Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID).

In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individualized mortality risk scores based on the anonymized clinical and laboratory data at admission and determine the probability of Deaths at 7 and 28 days. Data of 1393 admitted patients (Expired-8.54%) was collected from six Apollo Hospital centers (from April to July 2020) using a standardized template and electronic medical records. 63 Clinical and Laboratory parameters were studied based on the patient's initial clinical state at admission and laboratory parameters within the first 24 h. The Machine Learning (ML) modelling was performed using eXtreme Gradient Boosting (XGB) Algorithm. 'Time to event' using Cox Proportional Hazard Model was used and combined with XGB Algorithm. The prospective validation cohort was selected of 977 patients (Expired-8.3%) from six centers from July to October 2020. The Clinical API for the Algorithm is  http://20.44.39.47/covid19v2/page1.php being used prospectively. Out of the 63 clinical and laboratory parameters, Age [adjusted hazard ratio (HR) 2.31; 95% CI 1.52-3.53], Male Gender (HR 1.72, 95% CI 1.06-2.85), Respiratory Distress (HR 1.79, 95% CI 1.32-2.53), Diabetes Mellitus (HR 1.21, 95% CI 0.83-1.77), Chronic Kidney Disease (HR 3.04, 95% CI 1.72-5.38), Coronary Artery Disease (HR 1.56, 95% CI - 0.91 to 2.69), respiratory rate > 24/min (HR 1.54, 95% CI 1.03-2.3), oxygen saturation below 90% (HR 2.84, 95% CI 1.87-4.3), Lymphocyte% in DLC (HR 1.99, 95% CI 1.23-2.32), INR (HR 1.71, 95% CI 1.31-2.13), LDH (HR 4.02, 95% CI 2.66-6.07) and Ferritin (HR 2.48, 95% CI 1.32-4.74) were found to be significant. The performance parameters of the current model is at AUC ROC Score of 0.8685 and Accuracy Score of 96.89. The validation cohort had the AUC of 0.782 and Accuracy of 0.93. The model for Mortality Risk Prediction provides insight into the COVID Clinical and Laboratory Parameters at admission. It is one of the early studies, reflecting on 'time to event' at the admission, accurately predicting patient outcomes.

Basic Critical Care for Management of COVID-19 Patients: Position Paper of Indian Society of Critical Care Medicine, Part-I.

With more than 23 million infections and more than 814,000 deaths worldwide, the coronavirus disease-2019 (COVID-19) pandemic is still far from over. Several classes of drugs including antivirals, antiretrovirals, anti-inflammatory, immunomodulatory, and antibiotics have been tried with varying levels of success. Still, there is lack of any specific therapy to deal with this infection. Although less than 30% of these patients require intensive care unit admission, morbidity and mortality in this subgroup of patients remain high. Hence, it becomes imperative to have general principles to guide intensivists managing these patients. However, as the literature emerges, these recommendations may change and hence, frequent updates may be required. How to cite this article: Juneja D, Savio RD, Srinivasan S, Pandit RA, Ramasubban S, Reddy PK, et al. Basic Critical Care for Management of COVID-19 Patients: Position Paper of Indian Society of Critical Care Medicine, Part-I. Indian J Crit Care Med 2020;24(Suppl 5):S244-S253.

Basic Critical Care for Management of COVID-19 Patients: Position Paper of the Indian Society of Critical Care Medicine, Part II.

In a resource-limited country like India, rationing of scarce critical care resources might be required to ensure appropriate delivery of care to the critically ill patients suffering from COVID-19 infection. Most of these patients require critical care support because of respiratory failure or presence of multiorgan dysfunction syndrome. As there is no pharmacological therapy available, respiratory support in the form of supplemental oxygen, noninvasive ventilation, and invasive mechanical ventilation remains mainstay of care in intensive care units. As there is still dearth of direct evidence, most of the data are extrapolated from the experience gained from the management of general critical care patients. How to cite this article: Juneja D, Savio RD, Srinivasan S, Pandit RA, Ramasubban S, Reddy PK, et al. Basic Critical Care for Management of COVID-19 Patients: Position Paper of the Indian Society of Critical Care Medicine, Part II. Indian J Crit Care Med 2020;24(Suppl 5):S254-S262.

Assigning computed tomography involvement score in COVID-19 patients: prognosis prediction and impact on management.

OBJECTIVE: Chest CT can provide a simple quantitative assessment of the extent of the parenchymal opacities in COVID-19 patients. In this study, we postulate that CT findings can be used to ascertain the overall disease burden and predict the clinical outcome. METHODS: In this prospective study undertaken from March 28, 2020, until May 20, 2020, 142 patients with CT features suggestive of viral pneumonia, and positive RT-PCR for COVID-19 were enrolled. A dedicated spiral CT scanner was used for all COVID-19 suspects. CT features were reported as typical, indeterminate, or atypical for COVID-19 pneumonia. A CT involvement score (CT-IS) was given to each scan and assigned mild, moderate, or severe category depending on the score range. The patients were followed up for at least 15 days. RESULTS: Ground glass opacity was present in 100% of the patients. There was a significant association between CT-IS and the final outcome of the patients. A statistically significant increasing trend of mortality and requirement of critical medical attention was observed with the rising value of CT-IS in COVID-19. CONCLUSION: The severe CT-IS score group has a high mortality. The CT-IS score could be valuable in predicting clinical outcome and could also be useful in triage of patients needing hospital admission. In situations where healthcare resources are limited, and patient load high, a more careful approach for patients with higher CT-IS scores could be indispensable. ADVANCES IN KNOWLEDGE: CT-IS is a simple quantitative method for assessing the disease burden of COVID-19 cases. It can be invaluable in places with limited resources and high patient load to segregate patients requiring critical medical attention.

COVID Collateral: "Don't Forget the Diligent Healthcare Worker".

How to cite this article: Suresh Ramasubban. COVID Collateral: "Don't Forget the Diligent Healthcare Worker". Indian J Crit Care Med 2020;24(8):613-614.