COVID-19-Induced Hyperleucocytosis in Chronic Lymphocytic Leukaemia.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus responsible for the current global pandemic, coronavirus disease 2019 (COVID-19). COVID-19 usually presents with respiratory symptoms but can affect multiple organ systems. A wide spectrum of complications can occur depending upon the comorbidities of patients. There is limited literature available regarding the presentation and outcome of COVID-19 in chronic lymphocytic leukaemia (CLL) patients. We report 2 cases of COVID-19-induced hyperleucocytosis (WBC count >100,000/µl) in CLL patients. LEARNING POINTS: Lymphopenia has been associated with severe disease and is a poor prognostic factor in COVID-19 infected patients; however, our cases show COVID-19-induced hyperleucocytosis (WBC count >100,000/µl)/lymphocytosis in CLL patients.Prior reports suggest that ibrutinib may have a protective effect against COVID-19 by decreasing inflammation and preventing progression to ARDS.

Coronavirus Disease 2019 and Cold Agglutinin Syndrome: An Interesting Case.

The coronavirus disease 2019 (COVID-19) pandemic has caused significant morbidity and mortality worldwide. While patients with COVID-19 most frequently present with pneumonia, respiratory failure and acute respiratory distress syndrome, increasing cases of immune-mediated disorders such as autoimmune thrombocytopenia, haemolytic anaemia and antiphospholipid syndrome have been reported. In this article we describe a rare case of cold agglutinin syndrome (CAS) in a patient with COVID-19. The patient was a 77-year-old man with a history of glucose-6-phosphate dehydrogenase (G6PD) deficiency who presented with COVID-19 infection and acute respiratory failure. Initially he was started on intravenous steroids, antibiotics and hydroxychloroquine. Laboratory analysis revealed haemolytic anaemia with a positive direct anti-globulin test (DAT) and high titres of cold agglutinins. Hydroxychloroquine was stopped due to suspicion of haemolysis due to G6PD deficiency but the haemolysis persisted. Unfortunately, the respiratory failure progressed and the patient died. In summary, this article describes a rare case of CAS associated with COVID-19. CAS is a heterogenous group of cold autoimmune haemolytic anaemias occurring secondary to infections or malignancies. No definite treatment for CAS in COVID-19 patients has been approved so far. LEARNING POINTS: Autoimmune haemolytic anaemia has been reported in COVID-19 patients.Cold agglutinin syndrome (CAS) can occur in patients with COVID-19.Efforts to determine the optimal management of CAS in COVID-19 patients must continue.

Remdesivir-Induced Bradycardia.

Remdesivir, a viral RNA-dependent RNA polymerase inhibitor, found extensive use in coronavirus disease 2019-infected patients because it curbs the viral load expansion. Among patients hospitalized as a result of lower respiratory tract infection, remdesivir proved to improve recovery time; however, remdesivir also can induce significant cytotoxic effects on cardiac myocytes. In this narrative review, we discuss the pathophysiological mechanism of remdesivir-induced bradycardia and diagnostic and management strategies for these patients. We conclude that further research is necessary to understand better the mechanism of bradycardia in coronavirus disease 2019 patients with or without cardiovascular disorder treated with remdesivir.

Association between frailty and clinical outcomes in hospitalised patients requiring Code Blue activation.

BACKGROUND: Code Blues allow a rapid, hospital wide response to acutely deteriorating patients. The concept of frailty is being increasingly recognised as an important element in determining outcomes of critically ill patients. We hypothesised that increasing frailty would be associated with worse outcomes following a Code Blue. AIMS: To investigate the association between increasing frailty and outcomes of Code Blues. METHODS: Single-centre retrospective design of patients admitted to Frankston Hospital in Australia between 1 January 2013 and 31 December 2017 who triggered a Code Blue. Frailty evaluation was made based on electronic medical records as were the details and the outcomes of the Code Blue. The primary outcome measure was a composite of hospital mortality or Cerebral Performance Categories scale ≥3. Secondary outcomes included the immediate outcome of the Code Blue and hospital mortality. RESULTS: One hundred and forty-eight of 911 screened patients were included in the final analysis. Seventy-three were deemed 'frail' and the remainder deemed 'fit'. Seventy-eight percent of frail patients reached the primary outcome, compared with 41% of fit patients (P < 0.001). Multivariable analysis demonstrated frailty to be associated with primary outcome (odds ratio = 2.87; 95% confidence interval (CI) 1.28-6.44; P = 0.01). A cardiac aetiology for the Code Blue was also associated with an increased odds of primary outcome (OR = 3.52; 95% CI 1.51-8.05; P = 0.004). CONCLUSIONS: Frailty is independently associated with the composite outcome of hospital mortality or severe disability following a Code Blue. Frailty is an important tool in prognostication for these patients and might aid in discussions regarding treatment limitations.

A cohort data-based perspective on ecological aspects predicaments in human health

The ecological aspects such as environmental factors, socioeconomic constraints and demographic parameters are one of the key aspects to examine the health benefits of human subject and used as ready reference in eco system modeling. Presently, there are various kinds of deadly diseases and disorders who are liable for affecting the human health and impacting the eco framework of whole world. The virus such as Corona, Swine Flu, omicron and others are one of the best examples for the research community to understand the vulnerability of human health in relation to these unpredictable causes. As per report of world health organization every year, more than 10 million people are affected by such ecological and environmental disbalance. The burden of ecological aspects apparently affecting the working of various organs in human subject. There is a need to understand this ecological model in relation to health of human subjects. In this study, a cohort-based data set of ecological pollutants and physiological signals such as ECG and anthropogenic data of human subjects were extracted from Maharashtra from 2015 to 2021. As per neural network-based hazard ratio was calculated and observed to be deplorable among unhealthy and health categories of human subjects. It has been concluded that the accumulative eco system is responsible for overburden to organs of living beings and policy makers must focus on the facts of study for modern management framework designs. [ FROM AUTHOR]

Seroprevalence of anti SARS-CoV-2 IgG antibodies among adults in Jammu district, India: A community-based study.

Background & objectives: Serology testing is essential for immunological surveillance in the population. This serosurvey was conducted to ascertain the cumulative population immunity against SARS-CoV-2 among adults in Jammu district and to understand the association of seropositivity with sociodemographic and clinical correlates. Methods: On September 30 and October 1, 2020, a household survey was done in 20 villages/wards chosen from 10 health blocks in district Jammu, India. Demographic, clinical and exposure information was collected from 2000 adults. Serum samples were screened for IgG antibodies using COVID Kavach MERILISA kit. Tests of association were used to identify risk factors associated with IgG positivity. Crude odds ratio with 95 per cent confidence intervals (CIs) was calculated during univariate analysis followed by logistic regression. Results: Overall adjusted seroprevalence for SARS-CoV-2 was 8.8 per cent (95% CI: 8.78-8.82); it varied from 4.1 per cent in Chauki choura to 16.7 per cent Pallanwalla across 10 blocks in the district. Seropositivity was observed to be comparatively higher in 41-50 and 61-70 yr age groups, among males and in rural areas. Fever, sore throat, cough, dyspnoea, myalgias, anosmia, ageusia, fatigue, seizures, history of exposure, medical consultation, hospitalization and missing work showed significant association with seropositivity on univariate analysis. On logistic regression, only sore throat, myalgia and missing work showed significant adjusted odds of IgG positivity. Extrapolation to adult population suggested that exposure to SARS-CoV-2 was 14.4 times higher than reported cases, translating into Infection fatality rate of 0.08 per cent. Interpretation & conclusions: Since a major part of population was immunologically naive, all efforts to contain COVID-19 need to be vigorously followed while these baseline results provide an important yardstick to monitor the trends of COVID-19 and guide locally appropriate control strategies in the region.

A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques.

BACKGROUND AND OBJECTIVE: COVID-19 outbreak has become one of the most challenging problems for human being. It is a communicable disease caused by a new coronavirus strain, which infected over 375 million people already and caused almost 6 million deaths. This paper aims to develop and design a framework for early diagnosis and fast classification of COVID-19 symptoms using multimodal Deep Learning techniques. METHODS: we collected chest X-ray and cough sample data from open source datasets, Cohen and datasets and local hospitals. The features are extracted from the chest X-ray images are extracted from chest X-ray datasets. We also used cough audio datasets from Coswara project and local hospitals. The publicly available Coughvid DetectNow and Virufy datasets are used to evaluate COVID-19 detection based on speech sounds, respiratory, and cough. The collected audio data comprises slow and fast breathing, shallow and deep coughing, spoken digits, and phonation of sustained vowels. Gender, geographical location, age, preexisting medical conditions, and current health status (COVID-19 and Non-COVID-19) are recorded. RESULTS: The proposed framework uses the selection algorithm of the pre-trained network to determine the best fusion model characterized by the pre-trained chest X-ray and cough models. Third, deep chest X-ray fusion by discriminant correlation analysis is used to fuse discriminatory features from the two models. The proposed framework achieved recognition accuracy, specificity, and sensitivity of 98.91%, 96.25%, and 97.69%, respectively. With the fusion method we obtained 94.99% accuracy. CONCLUSION: This paper examines the effectiveness of well-known ML architectures on a joint collection of chest-X-rays and cough samples for early classification of COVID-19. It shows that existing methods can effectively used for diagnosis and suggesting that the fusion learning paradigm could be a crucial asset in diagnosing future unknown illnesses. The proposed framework supports health informatics basis on early diagnosis, clinical decision support, and accurate prediction.

Patients with allergic asthma have lower risk of severe COVID-19 outcomes than patients with nonallergic asthma.

BACKGROUND: Although asthma does not appear to be a risk factor for severe coronavirus disease 2019 (COVID-19), outcomes could vary for patients with different asthma subtypes. The objective of this analysis was to compare COVID-19 outcomes in real-world cohorts in the United States among patients with asthma, with or without evidence of allergy. METHODS: In a retrospective analysis of the COVID-19 Optum electronic health record dataset (February 20, 2020-January 28, 2021), patients diagnosed with COVID-19 with a history of moderate-to-severe asthma were divided into 2 cohorts: those with evidence of allergic asthma and those without (nonallergic asthma). After 1:1 propensity score matching, in which covariates were balanced and potential bias was removed, COVID-19 outcomes were compared between cohorts. RESULTS: From a COVID-19 population of 591,198 patients, 1595 patients with allergic asthma and 8204 patients with nonallergic asthma were identified. After propensity score matching (n = 1578 per cohort), risk of death from any cause after COVID-19 diagnosis was significantly lower for patients with allergic vs nonallergic asthma (hazard ratio, 0.48; 95% CI 0.28-0.83; P = 0.0087), and a smaller proportion of patients with allergic vs nonallergic asthma was hospitalized within - 7 to + 30 days of COVID-19 diagnosis (13.8% [n = 217] vs 18.3% [n = 289]; P = 0.0005). Among hospitalized patients, there were no significant differences between patients with allergic or nonallergic asthma in need for intensive care unit admission, respiratory support, or COVID-19 treatment. CONCLUSIONS: Asthma subtype may influence outcomes after COVID-19; patients with allergic asthma are at lower risk for hospitalization/death than those with nonallergic asthma.

Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19.

Over the past few years, the awful COVID-19 pandemic effect has become a lethal sickness. The processing of the gathered samples requires extra time due to the use of medical diagnostic equipment, methodologies, and clinical testing procedures for the early diagnosis of infected individuals. An innovative multimodal paradigm for the early diagnosis and precise categorization of COVID-19 is put up as a solution to this issue. To extract distinguishing features from the prepared chest X-ray picture and cough (audio) database, chest X-ray-based and cough-based model are used here. Other public chest X-ray image datasets, and the Coswara cough (audio) dataset containing 92 COVID-19 positive, and 1079 healthy subjects (people) using the deep Uniform-Net, and Convolutional Neural Network (CNN). The weighted sum-rule fusion method and ensemble deep learning algorithms are utilized to further combine the extracted features. For the early diagnosis of patients, the framework offers an accuracy of 98.67%.

Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19☆

All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models. The proposed framework performs early classification of patients based on collected samples of chest X-ray images and Coswara cough (sound) samples of possibly infected people. The captured cough samples are pre-processed using speech signal processing techniques and Mel frequency cepstral coefficient features are extracted using deep convolutional neural networks. Finally, the proposed system fuses extracted features to provide 98.70% and 82.7% based on Chest-X ray images and cough (audio) samples for early diagnosis using the weighted sum-rule fusion method.

Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19☆

Over the past few years, the awful COVID-19 pandemic effect has become a lethal sickness. The processing of the gathered samples requires extra time due to the use of medical diagnostic equipment, methodologies, and clinical testing procedures for the early diagnosis of infected individuals. An innovative multimodal paradigm for the early diagnosis and precise categorization of COVID-19 is put up as a solution to this issue. To extract distinguishing features from the prepared chest X-ray picture and cough (audio) database, chest X-ray-based and cough-based model are used here. Other public chest X-ray image datasets, and the Coswra cough (audio) dataset containing 92 COVID-19 positive, and 1079 healthy subjects (people) using the deep Uniform-Net, and Convolutional Neural Network (CNN). The weighted sum-rule fusion method and ensemble deep learning algorithms are utilized to further combine the extracted features. For the early diagnosis of patients, the framework offers an accuracy of 98.67 percentage. Graphical

A Novel Multimodal Fusion Framework for Early Diagnosis and Accurate Classification of COVID-19 Patients Using X-ray Images and Speech Signal Processing Techniques

Background and objective: COVID-19 outbreak has become one of the most challenging problems for human being. It is a communicable disease caused by a new coronavirus strain, which infected over 375 million people already and caused almost 6 million deaths. This paper aims to develop and design a framework for early diagnosis and fast classification of COVID-19 symptoms using multimodal Deep Learning techniques. Methods: we collected chest X-ray and cough sample data from open source datasets, Cohen and datasets and local hospitals. The features are extracted from the chest X-ray images are extracted from chest X-ray datasets. We also used cough audio datasets from Coswara project and local hospitals. The publicly available Coughvid DetectNow and Virufy datasets are used to evaluate COVID-19 detection based on speech sounds, respiratory, and cough. The collected audio data comprises slow and fast breathing, shallow and deep coughing, spoken digits, and phonation of sustained vowels. Gender, geographical location, age, preexisting medical conditions, and current health status (COVID-19 and Non-COVID-19) are recorded. Results: the proposed framework uses the selection algorithm of the pre-trained network to determine the best fusion model characterized by the pre-trained chest X-ray and cough models. Third, deep chest X-ray fusion by discriminant correlation analysis is used to fuse discriminatory features from the two models. The proposed framework achieved recognition accuracy, specificity, and sensitivity of 98.91%, 96.25%, and 97.69%, respectively. With the fusion method we obtained 94.99% accuracy. Conclusion: this paper examines the effectiveness of well-known ML architectures on a joint collection of chest-X-rays and cough samples for early classification of COVID-19. It shows that existing methods can effectively used for diagnosis and suggesting that the fusion learning paradigm could be a crucial asset in diagnosing future unknown illnesses. The proposed framework supports health informatics basis on early diagnosis, clinical decision support, and accurate prediction.

Endoscopic Endonasal Eustachian Tube Obliteration as a Treatment for Tension Pneumocephalus After Translabyrinthine Resection of Vestibular Schwannoma.

OBJECTIVE: Cerebrospinal fluid leak and pneumocephalus are rare but potentially devastating complications associated with translabyrinthine resection of cerebellopontine angle masses. Persistent pneumocephalus despite proximal eustachian tube (ET) obliteration is rare. We describe, to our knowledge, the first report of successful management of tension pneumocephalus by endoscopic endonasal ET obliteration using a novel V-loc (Covidien; Medtronic, Minneapolis, MN) suture technique. PATIENTS: A 63-year-old man presented with altered mental status 10 months after translabyrinthine excision of a left cerebellopontine angle vestibular schwannoma measuring 2.8 × 2.9 × 3.3 cm. Computed tomography demonstrated diffuse ventriculomegaly and new pneumocephalus along the right frontal lobe, lateral ventricles, and third ventricle, and air within the left translabyrinthine resection cavity. INTERVENTION: The patient underwent left-sided endoscopic endonasal ET obliteration using 2-0, 9-inch V-loc suture. MAIN OUTCOME MEASURE: Postoperatively, the patient's mental status improved with a decrease in size of the lateral and third ventricles on computed tomography. CONCLUSION: Endoscopic endonasal ET obliteration, a technique previously applied to recalcitrant cerebrospinal fluid leaks, is a safe and reasonable alternative to reentering the original surgical site for patients with pneumocephalus after lateral skull base surgery. Utilizing a V-loc suture for this technique instead of a traditional suture may improve procedural ease and speed.

Non-hypertension-associated Posterior Reversible Encephalopathy Syndrome in COVID-19.

Background: Coronavirus disease-2019 (COVID-19) infection-related neurological events are not uncommon but presenting as posterior reversible encephalopathy syndrome (PRES) without hypertension is a very rare presentation and requires a high index of suspicion. Case summary: We report a case of a middle-aged female who presented with severe COVID-19 disease with no neurological symptoms. She complained of diminished vision on day 7 of the illness and underwent an MRI brain to rule out an ischemic stroke but the findings were suggestive of PRES. She had no episode of hypertension during the hospital stay. Probably severe COVID-related inflammation was the reason for such a presentation. Conservative management resolved the issue and her symptoms weaned off. Conclusion: Severe COVID disease can lead to PRES-like symptoms and requires neuroimaging to validate it. Conservative management is the best treatment for such patients. How to cite this article: Sharma D, Tomar DS, Gupta S. Non-hypertension-associated Posterior Reversible Encephalopathy Syndrome in COVID-19. Indian J Crit Care Med 2022;26(5):641-642.

Vitamin D: The Missing Nutrient Behind the Two Deadly Pandemics, COVID-19 and Cardiovascular Diseases.

The coronavirus (COVID-19) pandemic is claiming millions of lives and creating an additional burden on health care, which is already affected by the rise of non-communicable diseases (NCDs). The scientific community, on the other side, is enormously engaged with studies to best identify the characteristics of the virus and minimize its effect while supporting the fight to contain NCDs, mainly cardiovascular diseases (CVDs), which are contributing hugely to the global death toll. Hence, the roles of vitamin D in COVID-19 immunity and cardiovascular health are gaining traction recently.  This literature review will mainly focus on summarizing pertinent studies and scientific publications which highlight the association of vitamin D levels with the various outcomes of COVID-19 and CVDs. It will also address how low vitamin D correlates with the epidemiology of CVDs and the inflammatory mechanisms attributed to COVID-19 severity. We believe that our review may open up hindsight perspectives and further discussions among the physicians in tapping the potential of vitamin D supplementation to tackle the morbidity, mortality, and health care cost of the two deadly diseases, COVID-19 and CVDs.

COVID-19 Risk Prediction for Diabetic Patients Using Fuzzy Inference System and Machine Learning Approaches.

Individuals with pre-existing diabetes seem to be vulnerable to the COVID-19 due to changes in blood sugar levels and diabetes complications. As observed globally, around 20-50% of individuals affected by coronavirus had diabetes. However, there is no recent finding that diabetic patients are more prone to contract COVID-19 than nondiabetic patients. However, a few recent findings have observed that it could be at least twice as likely to die from complications of diabetes. Considering the multifold mortality rate of COVID-19 in diabetic patients, this study proposes a COVID-19 risk prediction model for diabetic patients using a fuzzy inference system and machine learning approaches. This study aimed to estimate the risk level of COVID-19 in diabetic patients without a medical practitioner's advice for timely action and overcoming the multifold mortality rate of COVID-19 in diabetic patients. The proposed model takes eight input parameters, which were found as the most influential symptoms in diabetic patients. With the help of the various state-of-the-art machine learning techniques, fifteen models were built over the rule base. CatBoost classifier gives the best accuracy, recall, precision, F1 score, and kappa score. After hyper-parameter optimization, CatBoost classifier showed 76% accuracy and improvements in the recall, precision, F1 score, and kappa score, followed by logistic regression and XGBoost with 75.1% and 74.7% accuracy. Stratified k-fold cross-validation is used for validation purposes.

Nebulised heparin for patients with or at risk of acute respiratory distress syndrome: a multicentre, randomised, double-blind, placebo-controlled phase 3 trial.

BACKGROUND: Mechanical ventilation in intensive care for 48 h or longer is associated with the acute respiratory distress syndrome (ARDS), which might be present at the time ventilatory support is instituted or develop afterwards, predominantly during the first 5 days. Survivors of prolonged mechanical ventilation and ARDS are at risk of considerably impaired physical function that can persist for years. An early pathogenic mechanism of lung injury in mechanically ventilated, critically ill patients is inflammation-induced pulmonary fibrin deposition, leading to thrombosis of the microvasculature and hyaline membrane formation in the air sacs. The main aim of this study was to determine if nebulised heparin, which targets fibrin deposition, would limit lung injury and thereby accelerate recovery of physical function in patients with or at risk of ARDS. METHODS: The Can Heparin Administration Reduce Lung Injury (CHARLI) study was an investigator-initiated, multicentre, double-blind, randomised phase 3 trial across nine hospitals in Australia. Adult intensive care patients on invasive ventilation, with impaired oxygenation defined by a PaO2/FiO2 ratio of less than 300, and with the expectation of invasive ventilation beyond the next calendar day were recruited. Key exclusion criteria were heparin allergy, pulmonary bleeding, and platelet count less than 50 X 109/L. Patients were randomly assigned 1:1, with stratification by site and using blocks of variable size and random seed, via a web-based system, to either unfractionated heparin sodium 25 000 IU in 5 mL or identical placebo (sodium chloride 0·9% 5 mL), administered using a vibrating mesh membrane nebuliser every 6 h to day 10 while invasively ventilated. Patients, clinicians, and investigators were masked to treatment allocation. The primary outcome was the Short Form 36 Health Survey Physical Function Score (out of 100) of survivors at day 60. Prespecified secondary outcomes, which are exploratory, included development of ARDS to day 5 among at-risk patients, deterioration of the Murray Lung Injury Score (MLIS) to day 5, mortality at day 60, residence of survivors at day 60, and serious adverse events. Analyses followed the intention-to-treat principle. There was no imputation of missing data. The trial is registered with the Australian and New Zealand Clinical Trials Register, number ACTRN12612000418875 . FINDINGS: Between Sept 4, 2012, and Aug 23, 2018, 256 patients were randomised. Final follow-up was on Feb 25, 2019. We excluded three patients who revoked consent and one ineligible participant who received no intervention. Of 252 patients included in data analysis, the mean age was 58 years (SD 15), 157 (62%) were men, and 118 (47%) had ARDS. 128 (51%) patients were assigned to the heparin group and 124 (49%) to the placebo group, all of whom received their assigned intervention. Survivors in the heparin group (n=97) had similar SF-36 Physical Function Scores at day 60 compared to the placebo group (n=94; mean 53·6 [SD 31·6] vs 48·7 [35·7]; difference 4·9 [95% CI -4·8 to 14·5]; p=0·32). Compared with the placebo group, the heparin group had fewer cases of ARDS develop to day 5 among the at-risk patients (nine [15%] of 62 patients vs 21 [30%] of 71 patients; hazard ratio 0·46 [95% CI 0·22 to 0·98]; p=0·0431), less deterioration of the MLIS to day 5 (difference -0·14 [-0·26 to -0·02]; p=0·0215), similar day 60 mortality (23 [18%] of 127 patients vs 18 [15%] of 123 patients; odds ratio [OR] 1·29 [95% CI 0·66 to 2·53]; p=0·46), and more day 60 survivors at home (86 [87%] of 99 patients vs 73 [73%] of 100 patients; OR 2·45 [1·18 to 5·08]; p=0·0165). A similar number of serious adverse events occurred in each group (seven [5%] of 128 patients in the heparin group vs three [2%] of 124 patients in the placebo group; OR 2·33 [0·59 to 9·24]; p=0·23), which were a transient increase in airway pressure during nebulisation (n=3 in the heparin group), major non-pulmonary bleeding (n=2 in each group), haemoptysis (n=1 in the heparin group), tracheotomy site bleeding (n=1 in the heparin group), and hypoxaemia during nebulisation (n=1 in the placebo group). INTERPRETATION: In patients with or at risk of ARDS, nebulised heparin did not improve self-reported performance of daily physical activities, but was well tolerated and exploratory outcomes suggest less progression of lung injury and earlier return home. Further research is justified to establish if nebulised heparin accelerates recovery in those who have or are at risk of ARDS. FUNDING: Rowe Family Foundation, TR and RB Ditchfield Medical Research Endowment Fund, Patricia Madigan Charitable Trust, and The J and R McGauran Trust Fund.

Tocilizumab in COVID-19: Is the Temptation Worthwhile?

How to cite this article: Gupta S, Tomar DS. Tocilizumab in COVID-19: Is the Temptation Worthwhile? Indian J Crit Care Med 2021;25(3): 247-248.

The use of direct acting oral anticoagulants in patients with COVID-19 infection.

The use of direct-acting oral anticoagulants (DOACs) has increased rapidly in the last decade; becoming the mainstay for both the prophylaxis and the treatment of venous thromboembolism in various situations including non-valvular atrial fibrillation, joint replacement surgeries and acute DVT/PE, etc. In the present times, DOACs are possibly one of the most widely prescribed medications in the developed world. The worldwide epidemic caused by COVID-19 caused significant changes in the practice of medicine worldwide. Patients who developed severe respiratory illness caused by COVID-19 were noted to develop a wide range of complications, including both arterial and venous thromboembolic complications including deep vein thrombosis and pulmonary embolism, etc. This review is an attempt to identify the role of DOACs in the treatment and prevention of these complications as well as the safety of continuing therapy with DOACs in the patients who were receiving them before contracting the infection.