Severe Thrombocytopenia, Thrombosis and Anti-PF4 Antibody after Pfizer-BioNTech COVID-19 mRNA Vaccine Booster-Is It Vaccine-Induced Immune Thrombotic Thrombocytopenia?

Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a serious and life-threatening complication occurring after adenovirus-vector COVID-19 vaccines, and is rarely reported after other vaccine types. Herein, we report a case of possible VITT after the Pfizer-BioNTech mRNA vaccine booster, who presented with extensive lower limb deep vein thrombosis, severe thrombocytopenia, markedly elevated D-dimer and positive anti-PF4 antibody occurring 2 weeks post-vaccination, concurrent with a lupus anticoagulant. A complete recovery was made after intravenous immunoglobulin, prednisolone and anticoagulation with the oral direct Xa inhibitor rivaroxaban. The presenting features of VITT may overlap with those of antiphospholipid syndrome associated with anti-PF4 and immune thrombocytopenia. We discuss the diagnostic considerations in VITT and highlight the challenges of performing VITT confirmatory assays in non-specialized settings. The set of five diagnostic criteria for VITT is a useful tool for guiding initial management, but may potentially include patients without VITT. The bleeding risks of severe thrombocytopenia in the face of thrombosis, requiring anticoagulant therapy, present a clinical challenge, but early recognition and management can potentially lead to favorable outcomes.

Global haemostatic tests demonstrate the absence of parameters of hypercoagulability in non-hypoxic mild COVID-19 patients: a prospective matched study.

Severe COVID-19 patients demonstrate hypercoagulability, necessitating thromboprophylaxis. However, less is known about the haemostatic profile in mild COVID-19 patients. We performed an age and gender-matched prospective study of 10 severe and 10 mild COVID-19 patients. Comprehensive coagulation profiling together with Thromboelastography and Clot Waveform Analysis were performed. FBC, PT, APTT, D-dimer, fibrinogen and CWA were repeated every 3 days for both groups and repeat TEG was performed for severe patients up till 15 days. On recruitment, severe patients had markers reflecting hypercoagulability including raised median D-dimer 1.0 µg/mL (IQR 0.6, 1.4) (p = 0.0004), fibrinogen 5.6 g/L (IQR 4.9, 6.6) (p = 0.002), Factor VIII 206% (IQR 171, 203) and vWF levels 265.5% (IQR 206, 321). Mild patients had normal values of PT, aPTT, fibrinogen and D-dimer, and slightly elevated median Factor VIII and von Willebrand factor (vWF) levels. Repeated 3-day assessments for both groups showed declining trends in D-dimer and Fibrinogen. CWA of severe COVID-19 group demonstrated hypercoagulability with an elevated median values of aPTT delta change 78.8% (IQR 69.8, 85.2) (p = 0.001), aPTT clot velocity (min1) 7.8%/s (IQR 6.7, 8.3) (p = 0.001), PT delta change 22.4% (IQR 19.4, 29.5) (p = 0.004), PT min1 7.1%/s (IQR 6.3, 9.0) (p = 0.02), PT clot acceleration (min 2) 3.6%/s2 (IQR 3.2, 4.5) (p = 0.02) and PT clot deceleration (max2) 2.9%/s2 (IQR 2.5, 3.5) (p = 0.02). TEG of severe patients reflected hypercoagulability with significant increases in the median values of CFF MA 34.6 mm (IQR 27.4,38.6) (p = 0.003), CRT Angle 78.9° (IQR 78.3, 80.0) (p = 0.0006), CRT A10 67.6 mm (IQR 65.8, 69.6) (p = 0.007) and CFF A10 32.0 mm (IQR 26.8, 34.0) (p = 0.003). Mild COVID-19 patients had absent hypercoagulability in both CWA and TEG. 2 severe patients developed thromboembolic events while none occurred in the mild COVID-19 group. Mild COVID-19 patients show absent parameters of hypercoagulability in global haemostatic tests while those with severe COVID-19 demonstrated parameters associated with hypercoagulability on the global haemostatic tests together with raised D-Dimer, fibrinogen, Factor VIII and vWF levels.

The Implementation of AI and AI-Empowered Imaging System to Fight Against COVID-19—A Review Implementation of Machine Learning Techniques for the Analysis of Transmission Dynamics of COVID-19 Data-Driven Decision Making in IoT Healthcare Systems—COVID-19: A Case Study Touch and Voice-Assisted Multilingual Communication Prototype for ICU Patients Specific to COVID’19 Cerebral Venous Thrombosis post BNT162b2 mRNA SARS-CoV-2 vaccination: A Black Swan Event

Summary COVID-19 has already affected the world with this deadly virus, resulting in over 3.5 lakh deaths. The behavior of this virus is extraordinarily peculiar and mutates frequently. So, the scientific community faces the problems to analyze and forecast the virus's growth and transmission capability. The combined effort of powerful Artificial intelligence and Image processing techniques to predict the initial pattern of COVID-19 disease identifies the most affected areas in each country through social networking information and predicts drug-protein interactions for making new drugs vaccines. However, AI-empowered X-Ray and computed tomography image acquisition and segmentation techniques help us identify and diagnose the COVID-19 affected patients with minimal contact. In this chapter, our primary motivation is to sum up the essential roles of some AI-driven techniques (Machine learning, Deep learning, etc.) and AI-empowered imaging techniques to analyze, predict, and diagnose against COVID-19 disease. An essential set of open challenges and future research issues on AI-empowered procedures for handling COVID-19 are also discussed in this chapter. Summary This paper mainly deals with the design of Machine Learning model for the analysis of transmission dynamics of Covid 19. The entire globe is affected because of Corona virus. Ventilator dependent, Severe Acute respiratory and quarantine care ICU patients frequently face difficulties for their most basic human interactions, namely communication due to either respiratory illness, language problem or intubated. ICU patients have serious implications with respect to physical and psychological due to non communication problems. Researchers have developed different types of services like Speech language Pathologist so that Augmentative and alternative communication assistance can be given to all health professionals and caretakers. A probabilistic model is designed to analyse the new cases and death cases. Using machine learning approach Regression model is designed and future predications are displayed. The adequacy of the model is discussed along with the residuals of new cased and death cases. PCF and APCAF are obtained. This paper mainly deals with a probabilistic model to analyse and predict the new cases and deaths of covid 19. A new transformation of analyzing stationarity is carried out and based on this forecasting is executed. Summary This research express an impression of automated decision-making techniques that have been suggested for scrutiny of data from IoT based healthcare systems. IoT data analytics plays a vital role in this modern era since data from connected devices reveal meaningful results with better insights for the future. The chapter involves the design of a decision-making system that collects data from IoT based healthcare systems, preprocess and analyzes data, and generates detailed information reports for better diagnosis. Data preprocessing methods such as data cleaning, munging, normalization, reduction, and removing noisy data are applied. The blend of IoT data with analytics technique results to be beneficial in healthcare systems. The collected IoT information like pulse rate, temperature, oxygen level and heart rate from connected devices can be used to analyze the need and severity in the preliminary stage itself using appropriate machine learning techniques. Multi Criteria Decision Making (MCDM) techniques such as SMART, WPM, and TOPSIS are also applied for conclusion production procedure to generate detailed informative diagnostic reports. Being healthcare data, the overall objective is to aid business organizations with better decision making processes through data analytics thereby deploying the right IoT strategy. The result of the next-generation expert systems can utilize the results for further analysis in diagnosis and treatment. Summary The proposed work deals with the design and development of touch and native voice-assisted prototype to enable the intuitive communication & interaction between health professionals and patients who are affected with Severe Acute Respiratory Infection (SARI), Ventilator-dependent and admitted in Quarantine care. It also ensures the development of the multilingual capability to communicate effectively in most speaking ten Indian languages, so that the patients will be relieved from pains etc., as their queries are being addressed by health professionals. In this prototype, touch based gesture patterns can be effectively used as an interactive module and helps the doctors to monitor and answer to the queries of ICU patients regularly by updating it to the caretakers such that the patients are at ease to express their emotions or pains. The proposed prototype will be made available and accessible in an open software repository. As per the existing methods patients express their needs through non-verbal communication methods and they could be missed out or misinterpreted resulting in symptoms that are poorly understood and the clinicians overestimate their ability to understand their communication feelings. These situations are eradicated by employing the use of ?Touch Voice of SARI? Application. Hence this can be considered as an assistive communication tool which replaces the nonverbal communication to a meaningful communication for ventilator patients and healthcare professionals.