Risk Stratification of Patients Admitted to Hospital with Covid-19 Using the ISARIC WHO Clinical Characterisation Protocol: Development and Validation of the 4C Mortality Score | Chapter 20 | Diagnostic and Treatment Advances in COVID-19 and SARS-CoV-2

Few models can perfectly predict coming events. Even having encountered the disastrous pandemic of SARS-COV-2, and the complex disease COVID-19 it initiated. Antiviral drugs and vaccines available now have very limited effects, and the novel strategies on the way have uncertain results. In addition, COVID-19 has greatly changed our views, from formerly being a predominantly pulmonary disease to a systematic syndrome affecting many organs. Furthermore, many more factors are tightly correlated with COVID-19, such as age, comorbidities, psychic situation, public healthy polices, even the politic aspects. Management and prevention partially under the guidance of a pragmatic risk stratification tool to predict mortality in patients admitted to hospital with COVID-19 become more and more important.

Author(s) Details:

Yang Jianshe,
Department of Gastroenterology of Third Affiliated Hospital, the Chinese University of Hong Kong, Shenzhen, China.

Kang Li,
Department of Gastroenterology the Longgang District Peoples' Hospital of Shenzhen, No.53 Aixin Road, Longgang District, Shenzhen 518172, China.

Please see the link here: https://stm.bookpi.org/DTACSC/article/view/9572

Lead -aVR of Electrocardiogram (ECG): An Orphan Lead | Chapter 06 | Issues and Developments in Medicine and Medical Research Vol. 4

 This chapter is designed to provide medical students, professors, and clinicians with a comprehensive understanding of -aVR ECG lead interpretation in diverse clinical scenarios. Until recently, among the twelve electrocardiogram leads, Lead –aVR was overlooked for its use (ECG). The augmented and unipolar limb lead -aVR was designed to obtain specific information from the right ventricle outflow tract (RVOT) and the basal portion of the interventricular septum on an electrocardiogram. The reciprocal information from the left lateral side is provided by lead -aVR, which is covered by the leads aVL, II, V5, and V6. This chapter is a collection of the most important facts about the –aVR lead in an ECG. An investigation of the ST-segment in lead -aVR can help predict the coronary angiographic anatomy in acute coronary syndrome (ACS). In an ACS scenario, lead -aVR provides useful information for risk stratification, diagnosis, prognosis prediction, and outcome. The Lead –aVR could be useful in diagnosing, predicting prognosis, and predicting short- and long-term outcomes in a variety of diseases.

Author(S) Details

Virendra C. Patil
Krishna Institute of Medical Sciences Deemed to be University, Karad, Satara, Maharashtra, India.

View Book:- https://stm.bookpi.org/IDMMR-V4/article/view/5543

Acute-phase Treatment and Prevention of Large Coronary Artery Lesions in Kawasaki Disease | Book Publisher International

 Kawasaki disease (KD) is a type of febrile systemic vasculitis that mostly affects children under the age of five. CALs, or coronary artery lesions, are a serious consequence of KD. However, there is no recognised acute-phase therapy for the prevention of high CALs and coronary artery stenosis induced by KD. I've analysed the results of clinical trials undertaken in our department on acute-phase treatment and prevention of big CALs in KD in this book.

Intravenous immunoglobulin (IVIG) therapy at 2 g/kg, along with medium- or high-dose aspirin, is the current standard treatment for acute-phase KD. However, the effectiveness of combining medium- or high-dose aspirin with 2 g/kg IVIG therapy has not been thoroughly studied. According to certain research, aspirin may impede CAL prevention in IVIG therapy and that the delayed use of aspirin (DUA) for IVIG therapy may be useful for suppressing CALs and preventing coronary artery stenosis in KD patients. CAL problems are linked to a number of causes. Because single IVIG therapy has no effect on the clinical course of KD, doctors can better manage treatment progress and administer rescue medications for IVIG resistance and KD relapse at the right moment. With acute-phase care using risk classification before and after initial treatment, initial single IVIG therapy (2 g/kg/dose) with DUA, and appropriate rescue therapies employing IVIG and plasma exchange, the majority of KD patients had excellent medium-term outcomes. Furthermore, a single dosage of 2 g/kg IVIG therapy combined with DUA exhibited advantages in terms of preventing coronary artery stenosis, lowering KD recurrence rates, cost-effectiveness, and protection against severe infections.

Author(S) Details

Toshimasa Nakada
Department of Pediatrics, Aomori Prefectural Central Hospital, Higashi- tukurimiti 2-1-1, Aomori City, Aomori Prefecture, Japan.

View Book:- https://stm.bookpi.org/ATPLCALKD/article/view/4663

Clinical Applications of Machine Learning in Stroke Care | Chapter 4 | New Frontiers in Medicine and Medical Research Vol. 8

 Stroke is a leading cause of mortality and disability in the world. Machine learning (ML) technologies have been progressively used in stroke therapy in recent years. This study looked at studies that used machine learning in stroke care to give a broad overview of the subject. Automatically examining carotid plaques (for stroke risk stratification), detecting stroke lesions on imaging, identifying possible treatment complications in patients, facilitating brain-computer-interface (BCI)-assisted rehabilitation, and predicting stroke prognosis have all been made possible thanks to advances in machine learning techniques. In domains like evaluating carotid intima-media thickness and diagnosing early damage of ischemic stroke using CT imaging, certain machine learning applications outperform doctors. Furthermore, ML applications in clinical outcome prediction perform similarly to or better than the traditional method of logistic regression. However, issues such as automated lesion segmentation, BCI-assisted rehabilitation, and long-term stroke prognosis prediction remain obstacles. Deep learning and other recently developed machine learning technologies may be helpful in overcoming the issues. To make these ML methods more accurate, generalizable, and dependable, more research is needed to test and optimise these ML applications, as well as large-sample studies and proper validation. As the need for precision medicine in stroke grows and machine learning technology advances, it is expected that machine learning applications will be released to improve computer-aided stroke prevention and transform traditional stroke medicine into data-driven personalised stroke management, which will lower morbidity and mortality rates, improve stroke care, and save lives.

Author (S) Details

Jing Zhang
Department of Neurology, School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA.

View Book :- https://stm.bookpi.org/NFMMR-V8/article/view/3107