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Background: In the palliative care setting, family members of terminally ill patients wish to be present for the last moments of the patientu2019s life. However, it is difficult to provide family members with an estimated time of patientu2019s death with accuracy. We hypothesized that dysfunction of the autonomic nervous system resulting in an imbalance between systolic blood pressure (SBP) and heart rate(HR) might precede cardiac arrest with a certain time interval. The aim of this study was to develop the algorithm to predict cardiac arrest in terminally ill patients.Methods: We retrospectively collected data of patients who were admitted to the intensive care unit (ICU) of Yokohama City University Hospital between 2010 and 2016. Exponential decay model was used to develop the formula to calculate the predicted HR from SBP. The disparity between predicted HR and real HR was evaluated as the predictor of cardiac arrest in the following 2 hours. Patient data were randomly divided into two groups. One was used to determine the threshold of the HR disparity with the receiver operating characteristic curve, and the other was used to validate the formula by determining the diagnostic power of the threshold.Results:u3000Of 4330 patients who were admitted to the ICU, 32 died in the ICU and 19 patients were included in this study. Exponential decay model determined the following formula: Predicted HR =SBP x(0.995 +5.936 e -0.035 x SBP). Area under the ROC of the disparity between real HR and predicted HR was 0.650 (95% confidence interval 0.512 to 0.788), p
Cardiac arrest can strike a seemingly healthy individual of any age, race, ethnicity, or gender at any time in any location, often without warning. Cardiac arrest is the third leading cause of death in the United States, following cancer and heart disease. Four out of five cardiac arrests occur in the home, and more than 90 percent of individuals with cardiac arrest die before reaching the hospital. First and foremost, cardiac arrest treatment is a community issue - local resources and personnel must provide appropriate, high-quality care to save the life of a community member. Time between onset of arrest and provision of care is fundamental, and shortening this time is one of the best ways to reduce the risk of death and disability from cardiac arrest. Specific actions can be implemented now to decrease this time, and recent advances in science could lead to new discoveries in the causes of, and treatments for, cardiac arrest. However, specific barriers must first be addressed. Strategies to Improve Cardiac Arrest Survival examines the complete system of response to cardiac arrest in the United States and identifies opportunities within existing and new treatments, strategies, and research that promise to improve the survival and recovery of patients. The recommendations of Strategies to Improve Cardiac Arrest Survival provide high-priority actions to advance the field as a whole. This report will help citizens, government agencies, and private industry to improve health outcomes from sudden cardiac arrest across the United States.
This book analyzes the main topics of Palliative Care in Cardiac Intensive Care Units (CICU), from the changing epidemiology of patients admitted to the ICU, to the main clinical and ethical issues. The changing epidemiology of patients has led to new and emerging patient needs at the end of life. Care has shifted from acute coronary syndrome patients towards elderly patients, with a high prevalence of non-ischemic cardiovascular diseases and a high burden of non-cardiovascular comorbid conditions: both increase the susceptibility of patients to developing life-threatening critical conditions. These conditions are associated with a significant symptom burden, high mortality rate, and increased length of stay. The main new challenges involve shared decision-making, symptom control (pain, dyspnea, etc.), and ethical issues (withholding/withdrawing life sustaining treatments, deactivation of implanted cardiac devices, palliative sedation), all of which necessitate formal education on end-of-life care. Written by opinion leaders in their respective fields, who share their experience with improving the cultural and clinical competence of medical/nursing teams, this volume is chiefly intended for cardiologists, anesthesiologists, palliative care doctors and nursing staff.
The Cardiac Arrest Prediction Using Machine Learning Model is a sophisticated system that leverages the power of machine learning algorithms to identify individuals who are at high risk of experiencing a cardiac arrest. This innovative solution aims to assist healthcare professionals in proactively identifying patients who may require immediate intervention or closer monitoring, thereby improving patient outcomes and potentially saving lives. The model is designed to analyze a variety of patient data, including medical history, vital signs, laboratory results, and other relevant clinical variables. By employing advanced machine learning techniques, the model learns patterns and relationships within the data to identify potential risk factors associated with cardiac arrest. The development of this model involves a two-step process. First, a comprehensive dataset is collected, consisting of anonymized patient information, including both historical data and real-time updates. This dataset is then used to train the machine learning model, which learns to recognize patterns and associations between different variables and the occurrence of cardiac arrest. Once the model is trained, it can be applied to new patient data in real-time. The system takes input from various sources, such as electronic health records, wearable devices, and continuous monitoring systems, to continuously assess a patient's risk of cardiac arrest. The model analyzes the incoming data and generates a prediction score or risk probability indicating the likelihood of a cardiac arrest event occurring within a specific timeframe. Healthcare professionals can utilize the prediction scores provided by the model to prioritize and allocate resources more efficiently. Patients identified as having a higher risk can receive immediate attention and proactive interventions to prevent cardiac arrest, such as medication adjustments, lifestyle modifications, or close monitoring in intensive care units. This targeted approach allows healthcare providers to intervene before the condition deteriorates, potentially improving patient outcomes and reducing mortality rates. The Cardiac Arrest Prediction Using Machine Learning Model is a promising advancement in healthcare technology, providing a proactive approach to cardiac care. By leveraging the power of machine learning algorithms and real-time patient data, it offers healthcare professionals valuable insights and tools to identify high-risk individuals, ultimately leading to improved patient care and better management of cardiac arrest risks. One needs both real-world experience and in-depth knowledge to make an accurate prediction of heart illness. Heart disease is now one of the most extremely dangerous and serious illnesses since it is difficult to diagnose. Thus, the ideal moment for both physicians and patients. Only when it can be correctly anticipated before a patient experiences a heart attack can cardiovascular illness be effectively diagnosed. This goal can be accomplished by combining a suitable machine learning approach with a significant volume of cardiovascular disease health information. In the modern digital era, data is an important resource, and a lot of data was being produced across many different businesses. The main origin of information in healthcare are data about the patients and information about illnesses. Tendencies in the sickness and provide individualised therapy for each patient by using healthcare information and ML techniques.
For patients and their loved ones, no care decisions are more profound than those made near the end of life. Unfortunately, the experience of dying in the United States is often characterized by fragmented care, inadequate treatment of distressing symptoms, frequent transitions among care settings, and enormous care responsibilities for families. According to this report, the current health care system of rendering more intensive services than are necessary and desired by patients, and the lack of coordination among programs increases risks to patients and creates avoidable burdens on them and their families. Dying in America is a study of the current state of health care for persons of all ages who are nearing the end of life. Death is not a strictly medical event. Ideally, health care for those nearing the end of life harmonizes with social, psychological, and spiritual support. All people with advanced illnesses who may be approaching the end of life are entitled to access to high-quality, compassionate, evidence-based care, consistent with their wishes. Dying in America evaluates strategies to integrate care into a person- and family-centered, team-based framework, and makes recommendations to create a system that coordinates care and supports and respects the choices of patients and their families. The findings and recommendations of this report will address the needs of patients and their families and assist policy makers, clinicians and their educational and credentialing bodies, leaders of health care delivery and financing organizations, researchers, public and private funders, religious and community leaders, advocates of better care, journalists, and the public to provide the best care possible for people nearing the end of life.
Now in paperback, the second edition of the Oxford Textbook of Critical Care is a comprehensive multi-disciplinary text covering all aspects of adult intensive care management. Uniquely this text takes a problem-orientated approach providing a key resource for daily clinical issues in the intensive care unit. The text is organized into short topics allowing readers to rapidly access authoritative information on specific clinical problems. Each topic refers to basic physiological principles and provides up-to-date treatment advice supported by references to the most vital literature. Where international differences exist in clinical practice, authors cover alternative views. Key messages summarise each topic in order to aid quick review and decision making. Edited and written by an international group of recognized experts from many disciplines, the second edition of the Oxford Textbook of Critical Careprovides an up-to-date reference that is relevant for intensive care units and emergency departments globally. This volume is the definitive text for all health care providers, including physicians, nurses, respiratory therapists, and other allied health professionals who take care of critically ill patients.
Thoroughly updated for its Sixth Edition, this classic reference remains an unsurpassed source of definitive, practical guidance on adult patient care in the ICU. It provides encyclopedic, multidisciplinary coverage of both medical and surgical intensive care and includes a "how-to" atlas of procedures and a new section on noninvasive monitoring. Each Sixth Edition chapter, for the first time, identifies Advances in Management based on randomized controlled clinical trials. The cardiology section has been completely rewritten to reflect advances in management of acute coronary syndromes. Also included are extensive updates on management of COPD, diabetes, oncologic emergencies, and overdoses and poisonings. A companion Website will provide instant access to the complete and fully searchable online text.