Critical Care Clinics

This article provides a broad overview of key concepts and more commonly encountered critical illness presentations in adult congenital heart disease (ACHD) patients. General principles are discussed, and the need for ACHD subspecialty consultation is emphasized. ACHD is categorized based on hemodynamic profile, and common clinical presentations are reviewed, including common pitfalls. Many ACHD lesions are associated with predictable complications, and awareness of these associations can guide evaluation and management, which are listed in this article...

Durable left ventricular assist devices (LVADs) are a virtually limitless advanced therapy option for an increasingly growing population of patients with end-stage advanced heart failure. As of 2019, 30% to 40% of all patients diagnosed with heart failure were categorized as New York Heart Association class III or IV. In 2018 more than 3.2 million office visits and 1.4 million emergency department visits carried a primary diagnosis of heart failure. Given the rapid growth of the LVAD population, facility in the diagnosis and management of common perioperative and outpatient LVAD emergencies has become of paramount importance in a variety of clinical settings...

The contemporary practice of ultrasonography in the cardiac intensive care unit integrates the principles of echocardiography with whole-body imaging to create a more expansive paradigm of critical care ultrasonography (CCUS). This article will review the use of CCUS for diagnostic assessment, monitoring, therapeutic guidance, and prognosis.

Heart transplantation (HT) remains the best treatment of patients with severe heart failure who are deemed to be transplant candidates. The authors discuss postoperative management of the HT recipient by system, emphasizing areas where care might differ from other cardiac surgery patients. Working together, critical care physicians, heart transplant surgeons and cardiologists, advanced practice providers, pharmacists, transplant coordinators, nursing staff, physical therapists, occupational therapists, rehabilitation specialists, nutritionists, health psychologists, social workers, and the patient and their loved ones partner to increase the likelihood of a successful outcome...

Pulmonary hypertension (PH) encompasses a broad range of conditions, including pulmonary artery hypertension, left-sided heart disease, and pulmonary and thromboembolic disorders. Successful diagnosis and management rely on an integrated clinical assessment of the patient's physiology and right heart function. Right ventricular (RV) heart failure is often a result of PH, but may result from varying abnormalities in preload, afterload, and intrinsic myocardial dysfunction, which require distinct management strategies...

Valvular heart disease pathologies are commonly encountered in the cardiac intensive care unit (CICU). Clinical presentations may range from an acute pathology of the aortic or mitral valve necessitating emergency intervention to a more subtle decompensation of longstanding valvular disease. With growing numbers of transcatheter valvular interventions, CICU providers must recognize and manage common complications after transcatheter aortic, mitral, and tricuspid interventions. In addition, prosthetic valve dysfunction should always be excluded in a CICU patient presenting with an acute cardiopulmonary decompensation...

Coronary care units (CCUs) were originally designed to monitor and treat peri-infarction ventricular arrhythmias but have evolved into highly specialized cardiac intensive care units (CICUs) that provide care to a patient population that is increasingly heterogeneous and complex. Paralleling broader epidemiologic trends, patients admitted to contemporary CICUs are older and have a greater burden of cardiovascular and non-cardiovascular comorbidities. Moreover, contemporary CICU patients have high illness severity and often present with acute noncardiac organ dysfunction...

No abstract text is available yet for this article.

Critical care data contain information about the most physiologically fragile patients in the hospital, who require a significant level of monitoring. However, medical devices used for patient monitoring suffer from measurement biases that have been largely underreported. This article explores sources of bias in commonly used clinical devices, including pulse oximeters, thermometers, and sphygmomanometers. Further, it provides a framework for mitigating these biases and key principles to achieve more equitable health care delivery...

This article provides an overview of the most useful artificial intelligence algorithms developed in critical care, followed by a comprehensive outline of the benefits and limitations. We begin by describing how nurses and physicians might be aided by these new technologies. We then move to the possible changes in clinical guidelines with personalized medicine that will allow tailored therapies and probably will increase the quality of the care provided to patients. Finally, we describe how artificial intelligence models can unleash researchers' minds by proposing new strategies, by increasing the quality of clinical practice, and by questioning current knowledge and understanding...

Predictive analytics based on artificial intelligence (AI) offer clinicians the opportunity to leverage big data available in electronic health records (EHR) to improve clinical decision-making, and thus patient outcomes. Despite this, many barriers exist to facilitating trust between clinicians and AI-based tools, limiting its current impact. Potential solutions are available at both the local and national level. It will take a broad and diverse coalition of stakeholders, from health-care systems, EHR vendors, and clinical educators to regulators, researchers and the patient community, to help facilitate this trust so that the promise of AI in health care can be realized...

Syndromic conditions, such as sepsis, are commonly encountered in the intensive care unit. Although these conditions are easy for clinicians to grasp, these conditions may limit the performance of machine-learning algorithms. Individual hospital practice patterns may limit external generalizability. Data missingness is another barrier to optimal algorithm performance and various strategies exist to mitigate this. Recent advances in data science, such as transfer learning, conformal prediction, and continual learning, may improve generalizability of machine-learning algorithms in critically ill patients...

Large volumes of data are collected on critically ill patients, and using data science to extract information from the electronic medical record (EMR) and to inform the design of clinical trials represents a new opportunity in critical care research. Using improved methods of phenotyping critical illnesses, subject identification and enrollment, and targeted treatment group assignment alongside newer trial designs such as adaptive platform trials can increase efficiency while lowering costs. Some tools such as the EMR to automate data collection are already in use...

The practice of medicine is characterized by uncertainty, and the findings of randomized clinical trials (RCTs) are meant to help curb that uncertainty. Traditional RCTs, however, have many limitations. To overcome some of these limitations, new trial paradigms rooted in the origins of evidence-based medicine are beginning to disrupt the traditional mold. These new designs recognize uncertainty permeates medical decision making and aim to capitalize on modern health system infrastructure to integrate investigation as a component of care delivery...

Data science has the potential to greatly enhance efforts to translate evidence into practice in critical care. The intensive care unit is a data-rich environment enabling insight into both patient-level care patterns and clinician-level treatment patterns. By applying artificial intelligence to these novel data sources, implementation strategies can be tailored to individual patients, individual clinicians, and individual situations, revealing when evidence-based practices are missed and facilitating context-sensitive clinical decision support...

Electronic medical records (EMRs) constitute the electronic version of all medical information included in a patient's paper chart. The electronic health record (EHR) technology has witnessed massive expansion in developed countries and to a lesser extent in underresourced countries during the last 2 decades. We will review factors leading to this expansion, how the emergence of EHRs is affecting several health-care stakeholders; some of the growing pains associated with EHRs with a particular emphasis on the delivery of care to the critically ill; and ongoing developments on the path to improve the quality of research, health-care delivery, and stakeholder satisfaction...

Perioperative morbidity and mortality are significantly associated with both static and dynamic perioperative factors. The studies investigating static perioperative factors have been reported; however, there are a limited number of previous studies and data sets analyzing dynamic perioperative factors, including physiologic waveforms, despite its clinical importance. To fill the gap, the authors introduce a novel large size perioperative data set: Machine Learning Of physiologic waveforms and electronic health Record Data (MLORD) data set...

The rapid adoption of electronic health record (EHR) systems in US hospitals from 2008 to 2014 produced novel data elements for analysis. Concurrent innovations in computing architecture and machine learning (ML) algorithms have made rapid consumption of health data feasible and a powerful engine for clinical innovation. In critical care research, the net convergence of these trends has resulted in an exponential increase in outcome prediction research. In the following article, we explore the history of outcome prediction in the intensive care unit (ICU), the growing use of EHR data, and the rise of artificial intelligence and ML (AI) in critical care...

Precision medicine aims to identify treatments that are most likely to result in favorable outcomes for subgroups of patients with similar clinical and biological characteristics. The gaps for the development and implementation of precision medicine strategies in the critical care setting are many, but the advent of data science and multi-omics approaches, combined with the rich data ecosystem in the intensive care unit, offer unprecedented opportunities to realize the promise of precision critical care. In this article, the authors review the data-driven and technology-based approaches being leveraged to discover and implement precision medicine strategies in the critical care setting...

No abstract text is available yet for this article.