Advances in chronic kidney disease最新文献

Although medical schools across the United States have updated their curricula to incorporate active learning techniques, there has been little discussion on the nature of the content presented to students. Here, we share detailed examples of our experience in using original experiments to lay the groundwork for foundational concepts in renal physiology and pathophysiology. We believe that this approach offers distinct advantages over standard case-based teaching by (1) starting with simple concepts, (2) analyzing memorable visuals, (3) increasing graphical literacy, (4) translating observations to “rules,” (5) encouraging critical thinking, and (6) providing historical perspective to the study of medicine. Although we developed this content for medical students, we have found that many of these lessons are also appropriate as foundational concepts for residents and fellows and serve as an excellent springboard for increasingly complex discussions of clinical applications of physiology. The use of original experiments for teaching and learning in renal physiology harnesses skills in critical thinking and provides a solid foundation that will help learners with subsequent case-based learning in the preclerkship curriculum and in the clinical arena.

There is no consistent educational model to introduce the physician assistant and/or nurse practitioner to nephrology. The job descriptions of the nephrology physician assistant/nurse practitioner may be similar, but the training, state and federal licensing, background, and recertification are different for the 2 professions adding a level of complexity to the training of the physician assistant/nurse practitioner new to nephrology. On-the-job training is the most common modality, but formats, content, mentors, and practices vary from organization to organization and even within organizations. The advantage of on-the-job training is its flexibility while the disadvantage is its nonspecific outcomes. As nephrology practices vary widely and range from single provider private practices to multiprovider academic practices, it is difficult if not impossible to develop a generic orientation model. This article outlines the history and present state of postgraduate educational offerings for the physician assistant/nurse practitioner and provides insight into components of an ideal training program.

Detecting protected health information in electronic health record systems is often an early step in health care analytics, and it is a nontrivial problem. Specific challenges include finding clinician names and diseases, which lack a fixed format and are often context-dependent. The general problem of finding entities, termed named-entity recognition, has received a substantial amount of attention in the natural language processing and deep learning communities. This paper begins by outlining recent methods for finding protected health information, and it then introduces a hybrid system which combines regular expressions with a natural language processing framework called FLAIR. FLAIR is open-source, it includes state-of-the-art deep learning models, and it supports straightforward development of new models for language tasks including named-entity recognition. Finally, there is a discussion of how to apply the system to structured text in a database table as well as unstructured text in clinical notes.

Unstructured data in the electronic health records contain essential patient information. Natural language processing (NLP), teaching a computer to read, allows us to tap into these data without needing the time and effort of manual chart abstraction. The core first step for all NLP algorithms is preprocessing the text to identify the core words that differentiate the text while filtering out the noise. Traditional NLP uses a rule-based approach, applying grammatical rules to infer meaning from the text. Newer NLP approaches use machine learning/deep learning which can infer meaning without explicitly being programmed. NLP use in nephrology research has focused on identifying distinct disease processes, such as CKD, and extraction of patient-oriented outcomes such as symptoms with high sensitivity. NLP can identify patient features from clinical text associated with acute kidney injury and progression of CKD. Lastly, inclusion of features extracted using NLP improved the performance of risk-prediction models compared to models that only use structured data. Implementation of NLP algorithms has been slow, partially hindered by the lack of external validation of NLP algorithms. However, NLP allows for extraction of key patient characteristics from free text, an infrequently used resource in nephrology.

We reviewed some of the latest advancements in the use of mathematical models in nephrology. We looked over 2 distinct categories of mathematical models that are widely used in biological research and pointed out some of their strengths and weaknesses when applied to health care, especially in the context of nephrology. A mechanistic dynamical system allows the representation of causal relations among the system variables but with a more complex and longer development/implementation phase. Artificial intelligence/machine learning provides predictive tools that allow identifying correlative patterns in large data sets, but they are usually harder-to-interpret black boxes. Chronic kidney disease (CKD), a major worldwide health problem, generates copious quantities of data that can be leveraged by choice of the appropriate model; also, there is a large number of dialysis parameters that need to be determined at every treatment session that can benefit from predictive mechanistic models. Following important steps in the use of mathematical methods in medical science might be in the intersection of seemingly antagonistic frameworks, by leveraging the strength of each to provide better care.

Continuous renal replacement therapy (CRRT) is widely utilized to support critically ill patients with acute kidney injury. Artificial intelligence (AI) has the potential to enhance CRRT delivery, but evidence is limited. We reviewed existing literature on the utilization of AI in CRRT with the objective of identifying current gaps in evidence and research considerations. We conducted a scoping review focusing on the development or use of AI-based tools in patients receiving CRRT. Ten papers were identified; 6 of 10 (60%) published in 2021, and 6 of 10 (60%) focused on machine learning models to augment CRRT delivery. All innovations were in the design/early validation phase of development. Primary research interests focused on early indicators of CRRT need, prognostication of mortality and kidney recovery, and identification of risk factors for mortality. Secondary research priorities included dynamic CRRT monitoring, predicting CRRT-related complications, and automated data pooling for point-of-care analysis. Literature gaps included prospective validation and implementation, biases ascertainment, and evaluation of AI-generated health care disparities. Research on AI applications to enhance CRRT delivery has grown exponentially in the last years, but the field remains premature. There is a need to evaluate how these applications could enhance bedside decision-making capacity and assist structure and processes of CRRT delivery.

The use of artificial intelligence (AI) in nephrology and its associated clinical research is growing. Recent years have seen increased interest in utilizing AI to predict the development of hospital-based acute kidney injury (AKI). Several AI techniques have been employed to improve the ability to detect AKI across a variety of hospitalized settings. This review discusses the evolutions of AKI risk prediction discussing the static risk assessment models of yesteryear as well as the more recent trend toward AI and advanced learning techniques. We discuss the relative improvement in AKI detection as well as the relative dearth of data around the clinical implementation and patient outcomes using these models. The use of AI for AKI detection and clinical care is in its infancy, and this review describes how we arrived at our current position and hints at the promise of the future.

Machine learning is the field of artificial intelligence in which computers are trained to make predictions or to identify patterns in data through complex mathematical algorithms. It has great potential in critical care to predict outcomes, such as acute kidney injury, and can be used for prognosis and to suggest management strategies. Machine learning can also be used as a research tool to advance our clinical and biochemical understanding of acute kidney injury. In this review, we introduce basic concepts in machine learning and review recent research in each of these domains.