Kashani, Kianoush B

The exponential growth of artificial intelligence (AI) has allowed for its integration into multiple sectors, including, notably, healthcare. Chatbots have emerged as a pivotal resource for improving patient outcomes and assisting healthcare practitioners through various AI-based technologies. In critical care, kidney-related conditions play a significant role in determining patient outcomes. This article examines the potential for integrating chatbots into the workflows of critical care nephrology to optimize patient care. We detail their specific applications in critical care nephrology, such as managing acute kidney injury, alert systems, and continuous renal replacement therapy (CRRT); facilitating discussions around palliative care; and bolstering collaboration within a multidisciplinary team. Chatbots have the potential to augment real-time data availability, evaluate renal health, identify potential risk factors, build predictive models, and monitor patient progress. Moreover, they provide a platform for enhancing communication and education for both patients and healthcare providers, paving the way for enriched knowledge and honed professional skills. However, it is vital to recognize the inherent challenges and limitations when using chatbots in this domain. Here, we provide an in-depth exploration of the concerns tied to chatbots’ accuracy, dependability, data protection and security, transparency, potential algorithmic biases, and ethical implications in critical care nephrology. While human discernment and intervention are indispensable, especially in complex medical scenarios or intricate situations, the sustained advancements in AI signal that the integration of precision-engineered chatbot algorithms within critical care nephrology has considerable potential to elevate patient care and pivotal outcome metrics in the future.

Literature reviews are valuable for summarizing and evaluating the available evidence in various medical fields, including nephrology. However, identifying and exploring the potential sources requires focus and time devoted to literature searching for clinicians and researchers. ChatGPT is a novel artificial intelligence (AI) large language model (LLM) renowned for its exceptional ability to generate human-like responses across various tasks. However, whether ChatGPT can effectively assist medical professionals in identifying relevant literature is unclear. Therefore, this study aimed to assess the effectiveness of ChatGPT in identifying references to literature reviews in nephrology. We keyed the prompt “Please provide the references in Vancouver style and their links in recent literature on… name of the topic” into ChatGPT-3.5 (03/23 Version). We selected all the results provided by ChatGPT and assessed them for existence, relevance, and author/link correctness. We recorded each resource’s citations, authors, title, journal name, publication year, digital object identifier (DOI), and link. The relevance and correctness of each resource were verified by searching on Google Scholar. Of the total 610 references in the nephrology literature, only 378 (62%) of the references provided by ChatGPT existed, while 31% were fabricated, and 7% of citations were incomplete references. Notably, only 122 (20%) of references were authentic. Additionally, 256 (68%) of the links in the references were found to be incorrect, and the DOI was inaccurate in 206 (54%) of the references. Moreover, among those with a link provided, the link was correct in only 20% of cases, and 3% of the references were irrelevant. Notably, an analysis of specific topics in electrolyte, hemodialysis, and kidney stones found that >60% of the references were inaccurate or misleading, with less reliable authorship and links provided by ChatGPT. Based on our findings, the use of ChatGPT as a sole resource for identifying references to literature reviews in nephrology is not recommended. Future studies could explore ways to improve AI language models’ performance in identifying relevant nephrology literature.

Background The impact of changes in Doppler‐derived kidney venous flow in heart failure (HF) is not well studied. We aimed to investigate the association of Doppler‐derived kidney venous stasis index (KVSI) and intrakidney venous‐flow (IKVF) patterns with adverse cardiorenal outcomes in patients with HF. Methods and Results In this observational cohort study, consecutive inpatients with HF referred to a nephrologist because of a history of diuretic resistance and abnormal kidney function (n=216) underwent spectral kidney assessments after admission (Doppler 1) and 25 to 35 days later (Doppler 2) to identify IKVF patterns (continuous/pulsatile/biphasic/monophasic) and KVSI levels. Cox proportional hazard regression models were used to evaluate the associations between KVSI/IKVF patterns at Doppler 1 as well as changes from Doppler 1 to Doppler 2 and risk of cardiorenal events up to 18 months after admission. Worsening HF or death occurred in 126 patients. Both baseline KVSI (hazard ratio [HR], 1.49 [95% CI, 1.37–1.61] per 0.1‐unit increase) and baseline IKVF pattern (HR, 2.47 [95% CI, 2.01–3.04] per 1 pattern severity increase) were significantly associated with worsening HF/death. Increases in both KVSI and IKVF pattern severity from Doppler 1 to 2 were also associated with an increased risk of worsening HF/death (HR, 3.00 [95% CI, 2.08–4.32] per 0.1‐unit increase change; and HR, 6.73 [95% CI, 3.27–13.86] per 1 pattern increase in severity change, respectively). Similar results were observed for kidney outcomes. Conclusions Baseline kidney venous flow predicted adverse cardiorenal events, and inclusion of serial kidney venous flow in cardiorenal risk stratification could facilitate clinical decision‐making for patients with HF. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03039959.

ABSTRACT The Food and Drug Administration defines digital health (DH) as “a convergence of people, information, technology, and connectivity to improve health care and health outcomes”. Information and communication technologies are categorized under the DH umbrella. While still in their infancy, these technologies have great potential to revolutionize the clinical approaches to bedside management and overall health care. Over the past few decades, there has been a substantial improvement in the technology in smart wearable devices that enabled them to enhance their abilities to collect patient-related data, including physiological and behavioral information in the Internet of things. This has resulted in implementing several controlled clinical trials that have confirmed their validity and safety. These digital diagnostics have led to the expansion of digital therapies and specialized software (for example, apps) capable of effectively and measurably guiding patients’ progress toward improving their clinical condition. These technologies are clinically validated treatments for many diseases with particular effectiveness in pathological conditions related to behavior and psychological factors. In this review paper, we describe the outlines of some of the recent progress made in this field.

ABSTRACT The Food and Drug Administration defines digital health (DH) as “a convergence of people, information, technology, and connectivity to improve health care and health outcomes”. Information and communication technologies are categorized under the DH umbrella. While still in their infancy, these technologies have great potential to revolutionize the clinical approaches to bedside management and overall health care. Over the past few decades, there has been a substantial improvement in the technology in smart wearable devices that enabled them to enhance their abilities to collect patient-related data, including physiological and behavioral information in the Internet of things. This has resulted in implementing several controlled clinical trials that have confirmed their validity and safety. These digital diagnostics have led to the expansion of digital therapies and specialized software (for example, apps) capable of effectively and measurably guiding patients’ progress toward improving their clinical condition. These technologies are clinically validated treatments for many diseases with particular effectiveness in pathological conditions related to behavior and psychological factors. In this review paper, we describe the outlines of some of the recent progress made in this field.

Key Points Urinary TIMP2×IGFBP7 (uTIMP2×IGFBP7) concentrations experienced a rapid and sustained decline after high-dose methotrexate (MTX) exposure. uTIMP2×IGFBP7 kinetics and concentrations after high-dose MTX exposure demonstrated no utility in predicting AKI.

Background Sepsis is associated with an elevated risk of late cardiovascular events among hospital survivors. Methods and Results We included OptumLabs Data Warehouse patients from 2009 to 2019 who survived a medical/nonsurgical hospitalization lasting at least 2 nights. The association between sepsis during hospitalization, based on explicit and implicit discharge International Classification of Diseases, Ninth Revision ( ICD‐9 )/ Tenth Revision ( ICD‐10 ) diagnosis codes, with subsequent death and rehospitalization was analyzed using Kaplan–Meier survival analysis and multivariable Cox proportional‐hazards models. The study population included 2 258 464 survivors of nonsurgical hospitalization (5 396 051 total patient‐years of follow‐up). A total of 808 673 (35.8%) patients had a sepsis hospitalization, including implicit sepsis only in 448 644, explicit sepsis only in 124 841, and both in 235 188. Patients with sepsis during hospitalization had an elevated risk of all‐cause mortality (adjusted hazard ratio [HR], 1.27 [95% CI, 1.25–1.28]; P <0.001), all‐cause rehospitalization (adjusted HR, 1.38 [95% CI, 1.37–1.39]; P <0.001), and cardiovascular hospitalization (adjusted HR, 1.43 [95% CI, 1.41–1.44]; P <0.001), especially heart failure hospitalization (adjusted HR, 1.51 [95% CI, 1.49–1.53]). Patients with implicit sepsis had higher risk than those with explicit sepsis. A sensitivity analysis using the first hospitalization yielded concordant results for cardiovascular hospitalization (adjusted HR, 1.78 [95% CI, 1.76–1.78]; P <0.001), as did a propensity‐weighted analysis (adjusted HR, 1.52 [95% CI, 1.50–1.54]; P <0.001). Conclusions Survivors of sepsis hospitalization are at elevated risk of early and late post‐discharge death as well as cardiovascular and non‐cardiovascular rehospitalization. This hazard spans the spectrum of cardiovascular events and may suggest that sepsis is an important cardiovascular risk factor.

Abstract Objective Investigate the association of echocardiographic parameters with hemodynamic instability after initiating continuous kidney replacement therapy (CKRT) in a cohort of intensive care unit (ICU) patients requiring CKRT. Methods Historical cohort study of consecutive adults admitted to the ICU at a tertiary care hospital from December 2006 through November 2015 who underwent CKRT and had an echocardiogram done within seven days before CKRT initiation. The primary outcome was hypotension within one hour of CKRT initiation. Results We included 980 patients, 804 (82%) with acute kidney injury (AKI) and 176 (18%) with end-stage kidney disease (ESKD). Median patient age was 63 (± 14) years, and median Sequential Organ Failure Assessment (SOFA) score on the day of CKRT initiation was 12 (IQR 10–14). Multivariable analysis showed that Left (OR 2.01, 95% CI 1.04–3.86), and Right (OR 1.5, 95% CI 1.04–2.25) moderate and severe ventricular enlargement, Vasoactive-Inotropic Score (VIS) one hour before CKRT initiation (OR 1.18 per 10 units increase, 95% CI 1.09–1.28) and high bicarbonate fluid replacement (OR 2.52, 95% CI 1.01–6.2) were associated with hypotension after CKRT initiation. Conclusion Right and left ventricular enlargement are risk factors associated with hypotension after CKRT initiation.

One of the primary reasons for intensive care admission is shock. Identifying the underlying cause of shock (hypovolemic, distributive, cardiogenic, and obstructive) may lead to entirely different clinical pathways for management. Among patients with hypovolemic and distributive shock, fluid therapy is one of the leading management strategies. Although an appropriate amount of fluid administration might save a patient's life, inadequate (or excessive) fluid use could lead to more complications, including organ failure and mortality due to either hypovolemia or volume overload. Currently, intensivists have access to a wide variety of information sources and tools to monitor the underlying hemodynamic status, including medical history, physical examination, and specific hemodynamic monitoring devices. Although appropriate and timely assessment and interpretation of this information can promote adequate fluid resuscitation, misinterpretation of these data can also lead to additional mortality and morbidity. This article provides a narrative review of the most commonly used hemodynamic monitoring approaches to assessing fluid responsiveness and fluid tolerance. In addition, we describe the benefits and disadvantages of these tools.