Citations

IMPORTANCE: The outcomes of critically ill adults with obesity on continuous renal replacement therapy (CRRT) are poorly characterized. The impact of CRRT dose on these outcomes is uncertain. OBJECTIVES: This study aimed to determine if obesity conferred a survival advantage for critically ill adults with acute kidney injury (AKI) on CRRT. Secondarily, we evaluated whether the dose of CRRT predicted mortality in this population. DESIGN, SETTING, AND PARTICIPANTS: A retrospective, observational cohort study performed at an academic medical center in Minnesota. The study population included critically ill adults with AKI managed with CRRT. MAIN OUTCOMES AND MEASURES: The primary outcome of 30-day mortality was compared between obese (body mass index [BMI] ≥ 30 kg/m2) and nonobese (BMI < 30 kg/m2) patients. Multivariable regression assessed was used to assess CRRT dose as a predictor of outcomes. An analysis included dose indexed according to actual body weight (ABW), adjusted body weight (AdjBW), or ideal body weight (IBW). RESULTS: Among 1033 included patients, the median (interquartile range) BMI was 26 kg/m2 (23–28 kg/m2) in the nonobese group and 36 kg/m2 (32–41 kg/m2) in the obese group. Mortality was similar between groups at 30 days (54% vs. 48%; p = 0.06) but lower in the obese group at 90 days (62% vs. 55%; p = 0.02). CRRT dose predicted an increase in mortality when indexed according to ABW or AdjBW (hazard ratio [HR], 1.2–1.16) but not IBW (HR, 1.04). CONCLUSIONS AND RELEVANCE: In critically ill adults with AKI requiring CRRT, short-term mortality appeared lower in obese patients compared with nonobese patients. Among weight calculations, IBW appears to be preferred to promote safe CRRT dosing in obese patients.

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.

Purpose of review Acute kidney injury (AKI) is a highly prevalent clinical syndrome that substantially impacts patient outcomes. It is accepted by the clinical communities that the management of AKI is time-sensitive. Unfortunately, despite growing proof of its preventability, AKI management remains suboptimal in community, acute care, and postacute care settings. Digital health solutions comprise various tools and models to improve care processes and patient outcomes in multiple medical fields. AKI development, progression, recovery, or lack thereof, offers tremendous opportunities for developing, validating, and implementing digital health solutions in multiple settings. This article will review the definitions and components of digital health, the characteristics of AKI that allow digital health solutions to be considered, and the opportunities and threats in implementing these solutions. Recent findings Over the past two decades, the academic output related to the use of digital health solutions in AKI has exponentially grown. While this indicates the growing interest in the topic, most topics are primarily related to clinical decision support by detecting AKI within hospitals or using artificial intelligence or machine learning technologies to predict AKI within acute care settings. However, recently, projects to assess the impact of digital health solutions in more complex scenarios, for example, managing nephrotoxins among adults of pediatric patients who already have AKI, is increasing. Depending on the type of patients, chosen digital health solution intervention, comparator groups, and selected outcomes, some of these studies showed benefits, while some did not indicate additional gain in care processes or clinical outcomes. Summary Careful needs assessment, selection of the correct digital health solution, and appropriate clinical validation of the benefits while avoiding additional health disparities are moral, professional, and ethical obligations for all individuals using these healthcare tools, including clinicians, data scientists, and administrators.

ABSTRACT Background: Septic shock is the leading cause of acute kidney injury (AKI) in critically ill patients. The foundation of early septic shock management includes early fluid resuscitation, but the association between fluid resuscitation rates and kidney outcomes remains unclear. This investigation examines the association between fluid resuscitation rate and AKI recovery. Methods: In the medical intensive care unit of Mayo Clinic Rochester, adult patients with AKI and septic shock were retrospectively studied from January 1, 2006 to May 31, 2018. The surviving sepsis campaign recommends an initial fluid bolus of 30 ml/kg for sepsis resuscitation. The cohort of patients was divided into three groups based on the average fluid resuscitation time (<1 h, 1.1–3 h, >3 h) and the corresponding fluid rate ≥0.5, 0.17–0.49, and <0.17 ml/kg/min, respectively. The primary outcome was the recovery of AKI on day 7. To account for potential confounders, multivariable regression analyses were conducted. Results: After meeting the eligibility, 597 patients were included in the analysis. The AKI recovery was considerably different among the groups ( P = 0.006). Patients in group 1 who received fluid resuscitation faster had a higher rate of AKI recovery (53%) compared to group 2 and group 3 (50% and 37.8%). Conclusion: In septic shock patients with AKI, a higher fluid resuscitation rate of 30 ml/kg IV fluids within the 1 st -h sepsis diagnosis (i.e., >0.50 ml/kg/min) lead to higher AKI recovery compared with slower infusion rates.

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.

Background: Urine output (UO) measurement utility after out-of-hospital cardiac arrest (OHCA) survivors who undergo targeted temperature management (TTM) is not well understood. Methods: We included 247 comatose adult patients who underwent TTM after OHCA between 2007 and 2017 and excluded those on maintenance dialysis. Groups were defined based on mean hourly UO during the first 24 hours: Group 1 (<0.5 ml/kg/hr, n=73), Group 2 (0.5-1 ml/kg/hr, n=81) and Group 3 (>1 ml/kg/hr, n=93). Kidney Disease: Improving Global Outcomes (KDIGO) serum creatinine criteria were used to define and stage acute kidney injury (AKI). Multivariable analysis was used to evaluate in-hospital mortality. Results: In-hospital mortality decreased as 24-hour mean UO increased (56.2% [41/73] vs 29.6% [24/81] vs 18.3% [17/93] in Groups 1, 2 and 3, respectively; adjusted OR 0.93 per 0.1 ml/kg/h higher, 95% CI 0.87-0.99, p = 0.03), and UO < 0.5 ml/kg/hr was strongly associated with in-hospital mortality (adjusted OR 4.2 [1.6-10.8], p=0.003). In-hospital mortality varied by the KDIGO stage and UO group (Figure), lowest in KDIGO stage 0 and highest in KDIGO stage 3. Both UO (adjusted OR 0.92 per 0.1 ml/kg/h higher, p = 0.002) and KDIGO stage (adjusted OR 1.62 per each stage, p = 0.002) were associated with in-hospital mortality when included in the same model. Among patients with KDIGO stage 0, lower UO portended higher mortality (Figure). Likewise, among patients with low UO (group 1), a higher KDIGO stage predicted higher mortality. Conclusion: Amongst OHCA survivors undergoing TTM, higher UO is incrementally associated with lower in-hospital mortality. The lowest mortality was observed at UO >=1 ml/kg/h, suggesting this as a target for resuscitation in OHCA patients undergoing TTM. Low UO remains associated with in-hospital mortality even when accounting for AKI by creatinine criteria, suggesting that oliguria is a more sensitive prognostic marker in the early post-OHCA phase.

Abstract Background and Aims At least 30% of acute kidney injury (AKI) survivors lack appropriate follow up after hospital discharge. AKI survivors have highly dynamic posthospital course which warrants close monitoring to prevent adverse outcomes. Digital health solutions like remote patient monitoring (RPM) could be used to improve quality and efficiency of AKI survivor care. The purpose for this study was to assess the feasibility and effectiveness of the Mayo Clinic AKI RPM program, launched in October 2021. Method The Mayo Clinic AKI RPM program enrolled individuals who experienced AKI during a hospitalization and underwent nephrology consultation (Figure 1). Feasibility was assessed as the proportion of individuals approached for AKI RPM enrollment during the first year of the program who submitted at least one set of vital signs after discharge. An effectiveness analysis compared stage 3 AKI survivors enrolled in AKI RPM with at least 30-days of follow-up to matched historical controls (3:1) sampled from before RPM was available (2018-2021). The primary endpoint was hospital readmission or emergency department (ED) visit within 30-days, assessed with the Chi-square test. Secondarily we explored time to first readmission or ED visit with a Kaplan-Meier survival curve with a non-parametric comparison between groups, as well as readmission length of stay with the Wilcoxon Rank Sum test due to right skewed data distribution. Results Of the 50 individuals approached for AKI RPM participation, 45 (90%) submitted at least one set of vitals. Among AKI RPM patients, 34 patients with stage 3 AKI were matched to 102 controls based on baseline characteristics and demographics. Dialysis during hospitalization (liberated by discharge) was used in 36 (27%) of patients. Sixty (44%) individuals required ICU level of care. Median (IQR) discharge estimated glomerular filtration rate was 15 (11, 27) mL/min/1.73m2. Through matching, groups were well balanced with respect to pertinent baseline demographics. Hospital readmission or ED visit occurred in 17 (50%) of AKI RPM patients within 30-days compared to 39 (38%) of controls within 30-days (P = .23). The endpoint appeared driven by ED visits within 30-days, not readmissions [At least one ED visit: 13 (38%) vs 21 (21%), respectively (P = .04); At least one hospital readmission: 7 (21%) vs 26 (26%), respectively (P = .56)]. Time to first readmission or ED visit within 30-days was similar between groups (P = .35; Figure 2). Among the 33 patients who were readmitted to the hospital within 30-days, readmission length of stay was similar in the AKI RPM group compared to controls [Median (IQR) 76 (10.6, 121) hours vs 108 (70, 165) hours); P = 0.33]. Conclusion In conclusion, AKI RPM was a feasible program when used to bridge the care continuum (hospital to home) in non-dialysis dependent AKI survivors. Incidence of at least one hospital readmission or ED visit within 30-days was statistically similar between AKI RPM patients and controls. More AKI RPM patients experienced ED encounters in the 30-days after discharge, but frequency of hospital readmission was similar. Digital health solutions such as RPM offer a unique opportunity to address the important gap in AKI care after discharging from the hospital. Additional research is needed to explore the impact of AKI RPM on patient outcomes.

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.