Kashani, Kianoush

Abstract Background Vasoactive medications and Mechanical Circulatory Support (MCS) are both central to the contemporary management of cardiogenic shock (CS). There are limited data on the interaction between vasoactive medication dosing and MCS in patients with CS. Purpose We investigated the association between in-hospital mortality and vasoactive medication dosing among patients with CS who did and did not receive MCS during hospitalization. Methods We included patients with CS admitted to our cardiac intensive care unit (CICU) between 2007 and 2015. The peak combined vasoactive medication dose during the first 24 hours of CICU admission was recorded and converted to norepinephrine equivalents (NEE)(references 1 & 2). The primary outcome was in-hospital mortality. Multivariable logistic regression was used to adjust for relevant variables and calculate adjusted odds ratios (OR). Results Of 1078 patients with CS (median age 69.0 years [Interquartile Range 59.1-77.3], 36.8% female, 59% with acute myocardial infarction), 721 (66.9%) received vasoactive medications in the first 24 hours and 510 (47.3%) received MCS (87% intra-aortic balloon pump). Among those receiving vasoactive medications during the first 24 hours, the median NEE was 0.16 (Interquartile Range 0.06-0.40) mcg/kg/min. In hospital mortality was 34.4% overall, and was lower among those who received MCS (26.1% versus 41.9%, p<0.001; adjusted OR 0.71 95% CI 0.50-0.99) and higher among those who received vasoactive medications (38.1% versus 26.9%, p<0.001). A gradient of mortality was observed according to the use of vasoactive medications and MCS (Fig. 1). We found a strong incremental association between in-hospital mortality and NEE (adjusted OR 1.11 per 0.1 mcg/kg/min, 95% CI 1.06–1.16, P<0.001; Fig. 2). Mortality was lower across the spectrum of NEE for patients who received MCS (Fig. 2). There was no statistically significant difference in the slope of the relationship between in-hospital mortality and NEE in patients who did and did not receive MCS (p = 0.86). Conclusion Increasing vasoactive medication requirement during the first 24 hours of CICU admission is incrementally associated with in-hospital mortality in CS, representing a simplified marker of shock severity. Patients who received MCS were at lower risk of mortality across the spectrum of vasopressor requirements, emphasizing the potential synergy between these two complementary approaches to hemodynamic support.Figure 1Figure 2

Background One‐time assessment of the Society for Cardiovascular Angiography and Interventions (SCAI) shock classification robustly predicts mortality in the cardiac intensive care unit (CICU). We sought to determine whether serial SCAI shock classification could improve risk stratification. Methods and Results Unique admissions to a single academic level 1 CICU from 2015 to 2018 were included in this retrospective cohort study. Electronic health record data were used to assign the SCAI shock stage during 4‐hour blocks of the first 24 hours of CICU admission. Shock was defined as hypoperfusion (SCAI shock stage C, D, or E). In‐hospital death was evaluated using logistic regression. Among 2918 unique CICU patients, 1537 (52.7%) met criteria for shock during ≥1 block, and 266 (9.1%) died in the hospital. The SCAI shock stage on admission was: A, 37.6%; B, 31.5%; C, 25.9%; D, 1.8%; and E, 3.3%. Patients who met SCAI criteria for shock on admission (first 4 hours) and those with worsening SCAI shock stage after admission were at higher risk for in‐hospital death. Each higher admission (adjusted odds ratio, 1.36 [95% CI, 1.18–1.56]; area under the receiver operating characteristic curve, 0.70), maximum (adjusted odds ratio, 1.59 [95% CI, 1.37–1.85]; area under the receiver operating characteristic curve, 0.73) and mean (adjusted odds ratio, 2.42 [95% CI, 1.99–2.95]; area under the receiver operating characteristic curve, 0.78) SCAI shock stage was incrementally associated with a higher in‐hospital mortality rate. Discrimination was highest for the mean SCAI shock stage ( P <0.05). Each additional 4‐hour block meeting SCAI criteria for shock predicted a higher mortality rate (adjusted odds ratio, 1.15 [95% CI, 1.07–1.24]). Conclusions Dynamic assessment of shock using serial SCAI shock classification assignment can improve mortality risk stratification in CICU patients by quantifying the magnitude and duration of shock.

AbstractStudy ObjectiveTo develop and validate a model for predicting acute kidney injury (AKI) after high‐dose methotrexate (HDMTX) exposure.DesignRetrospective analysis.SettingMultisite integrated health system throughout Minnesota and Wisconsin.PatientsAdult patients with lymphoma who received HDMTX as a 4‐h infusion.Measurements and Main ResultsLASSO methodology was used to identify factors available at the outset of therapy that predicted incident AKI within 7 days following HDMTX. The model was then validated in an independent cohort. The incidence of AKI within 7 days following HDMTX was 21.6% (95% confidence interval (CI) 18.4%‐24.8%) in the derivation cohort (435 unique patients who received a total of 1642 doses of HDMTX) and 15.6% (95% CI 5.3%‐24.8%) in the validation cohort (55 unique patients who received a total of 247 doses of HDMTX). Factors significantly associated with AKI after HDMTX in the multivariable model included age ≥ 55 years, male sex, and lower HDMTX dose number. Other factors that were not found to be significantly associated with AKI on multivariable analysis, but were included in the final model, were body surface area, Charlson Comorbidity Index, and estimated glomerular filtration rate. The c‐statistic of the model was 0.72 (95% CI 0.69‐0.75) in the derivation cohort and 0.72 (95% CI 0.60‐0.84) in the validation cohort.ConclusionThis model utilizing identified sociodemographic and clinical factors is predictive of AKI following HDMTX administration in adult patients with lymphoma.

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.