Suppadungsuk, Supawadee

Background/Objectives: The study aimed to investigate whether malnutrition influences the obesity paradox and to explore the relationship between obesity with/without malnutrition and in-hospital outcomes among hospitalized ESKD patients. Methods: The study used the National Inpatient Sample database from 2016 to 2021. Hospitalized ESKD patients were included and categorized into three groups: non-obese, obese without malnutrition, and obese with malnutrition. The association between obesity with/without malnutrition and in-hospital outcomes, compared to non-obese patients, were analyzed. Results: Of 674,367 hospitalized ESKD patients included, 125,978 (18.7%) had obesity. Obese ESKD patients without malnutrition were associated with a decreased risk of mortality (odd ratio [OR] 0.87, 95% CI 0.84–0.91), whereas obese patients with malnutrition were associated with an increased risk of mortality (OR 2.08, 95% CI 1.90–2.27), compared to non-obese patients. Furthermore, obesity, with or without malnutrition, was linked to higher infection-related complications and resource utilization, especially when malnutrition was present. Conclusions: Our findings show that obesity is significantly associated with lower in-hospital mortality among ESKD patients without malnutrition. However, when malnutrition coexists, this survival advantage is reversed, underscoring the importance of detecting malnutrition in obese ESKD patients.

Background/Objectives: Malnutrition is a prevalent yet under-recognized condition in patients with advanced chronic kidney disease (CKD), contributing to increased morbidity, mortality, and healthcare burden. The aim of this study is to determine the prevalence and trends of malnutrition and investigate the impact of malnutrition on in-hospital outcomes, treatments, and resource utilization in hospitalized patients with advanced CKD. Methods: This study utilized the National Inpatient Sample (NIS) database to identify hospitalized patients with advanced CKD from 2016 to 2021. This study investigated temporal trends in the prevalence and in-hospital mortality across different degrees of malnutrition in advanced CKD patients. Multivariable regression models were used to assess the association between malnutrition and in-hospital outcomes. Results: Out of 1,244,415 advanced CKD patients, 67,587 (5.4%) had mild to moderate malnutrition, and 63,785 (5.1%) had severe malnutrition. Malnourished patients exhibited significantly higher in-hospital mortality, with adjusted odds ratios of 1.70 (95% confidence interval (CI), 1.64–1.75) for mild to moderate cases and 2.67 (95% CI, 2.60–2.75) for severe cases. Severely malnourished patients were associated with longer mean hospital stay by 7.0 days and higher hospitalization costs by $97,767 compared with non-malnourished patients. The prevalence of severe malnutrition showed a significant uptrend from 4.2% in 2016 to 5.5% in 2021 (p for trend < 0.001). Conclusions: Malnutrition in advanced CKD is an increasingly prevalent condition linked to worsened in-hospital outcomes and heightened healthcare resource utilization. The rising trend of severe malnutrition underscores the need for early nutritional screening and the need for future interventional studies to mitigate adverse clinical outcomes in this high-risk population.

Background: Sepsis-associated acute kidney injury (AKI) is linked to increased mortality and prolonged hospital stays. The exact relationship between central venous pressure (CVP) and AKI remains unclear. We explored the correlation between CVP and AKI in septic shock patients. Methods: This retrospective study included adult patients with septic shock admitted to Mayo Clinic Rochester between 2006 and 2018. CVP levels were measured at 6, 12, 24, and 48 h after the diagnosis of sepsis, and patients were stratified into two groups based on CVP levels (CVP < 8 or ≥8 mmHg). Results: Of 5600 patients with septic shock, 3128 patients without AKI on admission are included. One-thousand-and-ninety-eight patients (35.1%) developed AKI within a median of 4.4 days. The median CVP levels and frequency of elevated CVP at 6, 12, 24, and 48 h are significantly higher in the AKI group. Elevated CVP (≥8 mmHg) at 6, 12, 24, and 48 h is associated with AKI incidence, even after adjusting for mean arterial pressure (MAP) levels. This association, after multivariable adjustments, only remains significant at 12 h with an odds ratio (OR) of 1.60 (95% CI, 1.26–2.05), p < 0.001 and 48 h with an OR of 1.60 (95% CI, 1.29–1.99), p < 0.001. Conclusions: Our findings indicate that CVP ≥ 8 mmHg is strongly associated with an increased risk of AKI, even after adjusting for MAP at the 12 and 48 h time points. These findings underscore a critical 12 or 48h window for interventions to lower CVP.

Background/Objectives: With the growing population of kidney transplant recipients (KTRs) in intensive care units (ICUs), understanding their prognostic outcomes is critical. As conflicting findings exist, we aim to systematically evaluate and meta-analyze ICU outcomes in kidney transplant recipients compared to non-recipients. Methods: We conducted a comprehensive search of the PubMed, Embase, and Cochrane databases, from inception through 23 December 2024, to identify relevant studies comparing the outcomes of KTRs and non-transplant ICU patients. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for dichotomous outcomes, and weighted mean differences (WMDs) were calculated for continuous outcomes. The risk of bias was assessed using the ROBINS-I V2 tool. The study protocol was registered in the International Prospective Register of Systematic Reviews (CRD42024595104). Results: Seven studies, including 12,062 patients, were analyzed. Demographics, including age and sex, were comparable across groups. No statistically significant associations were found for overall mortality (OR: 1.82, 95% CI: 0.79 to 4.16), ICU mortality (OR: 1.06, 95% CI: 0.45 to 2.48), or 28/30-day mortality (OR: 2.06, 95% CI: 0.30 to 14.10) in KTRs, though there was a trend suggesting a potential increase in the odds of overall mortality. KTRs tended to have longer ICU stays (WMD: +1.96 days, 95% CI: 0.81–3.11) and higher Sequential Organ Failure Assessment (SOFA) scores (WMD: +0.79, 95% CI: −0.78–2.36), but these findings did not reach statistical significance. One study reported higher 1-year and 5-year mortality for KTRs. Sensitivity analyses revealed one influential study. Begg’s test for overall mortality suggested non-significant publication bias (p = 1.0). Conclusions: KTRs in ICUs are at significantly higher risk for long-term mortality, emphasizing the need for tailored critical care strategies and long-term management. Future research should focus on standardizing methodologies, reducing heterogeneity, and addressing gaps in data to improve evidence-based care for this vulnerable population.

ABSTRACTBackgroundDespite many people awaiting kidney transplant, kidney allografts from acute kidney injury (AKI) donors continue to be underutilized. We aimed to cluster kidney transplant recipients of AKI kidney allografts using an unsupervised machine learning (ML) approach.MethodsUsing Organ Procurement and Transplantation Network–United Network for Organ Sharing (OPTN/UNOS) data, a consensus clustering cohort analysis was performed in 12 356 deceased donor kidney transplant recipients between 2015 and 2019 in whom donors had a terminal serum creatinine ≥1.5 mg/dL. Significant cluster characteristics were determined, and outcomes were compared.ResultsThe median donor terminal creatinine was 2.2 (interquartile range [IQR] 1.7–3.3) mg/dL. Cluster analysis was performed on 12 356 AKI kidney recipients, and three clinically distinct clusters were identified. Young, sensitized kidney re‐transplant patients characterized Cluster 1. Cluster 2 was characterized by first‐time kidney transplant patients with hypertensive and diabetic kidney diseases. Older diabetic recipients characterized Cluster 3. Clusters 1 and 2 donors were young and met standard kidney donor profile index (KDPI) criteria; Cluster 3 donors were older, more likely to have hypertension or diabetes, and meet high KDPI criteria. Cluster 1 had a higher risk of acute rejection, 3‐year patient death, and graft failure. Cluster 3 had a higher risk of death‐censored graft failure, patient death, and graft failure at 1 and 3 years. Cluster 2 had the best patient‐, graft‐, and death‐censored graft survival at 1 and 3 years. Compared to non‐AKI kidney recipients, the AKI clusters showed a higher incidence of delayed graft function (DGF, AKI: 43.2%, 41.7%, 45.3% vs. non‐AKI: 25.5%); however, there were comparable long‐term outcomes specific to death‐censored graft survival (AKI: 93.6%, 93.4%, 90.4% vs. non‐AKI: 92.3%), patient survival (AKI: 89.1%, 93.2%, 84.2% vs. non‐AKI: 91.2%), and overall graft survival (AKI: 84.7%, 88.2%, 79.0% vs. non‐AKI: 86.0%).ConclusionsIn this unsupervised ML approach study, AKI recipient clusters demonstrated differing, but good clinical outcomes, suggesting opportunities for transplant centers to incrementally increase kidney utilization from AKI donors.

Background: Despite the prevalence and incidence of kidney stones progressively increasing worldwide, public awareness of this condition remains unclear. Understanding trends of awareness can assist healthcare professionals and policymakers in planning and implementing targeted health interventions. This study investigated online search interest in “kidney stone” by analyzing Google Trends, focusing on stationarity of the trends and predicting future trends. Methods: We performed time series analysis on worldwide Google monthly search data from January 2004 to November 2023. The Augmented Dickey–Fuller (ADF) test was used to assess the stationarity of the data, with a p-value below 0.05 indicating stationarity. Time series forecasting was performed using the autoregressive integrated moving average to predict future trends. Results: The highest search interest for “kidney stone” (score 100) was in August 2022, while the lowest was in December 2007 (score 36). As of November 2023, search interest remained high, at 92. The ADF test was significant (p = 0.023), confirming data stationarity. The time series forecasting projected continued high public interest, likely reflecting ongoing concern and awareness. Notably, diverse regions such as Iran, the Philippines, Ecuador, the United States, and Nepal showed significant interest, suggesting widespread awareness of nephrolithiasis. Conclusion: This study highlighted that “kidney stone” is a consistently relevant health issue globally. The increase and stationarity of search trends, the forecasted sustained interest, and diverse regional interest emphasize the need for collaborative research and educational initiatives. This study’s analysis serves as a valuable tool for shaping future healthcare policies and research directions in addressing nephrolithiasis related health challenges.

The integration of large language models (LLMs) into healthcare, particularly in nephrology, represents a significant advancement in applying advanced technology to patient care, medical research, and education. These advanced models have progressed from simple text processors to tools capable of deep language understanding, offering innovative ways to handle health-related data, thus improving medical practice efficiency and effectiveness. A significant challenge in medical applications of LLMs is their imperfect accuracy and/or tendency to produce hallucinations—outputs that are factually incorrect or irrelevant. This issue is particularly critical in healthcare, where precision is essential, as inaccuracies can undermine the reliability of these models in crucial decision-making processes. To overcome these challenges, various strategies have been developed. One such strategy is prompt engineering, like the chain-of-thought approach, which directs LLMs towards more accurate responses by breaking down the problem into intermediate steps or reasoning sequences. Another one is the retrieval-augmented generation (RAG) strategy, which helps address hallucinations by integrating external data, enhancing output accuracy and relevance. Hence, RAG is favored for tasks requiring up-to-date, comprehensive information, such as in clinical decision making or educational applications. In this article, we showcase the creation of a specialized ChatGPT model integrated with a RAG system, tailored to align with the KDIGO 2023 guidelines for chronic kidney disease. This example demonstrates its potential in providing specialized, accurate medical advice, marking a step towards more reliable and efficient nephrology practices.

The rapid advancement of artificial intelligence (AI) technologies, particularly machine learning, has brought substantial progress to the field of nephrology, enabling significant improvements in the management of kidney diseases. ChatGPT, a revolutionary language model developed by OpenAI, is a versatile AI model designed to engage in meaningful and informative conversations. Its applications in healthcare have been notable, with demonstrated proficiency in various medical knowledge assessments. However, ChatGPT’s performance varies across different medical subfields, posing challenges in nephrology-related queries. At present, comprehensive reviews regarding ChatGPT’s potential applications in nephrology remain lacking despite the surge of interest in its role in various domains. This article seeks to fill this gap by presenting an overview of the integration of ChatGPT in nephrology. It discusses the potential benefits of ChatGPT in nephrology, encompassing dataset management, diagnostics, treatment planning, and patient communication and education, as well as medical research and education. It also explores ethical and legal concerns regarding the utilization of AI in medical practice. The continuous development of AI models like ChatGPT holds promise for the healthcare realm but also underscores the necessity of thorough evaluation and validation before implementing AI in real-world medical scenarios. This review serves as a valuable resource for nephrologists and healthcare professionals interested in fully utilizing the potential of AI in innovating personalized nephrology care.

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.