Tangpanithandee, Supawit

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/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.

ABSTRACT Background Evidence supporting glucagon-like peptide-1 receptor agonists (GLP-1RAs) in kidney transplant recipients (KTRs) remains scarce. This systematic review and meta-analysis aims to evaluate the safety and efficacy of GLP-1RAs in this population. Methods A comprehensive literature search was conducted in the MEDLINE, Embase and Cochrane databases from inception through May 2023. Clinical trials and observational studies that reported on the safety or efficacy outcomes of GLP-1RAs in adult KTRs were included. Kidney graft function, glycaemic and metabolic parameters, weight, cardiovascular outcomes and adverse events were evaluated. Outcome measures used for analysis included pooled odds ratios (ORs) with 95% confidence intervals (CIs) for dichotomous outcomes and standardized mean difference (SMD) or mean difference (MD) with 95% CI for continuous outcomes. The protocol was registered in the International Prospective Register of Systematic Reviews (CRD 42023426190). Results Nine cohort studies with a total of 338 KTRs were included. The median follow-up was 12 months (interquartile range 6–23). While treatment with GLP-1RAs did not yield a significant change in estimated glomerular filtration rate [SMD −0.07 ml/min/1.73 m2 (95% CI −0.64–0.50)] or creatinine [SMD −0.08 mg/dl (95% CI −0.44–0.28)], they were associated with a significant decrease in urine protein:creatinine ratio [SMD −0.47 (95% CI −0.77 to −0.18)] and haemoglobin A1c levels [MD −0.85% (95% CI −1.41 to −0.28)]. Total daily insulin dose, weight and body mass index also decreased significantly. Tacrolimus levels remained stable [MD −0.43 ng/ml (95% CI −0.99 to 0.13)]. Side effects were primarily nausea and vomiting (17.6%), diarrhoea (7.6%) and injection site pain (5.4%). Conclusions GLP-1RAs are effective in reducing proteinuria, improving glycaemic control and supporting weight loss in KTRs, without altering tacrolimus levels. Gastrointestinal symptoms are the main side effects.

Background and Objectives: Limited evidence exists regarding the safety and efficacy of glucagon-like peptide-1 receptor agonists (GLP-1RAs) in type 2 diabetes mellitus (T2DM) patients with advanced chronic kidney disease (CKD) or end-stage kidney disease (ESKD). Thus, we conducted a systematic review and meta-analysis to assess the safety and efficacy of GLP-1RAs in T2DM patients with advanced CKD and ESKD. Materials and Methods: We performed a systematic literature search in MEDLINE, EMBASE, and Cochrane database until 25 October 2023. Included were clinical trials and cohort studies reporting outcomes of GLP-1RAs in adult patients with T2DM and advanced CKD. Outcome measures encompassed mortality, cardiovascular parameters, blood glucose, and weight. Safety was assessed for adverse events. The differences in effects were expressed as odds ratios with 95% confidence intervals (CIs) for dichotomous outcomes and the weighted mean difference or standardized mean difference (SMD) with 95% confidence intervals for continuous outcomes. The Risk of Bias In Non-randomized Studies—of Interventions (ROBIN-I) tool was used in cohort and non-randomized controlled studies, and the Cochrane Risk of Bias (RoB 2) tool was used in randomized controlled trials (RCTs). The review protocol was registered in the International Prospective Register of Systematic Reviews (CRD 42023398452) and received no external funding. Results: Eight studies (five trials and three cohort studies) consisting of 27,639 patients were included in this meta-analysis. No difference was observed in one-year mortality. However, GLP-1RAs significantly reduced cardiothoracic ratio (SMD of −1.2%; 95% CI −2.0, −0.4) and pro-BNP (SMD −335.9 pmol/L; 95% CI −438.9, −232.8). There was no significant decrease in systolic blood pressure. Moreover, GLP-1RAs significantly reduced mean blood glucose (SMD −1.1 mg/dL; 95% CI −1.8, −0.3) and increased weight loss (SMD −2.2 kg; 95% CI −2.9, −1.5). In terms of safety, GLP-1RAs were associated with a 3.8- and 35.7-time higher risk of nausea and vomiting, respectively, but were not significantly associated with a higher risk of hypoglycemia. Conclusions: Despite the limited number of studies in each analysis, our study provides evidence supporting the safety and efficacy of GLP-1RAs among T2DM patients with advanced CKD and ESKD. While gastrointestinal side effects may occur, GLP-1RAs demonstrate significant improvements in blood glucose control, weight reduction, and potential benefit in cardiovascular outcomes.

AbstractBackgroundWe aimed to cluster deceased donor kidney transplant recipients with prolonged cold ischemia time (CIT) using an unsupervised machine learning approach.MethodsWe performed consensus cluster analysis on 11 615 deceased donor kidney transplant patients with CIT exceeding 24 h using OPTN/UNOS data from 2015 to 2019. Cluster characteristics of clinical significance were identified, and post‐transplant outcomes were compared.ResultsConsensus cluster analysis identified two clinically distinct clusters. Cluster 1 was characterized by young, non‐diabetic patients who received kidney transplants from young, non‐hypertensive, non‐ECD deceased donors with lower KDPI scores. In contrast, the patients in cluster 2 were older and more likely to have diabetes. Cluster 2 recipients were more likely to receive transplants from older donors with a higher KDPI. There was lower use of machine perfusion in Cluster 1 and incrementally longer CIT in Cluster 2. Cluster 2 had a higher incidence of delayed graft function (42% vs. 29%), and lower 1‐year patient (95% vs. 98%) and death‐censored (95% vs. 97%) graft survival compared to Cluster 1.ConclusionsUnsupervised machine learning characterized deceased donor kidney transplant recipients with prolonged CIT into two clusters with differing outcomes. Although Cluster 1 had more favorable recipient and donor characteristics and better survival, the outcomes observed in Cluster 2 were also satisfactory. Overall, both clusters demonstrated good survival suggesting opportunities for transplant centers to incrementally increase CIT.

Patients with chronic kidney disease (CKD) necessitate specialized renal diets to prevent complications such as hyperkalemia and hyperphosphatemia. A comprehensive assessment of food components is pivotal, yet burdensome for healthcare providers. With evolving artificial intelligence (AI) technology, models such as ChatGPT, Bard AI, and Bing Chat can be instrumental in educating patients and assisting professionals. To gauge the efficacy of different AI models in discerning potassium and phosphorus content in foods, four AI models—ChatGPT 3.5, ChatGPT 4, Bard AI, and Bing Chat—were evaluated. A total of 240 food items, curated from the Mayo Clinic Renal Diet Handbook for CKD patients, were input into each model. These items were characterized by their potassium (149 items) and phosphorus (91 items) content. Each model was tasked to categorize the items into high or low potassium and high phosphorus content. The results were juxtaposed with the Mayo Clinic Renal Diet Handbook’s recommendations. The concordance between repeated sessions was also evaluated to assess model consistency. Among the models tested, ChatGPT 4 displayed superior performance in identifying potassium content, correctly classifying 81% of the foods. It accurately discerned 60% of low potassium and 99% of high potassium foods. In comparison, ChatGPT 3.5 exhibited a 66% accuracy rate. Bard AI and Bing Chat models had an accuracy rate of 79% and 81%, respectively. Regarding phosphorus content, Bard AI stood out with a flawless 100% accuracy rate. ChatGPT 3.5 and Bing Chat recognized 85% and 89% of the high phosphorus foods correctly, while ChatGPT 4 registered a 77% accuracy rate. Emerging AI models manifest a diverse range of accuracy in discerning potassium and phosphorus content in foods suitable for CKD patients. ChatGPT 4, in particular, showed a marked improvement over its predecessor, especially in detecting potassium content. The Bard AI model exhibited exceptional precision for phosphorus identification. This study underscores the potential of AI models as efficient tools in renal dietary planning, though refinements are warranted for optimal utility.

This comprehensive critical review critically examines the ethical implications associated with integrating chatbots into nephrology, aiming to identify concerns, propose policies, and offer potential solutions. Acknowledging the transformative potential of chatbots in healthcare, responsible implementation guided by ethical considerations is of the utmost importance. The review underscores the significance of establishing robust guidelines for data collection, storage, and sharing to safeguard privacy and ensure data security. Future research should prioritize defining appropriate levels of data access, exploring anonymization techniques, and implementing encryption methods. Transparent data usage practices and obtaining informed consent are fundamental ethical considerations. Effective security measures, including encryption technologies and secure data transmission protocols, are indispensable for maintaining the confidentiality and integrity of patient data. To address potential biases and discrimination, the review suggests regular algorithm reviews, diversity strategies, and ongoing monitoring. Enhancing the clarity of chatbot capabilities, developing user-friendly interfaces, and establishing explicit consent procedures are essential for informed consent. Striking a balance between automation and human intervention is vital to preserve the doctor–patient relationship. Cultural sensitivity and multilingual support should be considered through chatbot training. To ensure ethical chatbot utilization in nephrology, it is imperative to prioritize the development of comprehensive ethical frameworks encompassing data handling, security, bias mitigation, informed consent, and collaboration. Continuous research and innovation in this field are crucial for maximizing the potential of chatbot technology and ultimately improving patient outcomes.