BACKGROUND: Cirrhosis and end-stage kidney disease (ESKD) are significant global health concerns, contributing to high mortality and morbidity. Haemodialysis (HD) is frequently used to treat ESKD in patients with cirrhosis. However, it often presents challenges such as haemodynamic instability during dialysis sessions, leading to less than optimal outcomes. Peritoneal dialysis (PD), while less commonly used in cirrhotic patients, raises concerns about the risks of peritonitis and mortality. Our systematic review and meta-analysis aimed to assess outcomes in PD patients with cirrhosis. METHODS: We executed a comprehensive search in Ovid MEDLINE, EMBASE and Cochrane databases up to 25 September 2023. The search focused on identifying studies examining mortality and other clinical outcomes in ESKD patients with cirrhosis receiving PD or HD. In addition, we sought studies comparing PD outcomes in cirrhosis patients to those without cirrhosis. Data from each study were aggregated using a random-effects model and the inverse-variance method. RESULTS: Our meta-analysis included a total of 13 studies with 15,089 patients. Seven studies compared ESKD patients on PD with liver cirrhosis (2753 patients) against non-cirrhosis patients (9579 patients). The other six studies provided data on PD (824 patients) versus HD (1943 patients) in patients with cirrhosis and ESKD. The analysis revealed no significant difference in mortality between PD and HD in ESKD patients with cirrhosis (pooled odds ratio (OR) of 0.77; 95% confidence interval (CI), 0.53-1.14). In PD patients with cirrhosis, the pooled OR for peritonitis compared to non-cirrhosis patients was 1.10 (95% CI: 1.03-1.18). The pooled ORs for hernia and chronic hypotension in cirrhosis patients compared to non-cirrhosis controls were 2.48 (95% CI: 0.08-73.04) and 17.50 (95% CI: 1.90-161.11), respectively. The pooled OR for transitioning from PD to HD among cirrhotic patients was 1.71 (95% CI: 0.76-3.85). Mortality in cirrhosis patients on PD was comparable to non-cirrhosis controls, with a pooled OR of 1.05 (95% CI: 0.53-2.10). CONCLUSIONS: Our meta-analysis demonstrates that PD provides comparable mortality outcomes to HD in ESKD patients with cirrhosis. In addition, the presence of cirrhosis does not significantly elevate the risk of mortality among patients undergoing PD. While there is a higher incidence of chronic hypotension and a slightly increased risk of peritonitis in cirrhosis patients on PD compared to those without cirrhosis, the risks of hernia and the need to transition from PD to HD are comparable between both groups. These findings suggest PD as a viable and effective treatment option for ESKD patients with cirrhosis.
INTRODUCTION: Acute Kidney Injury (AKI) and Continuous Renal Replacement Therapy (CRRT) are critical areas in nephrology. The effectiveness of ChatGPT in simpler, patient education-oriented questions has not been thoroughly assessed. This study evaluates the proficiency of ChatGPT 4.0 in responding to such questions, subjected to various linguistic alterations. METHODS: Eighty-nine questions were sourced from the Mayo Clinic Handbook for educating patients on AKI and CRRT. These questions were categorized as original, paraphrased with different interrogative adverbs, paraphrased resulting in incomplete sentences, and paraphrased containing misspelled words. Two nephrologists verified the questions for medical accuracy. A chi-squared test was conducted to ascertain notable discrepancies in ChatGPT 4.0's performance across these formats. RESULTS: ChatGPT provided notable accuracy in handling a variety of question formats for patient education in AKI and CRRT. Across all question types, ChatGPT demonstrated an accuracy of 97% for both original and adverb-altered questions and 98% for questions with incomplete sentences or misspellings. Specifically for AKI-related questions, the accuracy was consistently maintained at 97% for all versions. In the subset of CRRT-related questions, the tool achieved a 96% accuracy for original and adverb-altered questions, and this increased to 98% for questions with incomplete sentences or misspellings. The statistical analysis revealed no significant difference in performance across these varied question types (p-value: 1.00 for AKI and 1.00 for CRRT), and there was no notable disparity between the AI's responses to AKI and CRRT questions (p-value: 0.71). CONCLUSION: ChatGPT 4.0 demonstrates consistent and high accuracy in interpreting and responding to queries related to AKI and CRRT, irrespective of linguistic modifications. These findings suggest that ChatGPT 4.0 has the potential to be a reliable support tool in the delivery of patient education, by accurately providing information across a range of question formats. Further research is needed to explore the direct impact of AI-generated responses on patient understanding and education outcomes.
The accurate interpretation of CRRT machine alarms is crucial in the intensive care setting. ChatGPT, with its advanced natural language processing capabilities, has emerged as a tool that is evolving and advancing in its ability to assist with healthcare information. This study is designed to evaluate the accuracy of the ChatGPT-3.5 and ChatGPT-4 models in addressing queries related to CRRT alarm troubleshooting. This study consisted of two rounds of ChatGPT-3.5 and ChatGPT-4 responses to address 50 CRRT machine alarm questions that were carefully selected by two nephrologists in intensive care. Accuracy was determined by comparing the model responses to predetermined answer keys provided by critical care nephrologists, and consistency was determined by comparing outcomes across the two rounds. The accuracy rate of ChatGPT-3.5 was 86% and 84%, while the accuracy rate of ChatGPT-4 was 90% and 94% in the first and second rounds, respectively. The agreement between the first and second rounds of ChatGPT-3.5 was 84% with a Kappa statistic of 0.78, while the agreement of ChatGPT-4 was 92% with a Kappa statistic of 0.88. Although ChatGPT-4 tended to provide more accurate and consistent responses than ChatGPT-3.5, there was no statistically significant difference between the accuracy and agreement rate between ChatGPT-3.5 and -4. ChatGPT-4 had higher accuracy and consistency but did not achieve statistical significance. While these findings are encouraging, there is still potential for further development to achieve even greater reliability. This advancement is essential for ensuring the highest-quality patient care and safety standards in managing CRRT machine-related issues.
Accurate information regarding oxalate levels in foods is essential for managing patients with hyperoxaluria, oxalate nephropathy, or those susceptible to calcium oxalate stones. This study aimed to assess the reliability of chatbots in categorizing foods based on their oxalate content. We assessed the accuracy of ChatGPT-3.5, ChatGPT-4, Bard AI, and Bing Chat to classify dietary oxalate content per serving into low (<5 mg), moderate (5-8 mg), and high (>8 mg) oxalate content categories. A total of 539 food items were processed through each chatbot. The accuracy was compared between chatbots and stratified by dietary oxalate content categories. Bard AI had the highest accuracy of 84%, followed by Bing (60%), GPT-4 (52%), and GPT-3.5 (49%) (p < 0.001). There was a significant pairwise difference between chatbots, except between GPT-4 and GPT-3.5 (p = 0.30). The accuracy of all the chatbots decreased with a higher degree of dietary oxalate content categories but Bard remained having the highest accuracy, regardless of dietary oxalate content categories. There was considerable variation in the accuracy of AI chatbots for classifying dietary oxalate content. Bard AI consistently showed the highest accuracy, followed by Bing Chat, GPT-4, and GPT-3.5. These results underline the potential of AI in dietary management for at-risk patient groups and the need for enhancements in chatbot algorithms for clinical accuracy.
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.
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.
Patient education has been transformed using digital media and online repositories which disseminate information with greater efficiency. In dermatology, this transformation has allowed for patients to gain education on common cutaneous conditions and improve health literacy. Xanthelasma palpebrarum is one of the most common cutaneous conditions, yet there is a poor understanding of how digital materials affect health literacy on this condition. Our study aimed to address this paucity of literature utilizing Brief DISCERN, Rothwell's Classification of Questions, and six readability calculations. The findings of this study indicate a poor-quality profile (Brief DISCERN < 16) regarding digital materials and readability scores which do not meet grade-level recommendations in the United States. This indicates a need to improve the current body of educational materials used by clinicians for diagnosing and managing xanthelasma palpebrarum.
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.
BACKGROUND AND OBJECTIVES: Literature reviews are foundational to understanding medical evidence. With AI tools like ChatGPT, Bing Chat and Bard AI emerging as potential aids in this domain, this study aimed to individually assess their citation accuracy within Nephrology, comparing their performance in providing precise. MATERIALS AND METHODS: We generated the prompt to solicit 20 references in Vancouver style in each 12 Nephrology topics, using ChatGPT, Bing Chat and Bard. We verified the existence and accuracy of the provided references using PubMed, Google Scholar, and Web of Science. We categorized the validity of the references from the AI chatbot into (1) incomplete, (2) fabricated, (3) inaccurate, and (4) accurate. RESULTS: A total of 199 (83%), 158 (66%) and 112 (47%) unique references were provided from ChatGPT, Bing Chat and Bard, respectively. ChatGPT provided 76 (38%) accurate, 82 (41%) inaccurate, 32 (16%) fabricated and 9 (5%) incomplete references. Bing Chat provided 47 (30%) accurate, 77 (49%) inaccurate, 21 (13%) fabricated and 13 (8%) incomplete references. In contrast, Bard provided 3 (3%) accurate, 26 (23%) inaccurate, 71 (63%) fabricated and 12 (11%) incomplete references. The most common error type across platforms was incorrect DOIs. CONCLUSIONS: In the field of medicine, the necessity for faultless adherence to research integrity is highlighted, asserting that even small errors cannot be tolerated. The outcomes of this investigation draw attention to inconsistent citation accuracy across the different AI tools evaluated. Despite some promising results, the discrepancies identified call for a cautious and rigorous vetting of AI-sourced references in medicine. Such chatbots, before becoming standard tools, need substantial refinements to assure unwavering precision in their outputs.
Graduate medical education in radiology serves an imperative role in training the next generation of specialists. Given the regularity of virtual interviews, the website of a fellowship programs remains a critical first-line source of information of applicants. The aim of this study is to systematically evaluate 7 radiology fellowship programs utilizing a systematic process. A cross-sectional descriptive 286 graduate medical education fellowship programs in radiology were screened from the Fellowship and Residency Electronic Interactive Database (FREIDA). Extracted data was evaluated for comprehensiveness using 20 content criteria, and a readability score is calculated. The mean comprehensiveness among all fellowship program websites was 55.8% (n = 286), and the average FRE among the program overview sections was 11.9 (n = 214). ANOVA revealed no statistical significance in program website comprehensiveness between radiology fellowships (P = 0.33). The quality of a program's website data continues to serve an important role in an applicant's decision-making. Fellowship programs have improved in their content availability overtime, but content reevaluation needs to be continued for tangible improvement.
Internship and Residency , Radiology , Humans , Cross-Sectional Studies , Fellowships and Scholarships , Education, Medical, Graduate , Radiology/education , Internet
BACKGROUND: Better understanding of the different phenotypes/subgroups of non-U.S. citizen kidney transplant recipients may help the transplant community to identify strategies that improve outcomes among non-U.S. citizen kidney transplant recipients. This study aimed to cluster non-U.S. citizen kidney transplant recipients using an unsupervised machine learning approach; Methods: We conducted a consensus cluster analysis based on recipient-, donor-, and transplant- related characteristics in non-U.S. citizen kidney transplant recipients in the United States from 2010 to 2019 in the OPTN/UNOS database using recipient, donor, and transplant-related characteristics. Each cluster's key characteristics were identified using the standardized mean difference. Post-transplant outcomes were compared among the clusters; Results: Consensus cluster analysis was performed in 11,300 non-U.S. citizen kidney transplant recipients and identified two distinct clusters best representing clinical characteristics. Cluster 1 patients were notable for young age, preemptive kidney transplant or dialysis duration of less than 1 year, working income, private insurance, non-hypertensive donors, and Hispanic living donors with a low number of HLA mismatch. In contrast, cluster 2 patients were characterized by non-ECD deceased donors with KDPI <85%. Consequently, cluster 1 patients had reduced cold ischemia time, lower proportion of machine-perfused kidneys, and lower incidence of delayed graft function after kidney transplant. Cluster 2 had higher 5-year death-censored graft failure (5.2% vs. 9.8%; p < 0.001), patient death (3.4% vs. 11.4%; p < 0.001), but similar one-year acute rejection (4.7% vs. 4.9%; p = 0.63), compared to cluster 1; Conclusions: Machine learning clustering approach successfully identified two clusters among non-U.S. citizen kidney transplant recipients with distinct phenotypes that were associated with different outcomes, including allograft loss and patient survival. These findings underscore the need for individualized care for non-U.S. citizen kidney transplant recipients.
BACKGROUND: Serum chloride derangement is common in critically ill patients requiring continuous renal replacement therapy (CRRT). We aimed to assess the association between serum chloride levels before and during CRRT with mortality. METHODS: This is a retrospective cohort study of critically ill patients receiving CRRT for acute kidney injury from December 2006 through November 2015 in a tertiary referral hospital in the United States. We used logistic regression to assess serum chloride before and mean serum chloride during CRRT as predictors for 90 days mortality after CRRT initiation. The normal reference range for serum chloride was 99-108 mmol/L. RESULTS: Of 1282 eligible patients, 25%, 50%, and 25% had hypochloremia, normochloremia, and hyperchloremia, respectively. The adjusted odds ratio for 90 days mortality in patients with hypochloremia before CRRT was 1.82 (95% CI 1.29-2.55). During CRRT, 4%, 70%, 26% of patients had mean serum chloride in the hypochloremia, normochloremia, and hyperchloremia range, respectively. The adjusted odds ratio for 90 days mortality in patients with mean serum chloride during CRRT in the hypochloremia range was 2.96 (95% CI 1.43-6.12). Hyperchloremia before and during CRRT was not associated with mortality. The greater serum chloride range during CRRT was associated with increased mortality (OR 1.29; 95% CI 1.13-1.47 per 5 mmol/L increase). CONCLUSION: Hypochloremia before and during CRRT is associated with higher mortality.
Acute Kidney Injury , Continuous Renal Replacement Therapy , Water-Electrolyte Imbalance , Humans , Retrospective Studies , Chlorides , Critical Illness/therapy , Logistic Models , Acute Kidney Injury/therapy , Renal Replacement Therapy
The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI's capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an "AI scrutiny" phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI's role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI's involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI's capabilities while upholding stringent academic standards can be envisioned.
BACKGROUND: We aimed to develop and validate an automated machine learning (autoML) prediction model for cardiac surgery-associated acute kidney injury (CSA-AKI). METHODS: Using 69 preoperative variables, we developed several models to predict post-operative AKI in adult patients undergoing cardiac surgery. Models included autoML and non-autoML types, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN), as well as a logistic regression prediction model. We then compared model performance using area under the receiver operating characteristic curve (AUROC) and assessed model calibration using Brier score on the independent testing dataset. RESULTS: The incidence of CSA-AKI was 36%. Stacked ensemble autoML had the highest predictive performance among autoML models, and was chosen for comparison with other non-autoML and multivariable logistic regression models. The autoML had the highest AUROC (0.79), followed by RF (0.78), XGBoost (0.77), multivariable logistic regression (0.77), ANN (0.75), and DT (0.64). The autoML had comparable AUROC with RF and outperformed the other models. The autoML was well-calibrated. The Brier score for autoML, RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.18, 0.18, 0.21, 0.19, 0.19, and 0.18, respectively. We applied SHAP and LIME algorithms to our autoML prediction model to extract an explanation of the variables that drive patient-specific predictions of CSA-AKI. CONCLUSION: We were able to present a preoperative autoML prediction model for CSA-AKI that provided high predictive performance that was comparable to RF and superior to other ML and multivariable logistic regression models. The novel approaches of the proposed explainable preoperative autoML prediction model for CSA-AKI may guide clinicians in advancing individualized medicine plans for patients under cardiac surgery.
Background: There is limited evidence on the association of serum phosphate with mortality in patients receiving continuous renal replacement therapy (CRRT). Objective: To assess the association of serum phosphate with mortality in critically ill patients requiring CRRT for acute kidney injury (AKI). Design: A cohort study. Setting: A tertiary referral hospital in the United States. Patients: Acute kidney injury patients receiving CRRT from 2006 through 2015 in intensive care units. Measurements: (1) Serum phosphate before CRRT and (2) mean serum phosphate during CRRT were categorized into 3 groups; ≤2.4 (hypophosphatemia), 2.5 to 4.5 (normal serum phosphate group), and ≥4.6 (hyperphosphatemia) mg/dL. Methods: Multivariable logistic regression was used to assess the association between serum phosphate and 90-day mortality. Results: A total of 1108 patients were included in this study. Of these, 55% died within 90 days after CRRT initiation. Before CRRT, 3%, 30%, and 66% had hypophosphatemia, normophosphatemia, and hyperphosphatemia, respectively. Before CRRT, both hypophosphatemia and hyperphosphatemia were significantly associated with higher 90-day mortality with the adjusted odds ratio (OR) of 2.22 (95% confidence interval [CI]: [1.03, 4.78]) and 1.62 (95% CI: [1.21, 2.18]), respectively. During CRRT, 3%, 85%, and 12% had mean serum phosphate in hypophosphatemia, normophosphatemia, and hyperphosphatemia range. During CRRT, hyperphosphatemia was significantly associated with higher 90-day mortality with adjusted OR of 2.22 (95% CI: [1.45, 3.38]). Limitations: Single center, observational design, lack of information regarding causes of serum phosphate derangement. Conclusion: Most CRRT patients had hyperphosphatemia before CRRT initiation but maintain normal serum phosphate during CRRT. Before CRRT, hypo- and hyperphosphatemia, and during CRRT, hyperphosphatemia predicted higher mortality. Trial registration: Not registered.
Contexte: Il existe peu de données sur l'association entre la phosphatémie et la mortalité chez les patients recevant une thérapie de remplacement rénal continue (TRRC). Objectif: Examiner l'association entre la phosphatémie et la mortalité chez les patients gravement malades nécessitant une TRRC pour suppléer une insuffisance rénale aiguë (IRA). Type d'étude: Étude de cohorte. Cadre: Un hôpital central de soins tertiaires aux États-Unis. Patients: Des patients atteints d'IRA ayant reçu une TRRC entre 2006 et 2015 dans les unités de soins intensifs. Mesures: 1) la phosphatémie avant la TRRC et 2) la phosphatémie moyenne pendant la TRRC ont été classées en trois groupes: hypophosphatémie (≤ 2,4 mg/dl), normophosphatémie (2,5 à 4,5 mg/dl) et hyperphosphatémie (≥ 4,6 mg/dl). Méthodologie: La régression logistique multivariable a été utilisée pour évaluer l'association entre la phosphatémie et la mortalité à 90 jours. Résultats: L'étude a inclus un total de 1 108 patients dont 55 % sont décédés dans les 90 jours suivant le début de la TRRC. Avant d'amorcer la TRRC, 3 % des patients présentaient une hypophosphatémie, 30 % une normophosphatémie et 66 % une hyperphosphatémie. Avant l'amorce de la TRRC, avec leur rapport de cotes ajusté de 2,22 (IC 95 %: 1,03-4,78) et 1,62 (IC 95 %: 1,21-2,18) respectivement, l'hypophosphatémie et l'hyperphosphatémie étaient significativement associées à une mortalité plus élevée à 90 jours. Pendant la TRRC, 3 % des patients présentaient un taux de phosphate sérique moyen dans les gammes d'hypophosphatémie; ces proportions étaient de 85 % pour la normophosphatémie et de 12 % pour l'hyperphosphatémie. Cette dernière était également significativement associée à un taux de mortalité plus élevé à 90 jours, avec un taux ajusté de 2,22 (IC à 95 %: 1,45-3,38), pendant la TRRC. Limites: Étude dans un seul center, conception observationnelle, manque d'information sur les causes du dérèglement de la phosphatémie. Conclusion: La plupart des patients présentaient une hyperphosphatémie avant l'initiation de la TRRC, mais ont maintenu des valeurs normales pendant la TRRC. L'hypophosphatémie et l'hyperphosphatémie avant l'amorce de la TRRC, ainsi que l'hyperphosphatémie pendant la TRRC, se sont avérés des facteurs prédictifs d'un taux de mortalité plus élevé. Enregistrement de l'essai: Non enregistré.
Background: This study aimed to better characterize morbidly obese kidney transplant recipients, their clinical characteristics, and outcomes by using an unsupervised machine learning approach. Methods: Consensus cluster analysis was applied to OPTN/UNOS data from 2010 to 2019 based on recipient, donor, and transplant characteristics in kidney transplant recipients with a pre-transplant BMI ≥ 40 kg/m2. Key cluster characteristics were identified using the standardized mean difference. Post-transplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection, were compared among the clusters. Results: Consensus clustering analysis identified 3204 kidney transplant recipients with a BMI ≥ 40 kg/m2. In this cohort, five clinically distinct clusters were identified. Cluster 1 recipients were predominantly white and non-sensitized, had a short dialysis time or were preemptive, and were more likely to receive living donor kidney transplants. Cluster 2 recipients were older and diabetic. They were likely to have been on dialysis >3 years and receive a standard KDPI deceased donor kidney. Cluster 3 recipients were young, black, and had kidney disease secondary to hypertension or glomerular disease. Cluster 3 recipients had >3 years of dialysis and received non-ECD, young, deceased donor kidney transplants with a KDPI < 85%. Cluster 4 recipients were diabetic with variable dialysis duration who either received non-ECD standard KDPI kidneys or living donor kidney transplants. Cluster 5 recipients were young retransplants that were sensitized. One-year patient survival in clusters 1, 2, 3, 4, and 5 was 98.0%, 94.4%, 98.5%, 98.7%, and 97%, and one-year death-censored graft survival was 98.1%, 93.0%, 96.1%, 98.8%, and 93.0%, respectively. Cluster 2 had the worst one-year patient survival. Clusters 2 and 5 had the worst one-year death-censored graft survival. Conclusions: With the application of unsupervised machine learning, variable post-transplant outcomes are observed among morbidly obese kidney transplant recipients. Recipients with earlier access to transplant and living donation show superior outcomes. Unexpectedly, reduced graft survival in cluster 3 recipients perhaps underscores socioeconomic access to post-transplant support and minorities being disadvantaged in access to preemptive and living donor transplants. Despite obesity-related concerns, one-year patient and graft survival were favorable in all clusters, and obesity itself should be reconsidered as a hard barrier to kidney transplantation.
Background: There have been concerns regarding increased perioperative mortality, length of hospital stay, and rates of graft loss in kidney transplant recipients with functional limitations. The application of machine learning consensus clustering approach may provide a novel understanding of unique phenotypes of functionally limited kidney transplant recipients with distinct outcomes in order to identify strategies to improve outcomes. Methods: Consensus cluster analysis was performed based on recipient-, donor-, and transplant-related characteristics in 3205 functionally limited kidney transplant recipients (Karnofsky Performance Scale (KPS) < 40% at transplant) in the OPTN/UNOS database from 2010 to 2019. Each cluster's key characteristics were identified using the standardized mean difference. Posttransplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection were compared among the clusters Results: Consensus cluster analysis identified two distinct clusters that best represented the clinical characteristics of kidney transplant recipients with limited functional status prior to transplant. Cluster 1 patients were older in age and were more likely to receive deceased donor kidney transplant with a higher number of HLA mismatches. In contrast, cluster 2 patients were younger, had shorter dialysis duration, were more likely to be retransplants, and were more likely to receive living donor kidney transplants from HLA mismatched donors. As such, cluster 2 recipients had a higher PRA, less cold ischemia time, and lower proportion of machine-perfused kidneys. Despite having a low KPS, 5-year patient survival was 79.1 and 83.9% for clusters 1 and 2; 5-year death-censored graft survival was 86.9 and 91.9%. Cluster 1 had lower death-censored graft survival and patient survival but higher acute rejection, compared to cluster 2. Conclusion: Our study used an unsupervised machine learning approach to characterize kidney transplant recipients with limited functional status into two clinically distinct clusters with differing posttransplant outcomes.
INTRODUCTION: We aimed to assess the association between serum potassium and mortality in patients receiving continuous renal replacement therapy (CRRT). METHODS: We studied 1279 acute kidney injury patients receiving CRRT in a tertiary referral hospital in the United States. We used logistic regression to assess the association of serum potassium before CRRT and mean serum potassium during CRRT with 90-day mortality after CRRT initiation, using serum potassium 4.0-4.4 mmol/L as reference group. RESULTS: Before CRRT, there was a U-shaped association between serum potassium and 90-day mortality. There was a significant increase in mortality when serum potassium before CRRT was ≤3.4 and ≥4.5 mmol/L. During CRRT, progressively increased mortality was noted when mean serum potassium was ≥4.5 mmol/L. The odds ratio of 90-day mortality was significantly higher when mean serum potassium was ≥4.5 mmol/L. CONCLUSION: Hypokalemia and hyperkalemia before CRRT and hyperkalemia during CRRT predicts 90-day mortality.
Acute Kidney Injury , Continuous Renal Replacement Therapy , Hyperkalemia , Humans , Hyperkalemia/epidemiology , Potassium , Acute Kidney Injury/therapy , Retrospective Studies , Renal Replacement Therapy
