The impact of induction therapy on the risk of posttransplant lymphoproliferative disorder in adult kidney transplant recipients with donor-recipient serological Epstein-Barr virus mismatch.

Posttransplant lymphoproliferative disorder (PTLD) poses a significant concern in Epstein-Barr virus (EBV)-negative patients transplanted from EBV-positive donors (EBV R-/D+). Previous studies investigating the association between different induction agents and PTLD in these patients have yielded conflicting results. Using the Organ Procurement and Transplant Network database, we identified EBV R-/D+ patients >18 years of age who underwent kidney-alone transplants between 2016 and 2022 and compared the risk of PTLD with rabbit antithymocyte globulin (ATG), basiliximab, and alemtuzumab inductions. Among the 6620 patients included, 64.0% received ATG, 23.4% received basiliximab, and 12.6% received alemtuzumab. The overall incidence of PTLD was 2.5% over a median follow-up period of 2.9 years. Multivariable analysis demonstrated that the risk of PTLD was significantly higher with ATG and alemtuzumab compared with basiliximab (adjusted subdistribution hazard ratio [aSHR] = 1.98, 95% confidence interval [CI] 1.29-3.04, P = .002 for ATG and aSHR = 1.80, 95% CI 1.04-3.11, P = .04 for alemtuzumab). However, PTLD risk was comparable between ATG and alemtuzumab inductions (aSHR = 1.13, 95% CI 0.72-1.77, P = .61). Therefore, the risk of PTLD must be taken into consideration when selecting the most appropriate induction therapy for this patient population.

Phenotypic subtypes of fibrotic hypersensitivity pneumonitis identified by machine learning consensus clustering analysis.

BACKGROUND: Patients with fibrotic hypersensitivity pneumonitis (f-HP) have varied clinical and radiologic presentations whose associated phenotypic outcomes have not been previously described. We conducted a study to evaluate mortality and lung transplant (LT) outcomes among clinical clusters of f-HP as characterized by an unsupervised machine learning approach. METHODS: Consensus cluster analysis was performed on a retrospective cohort of f-HP patients diagnosed according to recent international guideline. Demographics, antigen exposure, radiologic, histopathologic, and pulmonary function findings along with comorbidities were included in the cluster analysis. Cox proportional-hazards regression was used to assess mortality or LT risk as a combined outcome for each cluster. RESULTS: Three distinct clusters were identified among 336 f-HP patients. Cluster 1 (n = 158, 47%) was characterized by mild restriction on pulmonary function testing (PFT). Cluster 2 (n = 46, 14%) was characterized by younger age, lower BMI, and a higher proportion of identifiable causative antigens with baseline obstructive physiology. Cluster 3 (n = 132, 39%) was characterized by moderate to severe restriction. When compared to cluster 1, mortality or LT risk was lower in cluster 2 (hazard ratio (HR) of 0.42; 95% CI, 0.21-0.82; P = 0.01) and higher in cluster 3 (HR of 1.76; 95% CI, 1.24-2.48; P = 0.001). CONCLUSIONS: Three distinct phenotypes of f-HP with unique mortality or transplant outcomes were found using unsupervised cluster analysis, highlighting improved mortality in fibrotic patients with obstructive physiology and identifiable antigens.

Association of Proton Pump Inhibitor Use and Immune Checkpoint Inhibitor-Mediated Acute Kidney Injury: A Meta-Analysis and a Review of Related Outcomes.

INTRODUCTION: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, they pose the risk of immune-related adverse events, including ICI-mediated acute kidney injury (ICI-AKI). Recent studies have implicated proton pump inhibitors (PPIs) as potential contributors to ICI-AKI development. This meta-analysis examines the association between PPI use and ICI-AKI, exploring a potential modifiable risk factor in ICI therapy while also reviewing the possible outcomes of ICI-AKI. METHODS: We conducted a comprehensive systematic review and meta-analysis of observational studies, assessing the risk of ICI-AKI in cancer patients concurrently using PPIs and potential outcomes. Odds ratios (ORs) were pooled using random-effects models. Subgroup analyses and sensitivity analyses were performed to evaluate heterogeneity and potential biases. RESULTS: A total of 14 studies involving 12,694 patients were included. In total, we analyzed 639 patients with all-cause AKI and 779 patients with ICI-AKI. The pooled OR for the overall incidence of AKI from all-causes was 1.57 (95% confidence interval [CI] 1.02-2.40) among patients on PPIs. Specifically, the risk of ICI-AKI associated with PPI use was significantly higher, with a pooled OR of 1.84 (95% CI 1.16-2.90). This indicates approximately 84% higher likelihood of developing ICI-AKI with concurrent use of PPIs. Additionally, among patients with ICI-AKI, 67% had complete or partial recovery of renal function, 32% progressed to chronic kidney disease (CKD), and about 36% died during a follow-up period of at least 3 months. CONCLUSION: This meta-analysis highlights the importance of cautious PPI prescription in cancer patients undergoing ICI therapy. Clinicians are advised to evaluate the risks and benefits of PPI use and consider alternative therapies when feasible.

Re-assessing prolonged cold ischemia time in kidney transplantation through machine learning consensus clustering.

BACKGROUND: We aimed to cluster deceased donor kidney transplant recipients with prolonged cold ischemia time (CIT) using an unsupervised machine learning approach. METHODS: We 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. RESULTS: Consensus 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. CONCLUSIONS: Unsupervised 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.

Reexamining Transplant Outcomes in Acute Kidney Injury Kidneys Through Machine Learning.

BACKGROUND: Despite 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. METHODS: Using 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. RESULTS: The 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%). CONCLUSIONS: In 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.

Advances in critical care nephrology through artificial intelligence.

PURPOSE OF REVIEW: This review explores the transformative advancement, potential application, and impact of artificial intelligence (AI), particularly machine learning (ML) and large language models (LLMs), on critical care nephrology. RECENT FINDINGS: AI algorithms have demonstrated the ability to enhance early detection, improve risk prediction, personalize treatment strategies, and support clinical decision-making processes in acute kidney injury (AKI) management. ML models can predict AKI up to 24-48 h before changes in serum creatinine levels, and AI has the potential to identify AKI sub-phenotypes with distinct clinical characteristics and outcomes for targeted interventions. LLMs and generative AI offer opportunities for automated clinical note generation and provide valuable patient education materials, empowering patients to understand their condition and treatment options better. To fully capitalize on its potential in critical care nephrology, it is essential to confront the limitations and challenges of AI implementation, including issues of data quality, ethical considerations, and the necessity for rigorous validation. SUMMARY: The integration of AI in critical care nephrology has the potential to revolutionize the management of AKI and continuous renal replacement therapy. While AI holds immense promise for improving patient outcomes, its successful implementation requires ongoing training, education, and collaboration among nephrologists, intensivists, and AI experts.

Generative AI in Critical Care Nephrology: Applications and Future Prospects.

BACKGROUND: Generative artificial intelligence (AI) is rapidly transforming various aspects of healthcare, including critical care nephrology. Large language models (LLMs), a key technology in generative AI, show promise in enhancing patient care, streamlining workflows, and advancing research in this field. SUMMARY: This review analyzes the current applications and future prospects of generative AI in critical care nephrology. Recent studies demonstrate the capabilities of LLMs in diagnostic accuracy, clinical reasoning, and continuous renal replacement therapy (CRRT) alarm troubleshooting. As we enter an era of multiagent models and automation, the integration of generative AI into critical care nephrology holds promise for improving patient care, optimizing clinical processes, and accelerating research. However, careful consideration of ethical implications and continued refinement of these technologies are essential for their responsible implementation in clinical practice. This review explores the current and potential applications of generative AI in nephrology, focusing on clinical decision support, patient education, research, and medical education. Additionally, we examine the challenges and limitations of AI implementation, such as privacy concerns, potential bias, and the necessity for human oversight. KEY MESSAGES: (i) LLMs have shown potential in enhancing diagnostic accuracy, clinical reasoning, and CRRT alarm troubleshooting in critical care nephrology. (ii) Generative AI offers promising applications in patient education, literature review, and academic writing within the field of nephrology. (iii) The integration of AI into electronic health records and clinical workflows presents both opportunities and challenges for improving patient care and research. (iv) Addressing ethical concerns, ensuring data privacy, and maintaining human oversight are crucial for the responsible implementation of AI in critical care nephrology.

Efficacy of Therapeutic Apheresis for Cryoglobulinemic Vasculitis Patients with Renal Involvement: A Systematic Review.

INTRODUCTION: Therapeutic apheresis (TA) is commonly used for cryoglobulinemic vasculitis (CV) patients, but its efficacy remains uncertain. This systematic review aimed to assess the efficacy of different TA modalities, such as plasma exchange (PE), plasmapheresis (PP), and cryofiltration (CF), in treating CV patients with renal involvement. METHODS: Literature search of MEDLINE, EMBASE, and Cochrane Databases was conducted up to December 2022. Studies that reported the outcomes of TA in adult CV patients with renal involvement were assessed. The protocol for this systematic review has been registered with PROSPERO (No. CRD42023417727). The quality of each study was evaluated by the investigators using the validated methodological index for non-randomized studies (minors) quality score. RESULTS: 154 patients who encountered 170 episodes of serious events necessitating TA were evaluated across 76 studies. Among them, 51% were males, with a mean age ranging from 49 to 58 years. The CV types included 15 type I, 97 type II, and 13 type III, while the remaining patients exhibited mixed (n = 17) or undetermined CV types (n = 12). Among the treatment modalities, PE, PP, and CF were performed in 85 (56%), 52 (34%), and 17 patients (11%), respectively, with no identical protocol for TA treatment. The overall response rate for TA was 78%, with response rates of 84%, 77%, and 75% observed in type I, II, and III patients respectively. Most patients received steroids, immunosuppressants, and treatment targeting the underlying causative disease. The overall long-term renal outcome rate was 77%, with type I, II, and III patients experiencing response rates of 89%, 76%, and 90%, respectively. The renal outcomes in patients receiving PE, PP, and CF were comparable, with rates of 78%, 76%, and 81%, respectively. CONCLUSIONS: This study presents compelling evidence that combination of TA with other treatments, especially immunosuppressive therapy, is a successful strategy for effectively managing severe renal involvement in CV patients. Among the TA modalities studied, including PE, PP, and CF, all demonstrated efficacy, with PE being the most frequently employed approach.

Evaluating ChatGPT's Accuracy in Responding to Patient Education Questions on Acute Kidney Injury and Continuous Renal Replacement Therapy.

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 χ2 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 artificial intelligence (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.

Assessing the impact of continuous positive airway pressure therapy on clinical outcomes in interstitial lung disease patients with coexisting obstructive sleep apnea: a systematic review.

PURPOSE: Interstitial lung disease (ILD) often coexists with obstructive sleep apnea (OSA), contributing to increased morbidity and mortality. However, the effectiveness of continuous positive airway pressure (CPAP) therapy in this population remains unclear. We conducted a systematic review to evaluate CPAP therapy's impact on clinical outcomes in patients with ILD and comorbid OSA. METHODS: Following PRISMA guidelines, we systematically searched multiple databases for studies assessing CPAP therapy's effects on ILD exacerbation, hospitalization, quality of life, and mortality in ILD-OSA patients. Studies were selected based on predefined inclusion criteria, and their quality was assessed using the Newcastle-Ottawa quality scale. RESULTS: Among 485 articles screened, 82 underwent full review, with four observational studies meeting inclusion criteria. CPAP therapy demonstrated potential benefits in improving quality of life and reducing ILD exacerbations in ILD-OSA patients. However, its impact on mortality was inconclusive due to variability in study definitions and methodology. CONCLUSION: CPAP therapy may improve outcomes in ILD-OSA patients, particularly in terms of quality of life and ILD exacerbations. Nonetheless, further research with standardized definitions and rigorous methodology is needed to confirm its efficacy, particularly regarding mortality outcome.

Management of immune-mediated glomerular diseases in the elderly.

The management of immune-mediated nephropathies in the elderly presents unique challenges due to age-related physiological changes, comorbidities, and frailty. This review addresses the clinical workup, diagnostic evaluation, and treatment strategies for this rapidly growing patient population. We highlight the inadequacies of current classification systems and the lack of evidence-based guidelines tailored to individuals ≥75 years. The review discusses the specific considerations in diagnosing and treating common conditions such as minimal change disease, focal and segmental glomerulosclerosis, membranous nephropathy, ANCA-associated vasculitis, infection-related and post-infectious glomerulonephritis, and anti-GBM disease. Managing these diseases requires a nuanced approach due to age-related changes in the immune system and the presence of multiple comorbidities. Immunosuppressive therapy, including corticosteroids, rituximab, and cyclophosphamide, remains a cornerstone of treatment, but the choice and dosage of drugs must be carefully balanced to avoid severe side effects. Comorbidity management, regular monitoring of kidney function, and a patient-centered approach are crucial for improving outcomes and quality of life. A multidisciplinary team can provide comprehensive care, addressing all aspects of the patient's health. Supportive care, the role of kidney biopsy, and the balance between immunosuppressive therapy and the risk of complications are emphasized. Collaborative, individualized care approaches are recommended to improve outcomes and quality of life for elderly patients with immune-mediated kidney diseases. Future research should focus on including older patients in clinical trials to establish robust, age-specific guidelines.

Perspectives on AI-based recommendations for mask-wearing and COVID-19 vaccination for transplant recipients in the post-COVID-19 era.

In the aftermath of the COVID-19 pandemic, the ongoing necessity for preventive measures such as mask-wearing and vaccination remains particularly critical for organ transplant recipients, a group highly susceptible to infections due to immunosuppressive therapy. Given that many individuals nowadays increasingly utilize Artificial Intelligence (AI), understanding AI perspectives is important. Thus, this study utilizes AI, specifically ChatGPT 4.0, to assess its perspectives in offering precise health recommendations for mask-wearing and COVID-19 vaccination tailored to this vulnerable population. Through a series of scenarios reflecting diverse environmental settings and health statuses in December 2023, we evaluated the AI's responses to gauge its precision, adaptability, and potential biases in advising high-risk patient groups. Our findings reveal that ChatGPT 4.0 consistently recommends mask-wearing in crowded and indoor environments for transplant recipients, underscoring their elevated risk. In contrast, for settings with fewer transmission risks, such as outdoor areas where social distancing is possible, the AI suggests that mask-wearing might be less imperative. Regarding vaccination guidance, the AI strongly advocates for the COVID-19 vaccine across most scenarios for kidney transplant recipients. However, it recommends a personalized consultation with healthcare providers in cases where patients express concerns about vaccine-related side effects, demonstrating an ability to adapt recommendations based on individual health considerations. While this study provides valuable insights into the current AI perspective on these important topics, it is crucial to note that the findings do not directly reflect or influence health policy. Nevertheless, given the increasing utilization of AI in various domains, understanding AI's viewpoints on such critical matters is essential for informed decision-making and future research.

COVID-19 and renal allograft rejection: insight from controlled and non-controlled studies.

AIM: Kidney transplant recipients (KTRs), due to their immunosuppressed status, are potentially more susceptible to both the severe effects of COVID-19 and complications in their transplanted organ. The aim of this study is to investigate whether COVID-19 infection increases the risk of rejection in kidney transplant recipients (KTRs). METHODS: This study involved a detailed literature review, conducted using PubMed, with the search being completed by September 7th, 2023. The search strategy incorporated a combination of relevant keywords: 'COVID', 'Renal', 'Kidney', 'Transplant', and 'Rejection'. The results from controlled and uncontrolled studies were separately collated and analyzed. RESULTS: A total of 11 studies were identified, encompassing 1,179 patients. Among these, two controlled studies reported the incidence of rejection in KTRs infected with COVID-19. Pooling data from these studies revealed no significant statistical correlation between COVID-19 infection and biopsy-proven rejection (p = 0.26). In addition, nine non-controlled studies were found, with rejection incidences ranging from 0% to 66.7%. The majority of these studies (eight out of nine) had small sample sizes, ranging from 3 to 75 KTRs, while the largest included 372 KTRs. The combined rejection rate across these studies was calculated to be 11.8%. CONCLUSION: In conclusion, the limited number of published controlled studies revealed no statistically significant association between COVID-19 infection and biopsy-proven rejection among KTRs. However, the broader analysis of non-controlled studies showed a variable rejection incidence with a pooled rejection rate of 11.8%. There is insufficient high-quality data to explore the association of COVID-19 infection and rejection.

Identification of kidney-related medications using AI from self-captured pill images.

INTRODUCTION: ChatGPT, a state-of-the-art large language model, has shown potential in analyzing images and providing accurate information. This study aimed to explore ChatGPT-4 as a tool for identifying commonly prescribed nephrology medications across different versions and testing dates. METHODS: 25 nephrology medications were obtained from an institutional pharmacy. High-quality images of each medication were captured using an iPhone 13 Pro Max and uploaded to ChatGPT-4 with the query, 'What is this medication?' The accuracy of ChatGPT-4's responses was assessed for medication name, dosage, and imprint. The process was repeated after 2 weeks to evaluate consistency across different versions, including GPT-4, GPT-4 Legacy, and GPT-4.Ø. RESULTS: ChatGPT-4 correctly identified 22 out of 25 (88%) medications across all versions. However, it misidentified Hydrochlorothiazide, Nifedipine, and Spironolactone due to misreading imprints. For instance, Nifedipine ER 90 mg was mistaken for Metformin Hydrochloride ER 500 mg because 'NF 06' was misread as 'NF 05'. Hydrochlorothiazide 50 mg was confused with the 25 mg version due to imprint errors, and Spironolactone 25 mg was misidentified as Naproxen Sodium or Diclofenac Sodium. Despite these errors, ChatGPT-4 showed 100% consistency when retested, correcting misidentifications after receiving feedback on the correct imprints. CONCLUSION: ChatGPT-4 shows strong potential in identifying nephrology medications from self-captured images, though challenges with difficult-to-read imprints remain. Providing feedback improved accuracy, suggesting ChatGPT-4 could be a valuable tool in digital health for medication identification. Future research should enhance the model's ability to distinguish similar imprints and explore broader integration into digital health platforms.

Chain of Thought Utilization in Large Language Models and Application in Nephrology.

Chain-of-thought prompting enhances the abilities of large language models (LLMs) significantly. It not only makes these models more specific and context-aware but also impacts the wider field of artificial intelligence (AI). This approach broadens the usability of AI, increases its efficiency, and aligns it more closely with human thinking and decision-making processes. As we improve this method, it is set to become a key element in the future of AI, adding more purpose, precision, and ethical consideration to these technologies. In medicine, the chain-of-thought prompting is especially beneficial. Its capacity to handle complex information, its logical and sequential reasoning, and its suitability for ethically and context-sensitive situations make it an invaluable tool for healthcare professionals. Its role in enhancing medical care and research is expected to grow as we further develop and use this technique. Chain-of-thought prompting bridges the gap between AI's traditionally obscure decision-making process and the clear, accountable standards required in healthcare. It does this by emulating a reasoning style familiar to medical professionals, fitting well into their existing practices and ethical codes. While solving AI transparency is a complex challenge, the chain-of-thought approach is a significant step toward making AI more comprehensible and trustworthy in medicine. This review focuses on understanding the workings of LLMs, particularly how chain-of-thought prompting can be adapted for nephrology's unique requirements. It also aims to thoroughly examine the ethical aspects, clarity, and future possibilities, offering an in-depth view of the exciting convergence of these areas.

Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications.

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 efficacy and safety of cryoballoon atrial fibrillation ablation in patients with heart failure: A systematic review and meta-analysis.

INTRODUCTION: While atrial fibrillation (AF) ablation has proven beneficial for heart failure (HF) patients, most reports were performed with radiofrequency ablation. We aimed to evaluate the efficacy and safety of cryoballoon AF ablation in patients with HFrEF. METHOD: We comprehensively searched the databases of MEDLINE, EMBASE, and Cochrane database from inception to December 2022. Studies that reported the outcomes of freedom from atrial arrhythmia, complications, NYHA functional class (NYHA FC), and left ventricular ejection fraction (LVEF) after Cryoballoon AF ablation in HF patients were included. Data from each study were combined with a random-effects model. RESULT: A total of 9 studies observational studies with 1414 HF patients were included. Five studies had only HF with reduced ejection fraction (HFrEF), 1 study with HF with preserved ejection fraction (HFpEF), and others with mixed HF types. Freedom from AA in HFrEF at 12 months was 64% (95% CI 56-71%, I2 58%). There was a significant improvement of LVEF in these patients with a standard mean difference of 13% (95% CI 8.6-17.5%, I2 99% P < 0.001. The complication rate in HFrEF group was 6% (95% CI 4-10%, I2 0%). The risk of recurrence of atrial arrhythmia was not significantly different between HF and no HF patients (RR 1.34, 95% CI 0.8-2.23, I2 76%). CONCLUSION: Cryoballoon AF ablation is effective in HFrEF patients comparable to radiofrequency ablation. The complication rate was low.

Sex differences in clinical characteristics, management, and outcomes in patients admitted for ventricular tachycardia: 2016-2018.

INTRODUCTION: The concurrent data on sex disparities in VT management and outcomes have remained unclear. Therefore, our objective was to determine the impact of sex on ventricular tachycardia (VT) management and outcomes in patients admitted with VT, dervied from the US National Inpatient Sample database (NIS). METHODS: We used data from the US NIS to identify hospitalized adult patients who were admitted with VT between 2016 and 2018. Regression analysis was conducted to evaluate the impact of sex on VT management, in-hospital mortality, complications, length of stay, and hospitalization costs. RESULTS: Of the database, a total of 146 070 patients, who were primarily hospitalized for VT, were approximated. Among these, women comprised 25.5%; they were significantly younger and had fewer comorbidities. Of procedural aspects, women were less likely to receive an angiogram, mechanical support, implantable cardioverter-defibrillator implantation, and VT ablation compared to men. Notably, women were associated with higher do-not-resuscitate rates and in-hospital cardiac arrests than men. No differences in in-hospital mortality and cardiogenic shock were observed between men and women (p > .05). Length of stay was significantly longer for women, while no differences in hospital costs were observed in both sexes. CONCLUSION: Significant sex disparities in management and outcomes were observed in admitted patients with VT. Our results reflect the need for further studies to explore factors causing such diversities.

Network meta-analysis and systematic review comparing efficacy and safety between very high power short duration, high power short duration, and conventional radiofrequency ablation of atrial fibrillation.

BACKGROUND: High-power short-duration (HPSD) atrial fibrillation (AF) ablation with a power of 40-50 W was proved to be safe and effective. Very high-power short-duration (vHPSD) AF ablation is a novel method using >50 W to obtain more durable AF ablation. This study aimed to evaluate the efficacy and safety of vHPSD ablation compared with HPSD ablation and conventional power ablation. METHODS: A literature search for studies that reported AF ablation outcomes, including short-term freedom from atrial arrhythmia, first-pass isolation (FPI) rate, procedure time, and major complications, was conducted utilizing MEDLINE, EMBASE, and Cochrane databases. All relevant studies were included in this analysis. A random-effects model of network meta-analysis and surface under cumulative ranking curve (SUCRA) were used to rank the treatment for all outcomes. RESULTS: A total of 29 studies with 9721 patients were included in the analysis. According to the SUCRA analysis, HPSD ablation had the highest probability of maintaining sinus rhythm. Point estimation showed an odds ratio of 1.5 (95% confidence interval [CI]: 1.2-1.9) between HPSD ablation and conventional power ablation and an odds ratio of 1.3 (95% CI: 0.78-2.2) between vHPSD ablation and conventional power ablation. While the odds ratio of FPI between HPSD ablation and conventional power ablation was 3.6 (95% CI: 1.5-8.9), the odds ratio between vHPSD ablation and conventional power ablation was 2.2 (95% CI: 0.61-8.6). The procedure times of vHPSD and HPSD ablations were comparable and, therefore, shorter than that of conventional power ablation. Major complications were low in all techniques. CONCLUSION: vHPSD ablation did not yield higher efficacy than HPSD ablation and conventional power ablation. With the safety concern, vHPSD ablation outcomes were comparable with those of other techniques.

Differences between kidney retransplant recipients as identified by machine learning consensus clustering.

BACKGROUND: Our study aimed to characterize kidney retransplant recipients using an unsupervised machine-learning approach. METHODS: We performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 17 443 kidney retransplant recipients in the OPTN/UNOS database from 2010 to 2019. We identified each cluster's key characteristics using the standardized mean difference of >.3. We compared the posttransplant outcomes, including death-censored graft failure and patient death among the assigned clusters RESULTS: Consensus cluster analysis identified three distinct clusters of kidney retransplant recipients. Cluster 1 recipients were predominantly white and were less sensitized. They were most likely to receive a living donor kidney transplant and more likely to be preemptive (30%) or need ≤1 year of dialysis (32%). In contrast, cluster 2 recipients were the most sensitized (median PRA 95%). They were more likely to have been on dialysis >1 year, and receive a nationally allocated, low HLA mismatch, standard KDPI deceased donor kidney. Recipients in cluster 3 were more likely to be minorities (37% Black; 15% Hispanic). They were moderately sensitized with a median PRA of 87% and were also most likely to have been on dialysis >1 year. They received locally allocated high HLA mismatch kidneys from standard KDPI deceased donors. Thymoglobulin was the most commonly used induction agent for all three clusters. Cluster 1 had the most favorable patient and graft survival, while cluster 3 had the worst patient and graft survival. CONCLUSION: The use of an unsupervised machine learning approach characterized kidney retransplant recipients into three clinically distinct clusters with differing posttransplant outcomes. Recipients with moderate allosensitization, such as those represented in cluster 3, are perhaps more disadvantaged in the kidney retransplantation process. Potential opportunities for improvement specific to these re-transplant recipients include working to improve opportunities to improve access to living donor kidney transplantation, living donor paired exchange and identifying strategies for better HLA matching.