Revolutionizing Chronic Kidney Disease Management with Machine Learning and Artificial Intelligence

Meta-analysis of the association between the dietary inflammatory index and risk of chronic kidney disease

To explore the relationship between Dietary Inflammatory Index (DII) and chronic kidney disease (CKD) risk, we obtained 6 studies (3 prospective studies and 3 cross-sectional studies) from PubMed, CBM, Cochrane Library, and Embase, as of March 6, 2023. Our results revealed a positive link between the CKD risk and rising DII that signified a pro-inflammatory diet. With medium heterogeneity (Overall RR = 1.44, 95%CI: 1.22, 1.71; I2 = 64.7%, P = 0.015), individuals in the highest DII exposure category had a 44% greater overall risk of developing CKD than those in the lowest DII exposure category. According to risk estimations from cross-sectional studies, individuals in the highest DII exposure category had a 64% higher risk of developing CKD than those in the lowest DII exposure category, with significant heterogeneity (RR = 1.64, 95%CI: 1.18, 2.29; I2 = 70.9%, P = 0.032). The risk estimates in cohort studies revealed individuals in the highest DII exposure category had a 28% higher risk of CKD than those in the lowest DII exposure category, with a low heterogeneity (RR = 1.28, 95%CI: 1.14, 1.44; I2 = 17.2%, P = 0.015). Cross-sectional studies showed a nonlinear dose-response relationship between DII and CKD risk, while cohort studies indicated a linear dose-response relationship. Meta-regression results showed publication year, study design, and country had no significant correlation with the meta-analysis. The subgroup analysis results remained consistent. Results support the significance and importance of adopting a better anti-inflammatory diet in preventing CKD. These findings further confirm DII as a tool of the inflammatory potential of the diet to prevent and delay the onset and progression of CKD.

Artificial intelligence and machine learning trends in kidney care

BACKGROUND

The integration of artificial intelligence (AI) and machine learning (ML) in kidney care has seen a significant rise in recent years. This study specifically analyzed AI and ML research publications related to kidney care to identify leading authors, institutions, and countries in this area. It aimed to examine publication trends and patterns, and to explore the impact of collaborative efforts on citation metrics.

METHODS

The study used the Science Citation Index Expanded (SCI-EXPANDED) of Clarivate Analytics Web of Science Core Collection to search for AI and machine learning publications related to nephrology from 1992 to 2021. The authors used quotation marks and Boolean operator "or" to search for keywords in the title, abstract, author keywords, and Keywords Plus. In addition, the 'front page' filter was applied. A total of 5425 documents were identified and analyzed.

RESULTS

The results showed that articles represent 75% of the analyzed documents, with an average author to publications ratio of 7.4 and an average number of citations per publication in 2021 of 18. English articles had a higher citation rate than non-English articles. The USA dominated in all publication indicators, followed by China. Notably, the research also showed that collaborative efforts tend to result in higher citation rates. A significant portion of the publications were found in urology journals, emphasizing the broader scope of kidney care beyond traditional nephrology.

CONCLUSIONS

The findings underscore the importance of AI and ML in enhancing kidney care, offering a roadmap for future research and implementation in this expanding field.

Predicting the Progression of Chronic Kidney Disease: A Systematic Review of Artificial Intelligence and Machine Learning Approaches

Chronic kidney disease (CKD) is a progressive condition characterized by gradual loss of kidney function, necessitating timely monitoring and interventions. This systematic review comprehensively evaluates the application of artificial intelligence (AI) and machine learning (ML) techniques for predicting CKD progression. A rigorous literature search identified 13 relevant studies employing diverse AI/ML algorithms, including logistic regression, support vector machines, random forests, neural networks, and deep learning approaches. These studies primarily aimed to predict CKD progression to end-stage renal disease (ESRD) or the need for renal replacement therapy, with some focusing on diabetic kidney disease progression, proteinuria, or estimated glomerular filtration rate (GFR) decline. The findings highlight the promising predictive performance of AI/ML models, with several achieving high accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve scores. Key factors contributing to enhanced prediction included incorporating longitudinal data, baseline characteristics, and specific biomarkers such as estimated GFR, proteinuria, serum albumin, and hemoglobin levels. Integration of these predictive models with electronic health records and clinical decision support systems offers opportunities for timely risk identification, early interventions, and personalized management strategies. While challenges related to data quality, bias, and ethical considerations exist, the reviewed studies underscore the potential of AI/ML techniques to facilitate early detection, risk stratification, and targeted interventions for CKD patients. Ongoing research, external validation, and careful implementation are crucial to leveraging these advanced analytical approaches in clinical practice, ultimately improving outcomes and reducing the burden of CKD.

Machine learning models for predicting the onset of chronic kidney disease after surgery in patients with renal cell carcinoma

BACKGROUND

Patients with renal cell carcinoma (RCC) have an elevated risk of chronic kidney disease (CKD) following nephrectomy. Therefore, continuous monitoring and subsequent interventions are necessary. It is recommended to evaluate renal function postoperatively. Therefore, a tool to predict CKD onset is essential for postoperative follow-up and management.

METHODS

We constructed a cohort using data from eight tertiary hospitals from the Korean Renal Cell Carcinoma (KORCC) database. A dataset of 4389 patients with RCC was constructed for analysis from the collected data. Nine machine learning (ML) models were used to classify the occurrence and nonoccurrence of CKD after surgery. The final model was selected based on the area under the receiver operating characteristic (AUROC), and the importance of the variables constituting the model was confirmed using the shapley additive explanation (SHAP) value and Kaplan-Meier survival analyses.

RESULTS

The gradient boost algorithm was the most effective among the various ML models tested. The gradient boost model demonstrated superior performance with an AUROC of 0.826. The SHAP value confirmed that preoperative eGFR, albumin level, and tumor size had a significant impact on the occurrence of CKD after surgery.

CONCLUSIONS

We developed a model to predict CKD onset after surgery in patients with RCC. This predictive model is a quantitative approach to evaluate post-surgical CKD risk in patients with RCC, facilitating improved prognosis through personalized postoperative care.

Evaluating ChatGPT's efficacy in assessing the safety of non-prescription medications and supplements in patients with kidney disease

Background

This study investigated the efficacy of ChatGPT-3.5 and ChatGPT-4 in assessing drug safety for patients with kidney diseases, comparing their performance to Micromedex, a well-established drug information source. Despite the perception of non-prescription medications and supplements as safe, risks exist, especially for those with kidney issues. The study's goal was to evaluate ChatGPT's versions for their potential in clinical decision-making regarding kidney disease patients.

Method

The research involved analyzing 124 common non-prescription medications and supplements using ChatGPT-3.5 and ChatGPT-4 with queries about their safety for people with kidney disease. The AI responses were categorized as "generally safe," "potentially harmful," or "unknown toxicity." Simultaneously, these medications and supplements were assessed in Micromedex using similar categories, allowing for a comparison of the concordance between the two resources.

Results

Micromedex identified 85 (68.5%) medications as generally safe, 35 (28.2%) as potentially harmful, and 4 (3.2%) of unknown toxicity. ChatGPT-3.5 identified 89 (71.8%) as generally safe, 11 (8.9%) as potentially harmful, and 24 (19.3%) of unknown toxicity. GPT-4 identified 82 (66.1%) as generally safe, 29 (23.4%) as potentially harmful, and 13 (10.5%) of unknown toxicity. The overall agreement between Micromedex and ChatGPT-3.5 was 64.5% and ChatGPT-4 demonstrated a higher agreement at 81.4%. Notably, ChatGPT-3.5's suboptimal performance was primarily influenced by a lower concordance rate among supplements, standing at 60.3%. This discrepancy could be attributed to the limited data on supplements within ChatGPT-3.5, with supplements constituting 80% of medications identified as unknown.

Conclusion

ChatGPT's capabilities in evaluating the safety of non-prescription drugs and supplements for kidney disease patients are modest compared to established drug information resources. Neither ChatGPT-3.5 nor ChatGPT-4 can be currently recommended as reliable drug information sources for this demographic. The results highlight the need for further improvements in the model's accuracy and reliability in the medical domain.

Performance of ChatGPT on Nephrology Test Questions

BACKGROUND

ChatGPT is a novel tool that allows people to engage in conversations with an advanced machine learning model. ChatGPT's performance in the US Medical Licensing Examination is comparable with a successful candidate's performance. However, its performance in the nephrology field remains undetermined. This study assessed ChatGPT's capabilities in answering nephrology test questions.

METHODS

Questions sourced from Nephrology Self-Assessment Program and Kidney Self-Assessment Program were used, each with multiple-choice single-answer questions. Questions containing visual elements were excluded. Each question bank was run twice using GPT-3.5 and GPT-4. Total accuracy rate, defined as the percentage of correct answers obtained by ChatGPT in either the first or second run, and the total concordance, defined as the percentage of identical answers provided by ChatGPT during both runs, regardless of their correctness, were used to assess its performance.

RESULTS

A comprehensive assessment was conducted on a set of 975 questions, comprising 508 questions from Nephrology Self-Assessment Program and 467 from Kidney Self-Assessment Program. GPT-3.5 resulted in a total accuracy rate of 51%. Notably, the employment of Nephrology Self-Assessment Program yielded a higher accuracy rate compared with Kidney Self-Assessment Program (58% versus 44%; P < 0.001). The total concordance rate across all questions was 78%, with correct answers exhibiting a higher concordance rate (84%) compared with incorrect answers (73%) ( P < 0.001). When examining various nephrology subfields, the total accuracy rates were relatively lower in electrolyte and acid-base disorder, glomerular disease, and kidney-related bone and stone disorders. The total accuracy rate of GPT-4's response was 74%, higher than GPT-3.5 ( P < 0.001) but remained below the passing threshold and average scores of nephrology examinees (77%).

CONCLUSIONS

ChatGPT exhibited limitations regarding accuracy and repeatability when addressing nephrology-related questions. Variations in performance were evident across various subfields.

Innovating Personalized Nephrology Care: Exploring the Potential Utilization of ChatGPT

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.

Why is chronic kidney disease progressive? Evolutionary adaptations and maladaptations

Despite significant advances in renal physiology, the global prevalence of chronic kidney disease (CKD) continues to increase. The emergence of multicellular organisms gave rise to increasing complexity of life resulting in trade-offs reflecting ancestral adaptations to changing environments. Three evolutionary traits shape CKD over the lifespan: 1) variation in nephron number at birth, 2) progressive nephron loss with aging, and 3) adaptive kidney growth in response to decreased nephron number. Although providing plasticity in adaptation to changing environments, the cell cycle must function within constraints dictated by available energy. Prioritized allocation of energy available through the placenta can restrict fetal nephrogenesis, a risk factor for CKD. Moreover, nephron loss with aging is a consequence of cell senescence, a pathway accelerated by adaptive nephron hypertrophy that maintains metabolic homeostasis at the expense of increased vulnerability to stressors. Driven by reproductive fitness, natural selection operates in early life but diminishes thereafter, leading to an exponential increase in CKD with aging, a product of antagonistic pleiotropy. A deeper understanding of the evolutionary constraints on the cell cycle may lead to manipulation of the balance between progenitor cell renewal and differentiation, regulation of cell senescence, and modulation of the balance between cell proliferation and hypertrophy. Application of an evolutionary perspective may enhance understanding of adaptation and maladaptation by nephrons in the progression of CKD, leading to new therapeutic advances.

Exploring the Potential of Chatbots in Critical Care Nephrology

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.

Ethical Implications of Chatbot Utilization in Nephrology

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.