Artificial Intelligence in the Identification, Management, and Follow-Up of CKD

Tangri, Navdeep; Ferguson, Thomas

doi:10.34067/kid.0007572021

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…Machine learning approaches have already been used successfully as a time- and cost-saving method to improve diagnostic screening of CKD, closely monitor patients at risk for CKD so as to identify potential onset earlier, predict risk of ESRD, and determine mortality and treatment of patients with CKD [ 112 , 113 , 114 , 115 ]. A model that uses routinely collected laboratory measurements, which are rapidly accessed and can be broadly applied across all stages of CKD, has been suggested as the most optimal [ 116 ].…”

Section: Overcoming Barriers To Ckd Care Deliverymentioning

confidence: 99%

Optimizing Care Delivery in Patients with Chronic Kidney Disease in the United States: Proceedings of a Multidisciplinary Roundtable Discussion and Literature Review

Hirsch,

Danna,

Desai

et al. 2024

JCM

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Background: Approximately 37 million individuals in the United States (US) have chronic kidney disease (CKD). Patients with CKD have a substantial morbidity and mortality, which contributes to a huge economic burden to the healthcare system. A limited number of clinical pathways or defined workflows exist for CKD care delivery in the US, primarily due to a lower prioritization of CKD care within health systems compared with other areas (e.g., cardiovascular disease [CVD], cancer screening). CKD is a public health crisis and by the year 2040, CKD will become the fifth leading cause of years of life lost. It is therefore critical to address these challenges to improve outcomes in patients with CKD. Methods: The CKD Leaders Network conducted a virtual, 3 h, multidisciplinary roundtable discussion with eight subject-matter experts to better understand key factors impacting CKD care delivery and barriers across the US. A premeeting survey identified topics for discussion covering the screening, diagnosis, risk stratification, and management of CKD across the care continuum. Findings from this roundtable are summarized and presented herein. Results: Universal challenges exist across health systems, including a lack of awareness amongst providers and patients, constrained care team bandwidth, inadequate financial incentives for early CKD identification, non-standardized diagnostic classification and triage processes, and non-centralized patient information. Proposed solutions include highlighting immediate and long-term financial implications linked with failure to identify and address at-risk individuals, identifying and managing early-stage CKD, enhancing efforts to support guideline-based education for providers and patients, and capitalizing on next-generation solutions. Conclusions: Payers and other industry stakeholders have opportunities to contribute to optimal CKD care delivery. Beyond addressing the inadequacies that currently exist, actionable tactics can be implemented into clinical practice to improve clinical outcomes in patients at risk for or diagnosed with CKD in the US.

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Section: Overcoming Barriers To Ckd Care Deliverymentioning

confidence: 99%

Optimizing Care Delivery in Patients with Chronic Kidney Disease in the United States: Proceedings of a Multidisciplinary Roundtable Discussion and Literature Review

Hirsch,

Danna,

Desai

et al. 2024

JCM

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“…Integration of single sensor photoplethysmography into regular wearable devices would allow for development of a novel non-invasive, continuous way of monitoring-a technology that is not yet routinely available or approved for ambulatory outpatient use. Navdeep et al highlight the novel AI algorithms, pulseData and Renalytix AI, to determine progression of CKD based on biomarkers from electronic health records [20]. Deep learning has even been shown to help detect acute kidney injury and renal failure progression requiring hemodialysis with a lead time up to 48 h [21].…”

Section: Preventive Cardiologymentioning

confidence: 99%

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

Rao,

Iqbal,

Isath

et al. 2024

Hearts

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Artificial intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this review, we evaluate the myriad applications of artificial intelligence in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid, data-driven insights for diagnosis and treatment. We review the benefits of artificial intelligence such as ChatGPT in personalized patient care, particularly in geriatric care, medication management, weight loss and nutrition, and physical activity guidance. We further delve into its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies. In the realm of medical education, we investigate the utility of artificial intelligence as an information retrieval tool and personalized learning resource for medical students and professionals.

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“…Within the CKD context, AI and machine-learning models have been created for use as early identification and clinical decision aids in DKD, using information from electronic health records and biomarkers, though these models are not yet externally validated [ 98 ]. There are concerns regarding the under-prediction of all quantities of risk during the internal validation of these models [ 99 ]. We anticipate a greater focus on improving the practicality of these technologies going forward.…”

Section: Summary and Future Directionsmentioning

confidence: 99%

Exfoliated Kidney Cells from Urine for Early Diagnosis and Prognostication of CKD: The Way of the Future?

Goldys

Pollock

et al. 2022

IJMS

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Chronic kidney disease (CKD) is a global health issue, affecting more than 10% of the worldwide population. The current approach for formal diagnosis and prognostication of CKD typically relies on non-invasive serum and urine biomarkers such as serum creatinine and albuminuria. However, histological evidence of tubulointerstitial fibrosis is the ‘gold standard’ marker of the likelihood of disease progression. The development of novel biomedical technologies to evaluate exfoliated kidney cells from urine for non-invasive diagnosis and prognostication of CKD presents opportunities to avoid kidney biopsy for the purpose of prognostication. Efforts to apply these technologies more widely in clinical practice are encouraged, given their potential as a cost-effective approach, and no risk of post-biopsy complications such as bleeding, pain and hospitalization. The identification of biomarkers in exfoliated kidney cells from urine via western blotting, enzyme-linked immunosorbent assay (ELISA), immunofluorescence techniques, measurement of cell and protein-specific messenger ribonucleic acid (mRNA)/micro-RNA and other techniques have been reported. Recent innovations such as multispectral autofluorescence imaging and single-cell RNA sequencing (scRNA-seq) have brought additional dimensions to the clinical application of exfoliated kidney cells from urine. In this review, we discuss the current evidence regarding the utility of exfoliated proximal tubule cells (PTC), podocytes, mesangial cells, extracellular vesicles and stem/progenitor cells as surrogate markers for the early diagnosis and prognostication of CKD. Future directions for development within this research area are also identified.

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Artificial Intelligence in the Identification, Management, and Follow-Up of CKD

Abstract: This is an Early Access article. Please select the PDF button, above, to view it.

Cited by 9 publications

References 9 publications

Optimizing Care Delivery in Patients with Chronic Kidney Disease in the United States: Proceedings of a Multidisciplinary Roundtable Discussion and Literature Review

Optimizing Care Delivery in Patients with Chronic Kidney Disease in the United States: Proceedings of a Multidisciplinary Roundtable Discussion and Literature Review

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

Exfoliated Kidney Cells from Urine for Early Diagnosis and Prognostication of CKD: The Way of the Future?

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