The anaemia control model: Does it help nephrologists in therapeutic decision-making in the management of anaemia?

Bucalo, María Laura; Barbieri, C.; Roca, Susana; Titapiccolo, Jasmine Ion; Romero, Maria Soledad Ros; Ramos, Rosa; Albaladejo, Mercedes; Manzano, Diana; Mari, Flavio; Molina, Manuel

doi:10.1016/j.nefroe.2018.10.001

Cited by 12 publications

(15 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of studies have provided proof of concept that AI/ML-based CDSS could help to improve outcomes for anemic patients undergoing hemodialysis, particularly through minimizing the risks of adverse effects from the use of erythropoietin-stimulating agents, and also could reduce cost. 4,31,32 However, these CDSSs are noted to be trained with a limited data set from hemodialysis patients and hence do not apply to predialysis and peritoneal dialysis patients.…”

Section: Disparate Digitalization Of Nephrology Care and Care Pathways Clinical Decision Making And Treatment Guidelinesmentioning

confidence: 99%

A Call for an Ethics and Governance Action Plan to Harness the Power of Artificial Intelligence and Digitalization in Nephrology

Caals

2021

Seminars in Nephrology

View full text Add to dashboard Cite

Digitalization in nephrology has progressed in a manner that is disparate and siloed, even though learning (under a broader Learning Health System initiative) has been manifested in all the main areas of clinical application. Most applications based on artificial intelligence/machine learning (AI/ML) are still in the initial developmental stages and are yet to be adequately validated and shown to contribute to positive patient outcomes. There is also no consistent or comprehensive digitalization plan, and insufficient data are a limiting factor across all of these areas. In this article, we first consider how digitalization along nephrology care pathways relates to the Learning Health System initiative. We then consider the current state of AI/ML-based software and devices in nephrology and the ethical and regulatory challenges in scaling them up toward broader clinical application. We conclude with our proposal to establish a dedicated ethics and governance framework that is centered around health care providers in nephrology and the AI/ML-based software to which their work relates. This framework should help to integrate ethical and regulatory values and considerations, involve a wide range of stakeholders, and apply across normative domains that are conventionally demarcated as clinical, research, and public health. Semin Nephrol 41:282−293

show abstract

Section: Disparate Digitalization Of Nephrology Care and Care Pathways Clinical Decision Making And Treatment Guidelinesmentioning

confidence: 99%

A Call for an Ethics and Governance Action Plan to Harness the Power of Artificial Intelligence and Digitalization in Nephrology

Caals

2021

Seminars in Nephrology

View full text Add to dashboard Cite

show abstract

“…168 Of note, preliminary trials provide some early encouragement. 169 Such tools could also be combined with approaches to reduce instantaneous solute fluxes while keeping total solute mass transfer and dialysis adequacy equivalent in an attempt to reduce intradialytic morbidity. [140][141][142][143] It is clear from past experience that a one-size-fits-all approach to reducing the physiological stresses of dialysis does not work.…”

Section: A Personalized Medicine Approachmentioning

confidence: 99%

“…Personalized medicine based on mathematical models and artificial intelligence tools (neuronal networks, machine learning) combined with remote wearable biosensors may support physicians' decision making for individual patients regarding the selection of appropriate treatment modalities and technical options such as control of ultrafiltration rate and dialysate sodium and electrolytic concentrations. 168,169 Furthermore, continuous 24/7 monitoring of vital parameters via wearable sensors (e.g., heart rate, blood pressure, oxygen saturation) in the most fragile patients may facilitate an early detection or even prediction of serious intra-and/ or interdialytic morbid events. As suggested by recent reports, new remote sensing technology, so-called ihealth tracker connected devices, offers novel and convenient tools for monitoring in a fully automated, ambulatory and unobtrusive way in high-risk dialysis patients through the entire nychthemeral cycle.…”

Section: A Personalized Medicine Approachmentioning

confidence: 99%

Dialysis-Induced Cardiovascular and Multiorgan Morbidity

Canaud

Kooman

Selby

et al. 2020

Kidney International Reports

View full text Add to dashboard Cite

Hemodialysis has saved many lives, albeit with significant residual mortality. Although poor outcomes may reflect advanced age and comorbid conditions, hemodialysis per se may harm patients, contributing to morbidity and perhaps mortality. Systemic circulatory "stress" resulting from hemodialysis treatment schedule may act as a disease modifier, resulting in a multiorgan injury superimposed on preexistent comorbidities. New functional intradialytic imaging (i.e., echocardiography, cardiac magnetic resonance imaging [MRI]) and kinetic of specific cardiac biomarkers (i.e., Troponin I) have clearly documented this additional source of end-organ damage. In this context, several factors resulting from patienthemodialysis interaction and/or patient management have been identified. Intradialytic hypovolemia, hypotensive episodes, hypoxemia, solutes, and electrolyte fluxes as well as cardiac arrhythmias are among the contributing factors to systemic circulatory stress that are induced by hemodialysis. Additionally, these factors contribute to patients' symptom burden, impair cognitive function, and finally have a negative impact on patients' perception and quality of life. In this review, we summarize the adverse systemic effects of current intermittent hemodialysis therapy, their pathophysiologic consequences, review the evidence for interventions that are cardioprotective, and explore new approaches that may further reduce the systemic burden of hemodialysis. These include improved biocompatible materials, smart dialysis machines that automatically may control the fluxes of solutes and electrolytes, volume and hemodynamic control, health trackers, and potentially disruptive technologies facilitating a more personalized medicine approach.

show abstract

“…Systems using AI for predicting Hb values for hemodialysis patients were presented in the literature [13,14]. Anemia control model (ACM) achieved improved control accuracy and decreased patients' need for ESAs [15,16].…”

Section: Ivyspringmentioning

confidence: 99%

Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients

Ohara¹,

Ikeda²,

Sugitani³

et al. 2021

Int. J. Med. Sci.

View full text Add to dashboard Cite

Anemia, for which erythropoiesis-stimulating agents (ESAs) and iron supplements (ISs) are used as preventive measures, presents important difficulties for hemodialysis patients. Nevertheless, the number of physicians able to manage such medications appropriately is not keeping pace with the rapid increase of hemodialysis patients. Moreover, the high cost of ESAs imposes heavy burdens on medical insurance systems. An artificial-intelligence-supported anemia control system (AISACS) trained using administration direction data from experienced physicians has been developed by the authors. For the system, appropriate data selection and rectification techniques play important roles. Decision making related to ESAs poses a multi-class classification problem for which a two-step classification technique is introduced. Several validations have demonstrated that AISACS exhibits high performance with correct classification rates of 72%-87% and clinically appropriate classification rates of 92%-98%.

show abstract

The anaemia control model: Does it help nephrologists in therapeutic decision-making in the management of anaemia?

Cited by 12 publications

References 43 publications

A Call for an Ethics and Governance Action Plan to Harness the Power of Artificial Intelligence and Digitalization in Nephrology

A Call for an Ethics and Governance Action Plan to Harness the Power of Artificial Intelligence and Digitalization in Nephrology

Dialysis-Induced Cardiovascular and Multiorgan Morbidity

Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients

Contact Info

Product

Resources

About