Artificial intelligence and digital health for volume maintenance in hemodialysis patients.

Sandys, Vicki; Sexton, Donal; O'Seaghdha, Conall

dc.contributor.author	Sandys, Vicki
dc.contributor.author	Sexton, Donal
dc.contributor.author	O'Seaghdha, Conall
dc.date.accessioned	2024-06-27T11:28:17Z
dc.date.available	2024-06-27T11:28:17Z
dc.date.issued	2022-06-23
dc.identifier.pmid	35739632
dc.identifier.doi	10.1111/hdi.13033
dc.identifier.uri	http://hdl.handle.net/10147/641822
dc.description	Chronic fluid overload is associated with morbidity and mortality in hemodialysis patients. Optimizing the diagnosis and treatment of fluid overload remains a priority for the nephrology community. Although current methods of assessing fluid status, such as bioimpedance and lung ultrasound, have prognostic and diagnostic value, no single system or technique can be used to maintain euvolemia. The difficulty in maintaining and assessing fluid status led to a publication by the Kidney Health Initiative in 2019 aimed at fostering innovation in fluid management therapies. This review article focuses on the current limitations in our assessment of extracellular volume, and the novel technology and methods that can create a new paradigm for fluid management. The cardiology community has published research on multiparametric wearable devices that can create individualized predictions for heart failure events. In the future, similar wearable technology may be capable of tracking fluid changes during the interdialytic period and enabling behavioral change. Machine learning methods have shown promise in the prediction of volume-related adverse events. Similar methods can be leveraged to create accurate, automated predictions of dry weight that can potentially be used to guide ultrafiltration targets and interdialytic weight gain goals.	en_US
dc.language.iso	en	en_US
dc.rights	© 2022 The Authors. Hemodialysis International published by Wiley Periodicals LLC on behalf of International Society for Hemodialysis.
dc.rights	Attribution 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.subject	Hemodialysis	en_US
dc.subject	Machine learning	en_US
dc.subject	Volume	en_US
dc.subject	Wearable sensors	en_US
dc.title	Artificial intelligence and digital health for volume maintenance in hemodialysis patients.	en_US
dc.type	Article	en_US
dc.type	Other	en_US
dc.identifier.eissn	1542-4758
dc.identifier.journal	Hemodialysis international. International Symposium on Home Hemodialysis	en_US
dc.source.journaltitle	Hemodialysis international. International Symposium on Home Hemodialysis
dc.source.volume	26
dc.source.issue	4
dc.source.beginpage	480
dc.source.endpage	495
refterms.dateFOA	2024-06-27T11:28:19Z
dc.source.country	Canada