ReviewHemodialysis international. International Symposium on Home Hemodialysis2022
Artificial intelligence and digital health for volume maintenance in hemodialysis patients.
Review in Hemodialysis international. International Symposium on Home Hemodialysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Internet-based remote exercise interventions for maintenance hemodialysis patients: a systematic review of the evidence.BMC nephrology · 2025Pooled it
- Functionality and sustainability of telemedicine for home-based management of patients with chronic kidney disease: the telemechron study.Journal of nephrology · 2025Observational
- Reducing Excessive Interdialytic Weight Gain in Young Hemodialysis Patients: A Quality Improvement Initiative.Kidney medicine · 2025Article
- Volume Assessment in Patients Undergoing Long-Term Dialysis.Journal of the American Society of Nephrology : JASN · 2025Review
- How to Limit Interdialytic Weight Gain in Patients on Maintenance Hemodialysis: State of the Art and Perspectives.Journal of clinical medicine · 2025Review
- Ask a Doctor a Question: A Clinician's Message to the Industry.Medicina (Kaunas, Lithuania) · 2025Article
- Telemedicine for home-based management of patients with chronic kidney diseases and comorbidities in Tuscany North-west region: a pilot study protocol (telemechron study).Journal of nephrology · 2024Article
- Review
- Artificial intelligence and digital health for volume maintenance in hemodialysis patients.Hemodialysis international. International Symposium on Home Hemodialysis · 2022Review
- Safety and Efficacy of the First Mobile Intelligent Hemodialysis Platform in Mainland China: Protocol for a Single-Center Prospective Exploratory Study in Maintenance Hemodialysis Patients.Kidney diseases (Basel, Switzerland)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.