Evidence map›Paper›PMID 39195761›Full record

ReviewToxins2024

Closed Loop Ultrafiltration Feedback Control in Hemodialysis: A Narrative Review.

Zijun Dong, Lemuel Rivera Fuentes, Sharon Rao, Peter Kotanko

Abstract readReview
In one paragraph

Review in Toxins, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zijun DongRenal Research Institute, New York, NY 10065, USA.
Lemuel Rivera FuentesRenal Research Institute, New York, NY 10065, USA.ORCID 0000-0003-2916-8985
Sharon RaoRenal Research Institute, New York, NY 10065, USA.
Peter KotankoRenal Research Institute, New York, NY 10065, USA.ORCID 0000-0003-4373-412X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While life-sustaining, hemodialysis is a non-physiological treatment modality that exerts stress on the patient, primarily due to fluid shifts during ultrafiltration. Automated feedback control systems, integrated with sensors that continuously monitor bio-signals such as blood volume, can adjust hemodialysis treatment parameters, e.g., ultrafiltration rate, in real-time. These systems hold promise to mitigate hemodynamic stress, prevent intradialytic hypotension, and improve the removal of water and electrolytes in chronic hemodialysis patients. However, robust evidence supporting their clinical application remains limited. Based on an extensive literature research, we assess feedback-controlled ultrafiltration systems that have emerged over the past three decades in comparison to conventional hemodialysis treatment. We identified 28 clinical studies. Closed loop ultrafiltration control demonstrated effectiveness in 23 of them. No adverse effects of closed loop ultrafiltration control were reported across all trials. Closed loop ultrafiltration control represents an important advancement towards more physiological hemodialysis. Its development is driven by innovations in real-time bio-signals monitoring, advancement in control theory, and artificial intelligence. We expect these innovations will lead to the prevalent adoption of ultrafiltration control in the future, provided its clinical value is substantiated in adequately randomized controlled trials.

Indexed as

Renal DialysisUltrafiltrationFeedbackHumansblood volume monitoringhemodialysisintradialytic hypotensionintradialytic morbid eventsrelative blood volumeultrafiltrationvolume overload

Identifiers

PMID39195761
PMCPMC11360213

What OpenQuestion holds

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Registered trials

None linked

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.