Evidence map›Paper›PMID 34101772›Full record

ArticleClinical kidney journal2021

Dabigatran overload in acute kidney injury: haemodialysis or idarucizumab? A case report and proposal for a decisional algorithm.

Andrea Galassi, Gianmarco Podda, Paola Monciino, Andrea Stucchi, Alberto Del Nero, Mario Cozzolino

Open access · goldAbstract readCase Reports
In one paragraph

Article in Clinical kidney journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 24% of its field
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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
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

6 authors at 2 institutions in 1 country.

Andrea GalassiRenal Division, Department of Health Sciences, University of Milan, San Paolo Hospital, Milan, Italy.
Gianmarco PoddaRenal Division, Department of Health Sciences, University of Milan, San Paolo Hospital, Milan, Italy.
Paola MonciinoRenal Division, Department of Health Sciences, University of Milan, San Paolo Hospital, Milan, Italy.
Andrea StucchiRenal & Dialysis Unit, San Paolo Hospital, ASST Santi Paolo e Carlo, Milano, Italy.
Alberto Del NeroUrology Unit, San Paolo Hospital, ASST Santi Paolo e Carlo, Milan, Italy.
Mario CozzolinoDepartment of Health Sciences, University of Milan Ringgold Standard Institution, Milano, Italy.ORCID 0000-0002-8494-6252
University of Milan · ITOspedale San Paolo · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dabigatran overload has been reported in acute kidney injury (AKI), leading to occasional major bleeding. Haemodialysis (HD) was the method used for reversing dabigatran anticoagulant effects before the approval of idarucizumab, which is now indicated for dabigatran reversal in major bleeding or surgical emergencies. There have been reports of rebound of dabigatran levels following idarucizumab administration in AKI, requiring HD to achieve effective dabigatran clearance. However, a decisional algorithm to individualize treatments for dabigatran overload seems lacking. We present a case of dabigatran accumulation in obstructive AKI with minor bleeding that was successfully treated with HD and tranexamic acid without using idarucizumab, and propose a decision-making algorithm including different pathways in the management of suspected dabigatran overload in AKI.

Indexed as

acute kidney injurydabigatrandecision algorithmdialysisidarucizumab

Identifiers

PMID34101772
PMCPMC8173663
OpenAlexW3011630323

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.