Evidence map›Paper›PMID 28198403›Full record

ArticleScientific reports2017

Noninvasive quantification of blood potassium concentration from ECG in hemodialysis patients.

Cristiana Corsi, Marilisa Cortesi, Giulia Callisesi, Johan De Bie, Carlo Napolitano, Antonio Santoro, David Mortara, Stefano Severi

Registry-linked trialAbstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07493798 (Serum Potassium Prediction Using Machine Learning and Single-lead ECG), which is not on this map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–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.

NCT07493798 withdrawnnot on this mapstarted 2021, after this paper: background citation

Serum Potassium Prediction Using Machine Learning and Single-lead ECG

TypeobservationalSponsorBrigham and Women's HospitalRan2021 to 2021Enrolled0ConditionsInfection, Heart Failure, Chronic Obstructive Pulmonary Disease, AsthmaArmsPotassium estimation algorithm
3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

  1. Trial
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  9. Review
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  11. ECG frequency changes in potassium disorders: a narrative review.American journal of cardiovascular disease · 2022
    Review
  12. Article
  13. Article
  14. Review
  15. Observational
  16. Article
  17. Article
  18. Classification of drug-induced hERG potassium-channel block from electrocardiographic T-wave features using artificial neural networks.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2019
    Article
  19. Article
  20. 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

8 authors.

Cristiana CorsiDepartment of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy.
Marilisa CortesiDepartment of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy.
Giulia CallisesiDepartment of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy.
Johan De BieMortara Instrument Inc., Milwaukee (WI), USA.
Carlo NapolitanoMolecular Cardiology, IRCCS Fondazione Salvatore Maugeri, Pavia, Italy.
Antonio SantoroNephrology Dialysis, Hypertension Unit, Hospital Policlinico S.Orsola-Malpighi, Bologna, Italy.
David MortaraMortara Instrument Inc., Milwaukee (WI), USA.
Stefano SeveriDepartment of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Blood potassium concentration ([K

Indexed as

ElectrocardiographyRenal DialysisAdolescentAdultChildFemaleHumansLong QT SyndromeMaleMiddle AgedPotassiumYoung AdultPotassium

Identifiers

PMID28198403
PMCPMC5309791

What OpenQuestion holds

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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.