Evidence map›Paper›PMID 30567385›Full record

ReviewNon-coding RNA2018

Non-Coding RNAs to Aid in Neurological Prognosis after Cardiac Arrest.

Antonio Salgado-Somoza, Francesca Maria Stefanizzi, Pascal Stammet, David Erlinge, Hans Friberg, Niklas Nielsen, Yvan Devaux

Abstract readReview
In one paragraph

Review in Non-coding RNA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

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

2 citing papers in PubMed.

  1. Article
  2. RNAs in Brain and Heart Diseases.International journal of molecular sciences · 2020
    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

7 authors.

Antonio Salgado-SomozaCardiovascular Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg. antonio.salgadosomoza@lih.lu.
Francesca Maria StefanizziCardiovascular Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg. francescamaria.stefanizzi@lih.lu.ORCID 0000-0001-5029-9491
Pascal StammetMedical and Health Department, Luxembourg Fire and Rescue Corps, L-2557 Luxembourg, Luxembourg. Pascal.Stammet@secours.etat.lu.
David ErlingeDepartment of Cardiology, Clinical Sciences, Lund University, 221 85 Lund, Sweden. david.erlinge@gmail.com.
Hans FribergSkane University Hospital, Lund University, 221 85 Malmo, Sweden. hans.a.friberg@gmail.com.
Niklas NielsenHelsingborg Hospital, Lund University, 25187 Helsingborg, Sweden. niklas.nielsen@telia.com.
Yvan DevauxCardiovascular Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg. yvan.devaux@lih.lu.ORCID 0000-0002-5321-8543

Funding

Fonds National de la Recherche Luxembourg C14/BM/8225223Fonds National de la Recherche Luxembourg C17/BM/11613033Ministère de l'Enseignement Supérieur et de la Recherche Luxembourg naSociété pour la Recherche sur les Maladies Cardio-Vasculaires Luxembourg naSvenska Forskningsrådet Formas naSwedish governmental funding within the ALF-system na
6 · The paper itself

Abstract

Cardiovascular disease in general, and sudden cardiac death in particular, have an enormous socio-economic burden worldwide. Despite significant efforts to improve cardiopulmonary resuscitation, survival rates remain low. Moreover, patients who survive to hospital discharge have a high risk of developing severe physical or neurological symptoms. Being able to predict outcomes after resuscitation from cardiac arrest would make it possible to tailor healthcare approaches, thereby maximising efforts for those who would mostly benefit from aggressive therapy. However, the identification of patients at risk of poor recovery after cardiac arrest is still a challenging task which could be facilitated by novel biomarkers. Recent investigations have recognised the potential of non-coding RNAs to aid in outcome prediction after cardiac arrest. In this review, we summarize recent discoveries and propose a handful of novel perspectives for the use of non-coding RNAs to predict outcome after cardiac arrest, discussing their use for precision medicine.

Indexed as

biomarkerscardiac arrestnon-coding RNAs

Identifiers

PMID30567385
PMCPMC6316658

What OpenQuestion holds

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LicenceCC BY
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.