Evidence map›Paper›PMID 29961858›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2018

Reining in the QTc: reducing the risk of Torsades de Pointes across a major health system.

Dora Cheung, Ethan Cumbler, Gary Hale, Jonathan Pell

Open access · bronzeAbstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.3field-weighted citation impact, top 36% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Observational
  2. Article
  3. Article
  4. Article
  5. 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 at 2 institutions in 1 country.

Dora CheungDepartment of Inpatient Pharmacy, University of Colorado Hospital, Aurora, Colorado, USA.
Ethan CumblerDepartment of Internal Medicine, University of Colorado Hospital, Aurora, Colorado, USA.
Gary HaleDepartment of EPIC Inpatient Pharmacy & Clinical Decision Support, University of Colorado Health, Memorial Central Hospital, Colorado Springs, Colorado, USA.
Jonathan PellDepartment of Internal Medicine, University of Colorado Hospital, Aurora, Colorado, USA.
University of Colorado Denver · USUniversity of Colorado Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hospitalized patients have a high prevalence of prolonged QTc and are a high-risk population for Torsades de Pointes (TdP). One modifiable risk factor for TdP is the use of QT prolonging drugs. Electronically alerting providers who are ordering QT prolonging drugs in at-risk patients may help to achieve safer prescribing practices. Our previous study decreased inappropriate prescription of IV haloperidol by 36% using a targeted "smart" electronic alert. We wanted to assess an approach to expanding this type of electronic alert to commonly used QT prolonging medications and evaluate how this would affect prescribing practice. This retrospective cohort study evaluated the impact of these alerts for 12 frequently prescribed high-risk medications across a major health system. Between October 2016 and June 2017, a total of 6453 alerts fired and resulted in 3020 (46.8%) orders being cancelled by the provider. Our focused electronic alert significantly decreased prescribing of QT prolonging medications in high-risk patients.

Indexed as

ElectrocardiographyArrhythmias, CardiacCardiovascular AgentsHeart RateHumansRetrospective StudiesRisk FactorsTorsades de PointesCardiovascular Agents

Identifiers

PMID29961858
PMCPMC7646888
OpenAlexW2810765525

What OpenQuestion holds

Textmetadata
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.