Evidence map›Paper›PMID 33694143›Full record

ArticleApplied clinical informatics2021

Assessing Prescriber Behavior with a Clinical Decision Support Tool to Prevent Drug-Induced Long QT Syndrome.

Katy E Trinkley, Jonathan M Pell, Dario D Martinez, Nicola R Maude, Gary Hale, Michael A Rosenberg

Abstract read
In one paragraph

Article in Applied clinical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Observational
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

6 authors.

Katy E TrinkleyDepartment of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States.
Jonathan M PellDepartment of Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States.
Dario D MartinezDepartment of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States.
Nicola R MaudeDepartment of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States.
Gary HaleDepartment of Clinical Informatics, University of Colorado Health, Aurora, Colorado, United States.
Michael A RosenbergDivision of Cardiac Electrophysiology, University of Colorado School of Medicine, Aurora, Colorado, United States.

Funding

Development of End-To-End Clinical Decision Support Tools To Prevent Cardiotoxic Drug ResponseR01HL146824 · NHLBI · UNIVERSITY OF COLORADO DENVER · PI ROSENBERG, MICHAEL A · 2020 to 2024
$3.9M
IMPlementation to Achieve Clinical Transformation (IMPACT): The Colorado Training ProgramK12HL137862 · NHLBI · UNIVERSITY OF COLORADO DENVER · PI GLASGOW, RUSSELL E, HAVRANEK, EDWARD PAUL · 2017 to 2021
$2.9M
Genetics of Cardiotoxic Drug ResponseK23HL127296 · NHLBI · UNIVERSITY OF COLORADO DENVER · PI ROSENBERG, MICHAEL A · 2015 to 2020
$932k
NHLBI NIH HHS K12 HL137862NHLBI NIH HHS K23 HL127296NHLBI NIH HHS R01 HL146824
6 · The paper itself

Abstract

objectiveClinical decision support (CDS) alerts built into the electronic health record (EHR) have the potential to reduce the risk of drug-induced long QT syndrome (diLQTS) in susceptible patients. However, the degree to which providers incorporate this information into prescription behavior and the impact on patient outcomes is often unknown.

methodsWe examined provider response data over a period from October 8, 2016 until November 8, 2018 for a CDS alert deployed within the EHR from a 13-hospital integrated health care system that fires when a patient with a QTc ≥ 500 ms within the past 14 days is prescribed a known QT-prolonging medication. We used multivariate generalized estimating equations to analyze the impact of therapeutic alternatives, relative risk of diLQTS for specific medications, and patient characteristics on provider response to the CDS and overall patient mortality.

resultsThe CDS alert fired 15,002 times for 7,510 patients for which the most common response (51.0%) was to override the alert and order the culprit medication. In multivariate models, we found that patient age, relative risk of diLQTS, and presence of alternative agents were significant predictors of adherence to the CDS alerts and that nonadherence itself was a predictor of mortality. Risk of diLQTS and presence of an alternative agent are major factors in provider adherence to a CDS to prevent diLQTS; however, provider nonadherence was associated with a decreased risk of mortality.

conclusionSurrogate endpoints, such as provider adherence, can be useful measures of CDS value but attention to hard outcomes, such as mortality, is likely needed.

Indexed as

Decision Support Systems, ClinicalLong QT SyndromeMedical Order Entry SystemsPharmaceutical PreparationsElectronic Health RecordsHumansPharmaceutical Preparations

Identifiers

PMID33694143
PMCPMC7946597

What OpenQuestion holds

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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.