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Coupling Data Mining and Laboratory Experiments to Discover Drug Interactions Causing QT Prolongation

Overview of attention for article published in JACC, October 2016
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

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19 news outlets
blogs
2 blogs
twitter
121 X users
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5 Facebook pages

Citations

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62 Dimensions

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94 Mendeley
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Title
Coupling Data Mining and Laboratory Experiments to Discover Drug Interactions Causing QT Prolongation
Published in
JACC, October 2016
DOI 10.1016/j.jacc.2016.07.761
Pubmed ID
Authors

Tal Lorberbaum, Kevin J. Sampson, Jeremy B. Chang, Vivek Iyer, Raymond L. Woosley, Robert S. Kass, Nicholas P. Tatonetti

Abstract

QT interval-prolonging drug-drug interactions (QT-DDIs) may increase the risk of life-threatening arrhythmia. Despite guidelines for testing from regulatory agencies, these interactions are usually discovered after drugs are marketed and may go undiscovered for years. Using a combination of adverse event reports, electronic health records (EHR), and laboratory experiments, the goal of this study was to develop a data-driven pipeline for discovering QT-DDIs. 1.8 million adverse event reports were mined for signals indicating a QT-DDI. Using 1.6 million electrocardiogram results from 380,000 patients in our institutional EHR, these putative interactions were either refuted or corroborated. In the laboratory, we used patch-clamp electrophysiology to measure the human ether-à-go-go-related gene (hERG) channel block (the primary mechanism by which drugs prolong the QT interval) to evaluate our top candidate. Both direct and indirect signals in the adverse event reports provided evidence that the combination of ceftriaxone (a cephalosporin antibiotic) and lansoprazole (a proton-pump inhibitor) will prolong the QT interval. In the EHR, we found that patients taking both ceftriaxone and lansoprazole had significantly longer QTc intervals (up to 12 ms in white men) and were 1.4 times more likely to have a QTc interval above 500 ms. In the laboratory, we found that, in combination and at clinically relevant concentrations, these drugs blocked the hERG channel. As a negative control, we evaluated the combination of lansoprazole and cefuroxime (another cephalosporin), which lacked evidence of an interaction in the adverse event reports. We found no significant effect of this pair in either the EHR or in the electrophysiology experiments. Class effect analyses suggested this interaction was specific to lansoprazole combined with ceftriaxone but not with other cephalosporins. Coupling data mining and laboratory experiments is an efficient method for identifying QT-DDIs. Combination therapy of ceftriaxone and lansoprazole is associated with increased risk of acquired long QT syndrome.

X Demographics

X Demographics

The data shown below were collected from the profiles of 121 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
United Kingdom 1 1%
Brazil 1 1%
Unknown 88 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 19%
Researcher 15 16%
Other 11 12%
Student > Master 11 12%
Student > Bachelor 5 5%
Other 18 19%
Unknown 16 17%
Readers by discipline Count As %
Medicine and Dentistry 24 26%
Biochemistry, Genetics and Molecular Biology 10 11%
Pharmacology, Toxicology and Pharmaceutical Science 10 11%
Computer Science 9 10%
Agricultural and Biological Sciences 3 3%
Other 17 18%
Unknown 21 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 219. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 December 2023.
All research outputs
#179,745
of 25,732,188 outputs
Outputs from JACC
#397
of 16,923 outputs
Outputs of similar age
#3,491
of 333,688 outputs
Outputs of similar age from JACC
#15
of 290 outputs
Altmetric has tracked 25,732,188 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 16,923 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 30.1. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 333,688 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 290 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.