Evidence map›Paper›PMID 11994029›Full record

ReviewDrug safety2002

Safety of non-antiarrhythmic drugs that prolong the QT interval or induce torsade de pointes: an overview.

Fabrizio De Ponti, Elisabetta Poluzzi, Andrea Cavalli, Maurizio Recanatini, Nicola Montanaro

Abstract readReview
PubMed Publisher
In one paragraph

Review in Drug safety, 2002. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
76citing papers in PubMed, 3 pooled it
13.2field-weighted citation impact, top 1% 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

76 citing papers in PubMed, 3 syntheses or guidelines pooled it, 306 citations in OpenAlex.

  1. Pooled it
  2. Guideline
  3. Pooled it
  4. Trial
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  6. Article
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  13. Article
  14. Cardiovascular Safety Assessment in Cancer Drug Development.Journal of the American Heart Association · 2021
    Review
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  17. Article
  18. Review
  19. Article
  20. Prevalence of Torsades de Pointes inducing drugs usage among elderly outpatients in North Jordan Hospitals.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2018
    Article

16 more citing papers are in PubMed but not listed here.

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

5 authors at 1 institution in 1 country.

Fabrizio De PontiDepartment of Pharmacology, University of Bologna, Bologna, Italy. deponti@biocfarm.unibo.it
Elisabetta Poluzzi
Andrea Cavalli
Maurizio Recanatini
Nicola Montanaro
University of Bologna · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The long and growing list of non-antiarrhythmic drugs associated with prolongation of the QT interval of the electrocardiogram has generated concern not only for regulatory interventions leading to drug withdrawal, but also for the unjustified view that QT prolongation is usually an intrinsic effect of a whole therapeutic class [e.g. histamine H(1) receptor antagonists (antihistamines)], whereas, in many cases, it is displayed only by some compounds within a given class of non-antiarrhythmic drugs because of an effect on cardiac repolarisation. We provide an overview of the different classes of non-antiarrhythmic drugs reported to prolong the QT interval (e.g. antihistamines, antipsychotics, antidepressants and macrolides) and discusses the clinical relevance of the QT prolonging effect. Drug-induced torsade de pointes are sometimes considered idiosyncratic, totally unpredictable adverse drug reactions, whereas a number of risk factors for their occurrence is now recognised. Widespread knowledge of these risk factors and implementation of a comprehensive list of QT prolonging drugs becomes an important issue. Risk factors include congenital long QT syndrome, clinically significant bradycardia or heart disease, electrolyte imbalance (especially hypokalaemia, hypomagnesaemia, hypocalcaemia), impaired hepatic/renal function, concomitant treatment with other drugs with known potential for pharmacokinetic/pharmacodynamic interactions (e.g. azole antifungals, macrolide antibacterials and class I or III antiarrhythmic agents). This review provides insight into the strategies that should be followed during a drug development program when a drug is suspected to affect the QT interval. The factors limiting the predictive value of preclinical and clinical studies are also outlined. The sensitivity of preclinical tests (i.e. their ability to label as positive those drugs with a real risk of inducing QT pronglation in humans) is sufficiently good, but their specificity (i.e. their ability to label as negative those drugs carrying no risk) is not well established. Verapamil is a notable example of a false positive: it blocks human ether-a-go-go-related (HERG) K(+) channels, but is reported to have little potential to trigger torsade de pointes. Although inhibition of HERG K(+) channels has been proposed as a primary test for screening purposes, it is important to remember that several ion currents are involved in the generation of the cardiac potential and that metabolites must be specifically tested in this in vitro test. At the present state of knowledge, no preclinical model has an absolute predictive value or can be considered as a gold standard. Therefore, the use of several models facilitates decision making and is recommended by most experts in the field.

Indexed as

ElectrocardiographyAnti-Bacterial AgentsAntidepressive AgentsAntipsychotic AgentsDrug DesignHistamine H1 AntagonistsHumansLong QT SyndromeMacrolidesRisk FactorsTorsades de PointesAnti-Bacterial AgentsAntidepressive AgentsAntipsychotic AgentsHistamine H1 AntagonistsMacrolides

Identifiers

PMID11994029
OpenAlexW1963748793

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.