Evidence map›Paper›PMID 30146672›Full record

ReviewCurrent treatment options in cardiovascular medicine2018

Assessment of the QT Interval in Athletes: Red Flags and Pitfalls.

Nikhil Ahluwalia, Hariharan Raju

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current treatment options in cardiovascular medicine, 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
0.3field-weighted citation impact, top 35% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Review
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

2 authors at 2 institutions in 2 countries.

Nikhil AhluwaliaSt George's, University of London, London, UK.
Hariharan RajuMQ Health Cardiology, Macquarie University, 2 Technology Place, Sydney, NSW, 2109, Australia. hari.raju@mqhealth.org.au.
Macquarie University · AUSt George's, University of London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewPre-participation athlete screening has led to the referral of asymptomatic athletes with a prolonged QT interval warranting their evaluation for long QT syndrome (LQTS). Establishing a diagnosis of LQTS can be difficult, particularly in asymptomatic athletes presenting with a prolonged QTc < 500 ms. This review examines the evaluatory pathway to ascertain the common pitfalls leading to mis- or overdiagnosis. We discuss the advanced ECG-based tools and consider their application in the diagnostic process. RECENT

findingsCritical analysis of the ECG, symptom, and pedigree analysis has established value but relies on experienced interpretation. Protocolisation of the former has effectively reduced error. Exercise recovery ECG testing has demonstrated diagnostic value and provocation testing, reliant on QT hysteresis in LQTS, have shown reasonable sensitivity. Although it is becoming more established in experienced centres, its diagnostic value relies on effective risk stratification and subject selection. LQTS is a rare condition and the precision of any available test is greatly diluted if pre-test probability is low. Clinical and familial evaluation and exercise ECG testing are the foundation of the evaluatory process following referral. Adjunctive tests may have high sensitivity for LQTS but rely on high pre-test probability. Several pitfalls have been identified that can lead to misdiagnosis and thus informed evaluation at an experienced specialist centre is appropriate.

Indexed as

AthleteLong QT syndromeQT intervalSports cardiologySudden cardiac death

Identifiers

PMID30146672
OpenAlexW2888792541

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.