Evidence map›Paper›PMID 26358085›Full record

ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2016

Differential risk reclassification improvement by exercise testing and myocardial perfusion imaging in patients with suspected and known coronary artery disease.

Angela S Koh, Fei Gao, C T Chin, Felix Y J Keng, Ru-San Tan, Terrance S J Chua

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Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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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

6 citing papers in PubMed.

  1. Non-traditional factors affecting referral for coronary angiography following SPECT myocardial perfusion imaging.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022
    Article
  2. CRAX: A simple cardiovascular risk assessment tool to predict risk of acute myocardial infarction or death.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2020
    Article
  3. Cardiovascular risk assessment models: Have we found the perfect solution yet?Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2020
    Article
  4. Review of cardiovascular imaging in the journal of nuclear cardiology in 2016: Part 2 of 2-myocardial perfusion imaging.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2017
    Review
  5. The benefits of advanced risk reclassification.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2016
    Article
  6. Review of Cardiovascular Imaging in the Journal of Nuclear Cardiology in 2015-Part 2 of 2: Myocardial perfusion imaging.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2016
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Angela S KohNational Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore. angela.koh.s.m@nhcs.com.sg.
Fei GaoNational Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
C T ChinNational Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
Felix Y J KengNational Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
Ru-San TanNational Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
Terrance S J ChuaNational Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe objective of this study is to compare the incremental prognostic and net risk reclassification value of exercise testing alone vs exercise myocardial perfusion imaging (MPI) for estimating the risk of death in patients with suspected and known coronary artery disease (CAD).

methods6702 patients with suspected CAD and 2008 with known CAD had treadmill exercise MPI and were followed for 2.5 ± 0.9 years for the occurrence of all-cause death. The estimation of risk of death and net reclassification improvement (NRI) were examined in three models. Model 1: clinical variables; Model 2: model 1+Duke Treadmill Score; and Model 3: model 2+ MPI variables. Risk estimates were categorized as <1%, 1-3%, and >3% risk of death per year.

resultsIn patients with suspected CAD, the global Chi-square for predicting risk of death increased significantly for Model 2 compared to Model 1 (74.78 vs 63.86 to (P = .001). However, adding MPI variables in Model 3 did not further improve predictive value (Chi-square 79.38, P = .10). In patients with suspected CAD risk, reclassification improved significantly in Model 2 over Model 1 (NRI = 0.12, 95% CI 0.02 to 0.22, P = .019), but not in Model 3 (NRI = 0.0009, 95% CI -0.072 to 0.070; P = .98). In contrast, in patients with known CAD Model 2 did not yield significant improvements for predicting risk and risk reclassification compared to Model 1. However, global Chi-square of Model 3 was significantly higher than that of Model 2 (30.03 vs 6.56, P < .0001) with associated significant reclassification improvement (NRI = 0.26 95% CI 0.067 to 0.46. P = .0084).

conclusionRisk reclassification by diagnostic testing is importantly influenced by baseline characteristics of patient cohorts. In patients with suspected CAD, NRI is predominately achieved by exercise variables, whereas in patients with known CAD, greatest NRI is obtained by MPI variables.

Indexed as

Proportional Hazards ModelsCoronary Artery DiseaseDeath, Sudden, CardiacExercise TestFemaleHumansMaleMiddle AgedMyocardial Perfusion ImagingPrevalencePrognosisReproducibility of ResultsRisk AssessmentRisk FactorsSensitivity and SpecificitySingaporediagnostic and prognostic applicationexercise: stress testingMyocardial perfusion imaging: SPECT

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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.