Evidence map›Paper›PMID 28224450›Full record

ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2018

Artificial neural network-based model enhances risk stratification and reduces non-invasive cardiac stress imaging compared to Diamond-Forrester and Morise risk assessment models: A prospective study.

Hussain A Isma'eel, George E Sakr, Mustapha Serhan, Nader Lamaa, Ayman Hakim, Paul C Cremer, Wael A Jaber, Torkom Garabedian, Imad Elhajj, Antoine B Abchee

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
–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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Review
  6. Can machine learning spin straw into gold?Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2018
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Hussain A Isma'eelDivision of Cardiology, Department of Internal Medicine, American University of Beirut, PO-BOX 11-0236, Riad el Solh, Beirut, 11072020, Lebanon. hi09@aub.edu.lb.
George E SakrÉcole Superieurd'Ing. de Beirut (ESIB), St Joseph University, Beirut, Lebanon.
Mustapha SerhanDivision of Cardiology, Department of Internal Medicine, American University of Beirut, PO-BOX 11-0236, Riad el Solh, Beirut, 11072020, Lebanon.
Nader LamaaDivision of Cardiology, Department of Internal Medicine, American University of Beirut, PO-BOX 11-0236, Riad el Solh, Beirut, 11072020, Lebanon.
Ayman HakimDivision of Cardiology, Department of Internal Medicine, American University of Beirut, PO-BOX 11-0236, Riad el Solh, Beirut, 11072020, Lebanon.
Paul C CremerDepartment of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, OH, USA.
Wael A JaberDepartment of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, OH, USA.
Torkom GarabedianDepartment of Internal Medicine, Saint Elizabeth's Medical Center, Boston, MA, USA.
Imad ElhajjVascular Medicine Program, American University of Beirut Medical Center, Beirut, Lebanon.
Antoine B AbcheeDivision of Cardiology, Department of Internal Medicine, American University of Beirut, PO-BOX 11-0236, Riad el Solh, Beirut, 11072020, Lebanon.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCoronary artery disease (CAD) accounts for more than half of all cardiovascular events. Stress testing remains the cornerstone for non-invasive assessment of patients with possible or known CAD. Clinical utilization reviews show that most patients presenting for evaluation of stable CAD by stress testing are categorized as low risk prior to the test. Attempts to enhance risk stratification of individuals who are sent for stress testing seem to be more in need today. The present study compares artificial neural networks (ANN)-based prediction models to the other risk models being used in practice (the Diamond-Forrester and the Morise models).

methodsIn our study, we prospectively recruited patients who were 19 years of age or older, and were being evaluated for coronary artery disease with imaging-based stress tests. For ANN, the network architecture employed a systematic method, where the number of neurons is changed incrementally, and bootstrapping was performed to evaluate the accuracy of the models.

resultsWe prospectively enrolled 486 patients. The mean age of patients undergoing stress test was 55.2 ± 11.2 years, 35% were women, and 12% had a positive stress test for ischemic heart disease. When compared to Diamond-Forrester and Morise risk models, the ANN model for predicting ischemia provided higher discriminatory power (DP)(1.61), had a negative predictive value of 98%, Sensitivity 91% [81%-97%], Specificity 65% [60%-79%], positive predictive value 26%, and a potential 59% reduction of non-invasive imaging.

conclusionThe ANN models improved risk stratification when compared to the other risk scores (Diamond-Forrester and Morise) with a 98% negative predictive value and a significant potential reduction in non-invasive imaging tests.

Indexed as

Neural Networks, ComputerAdultAgedCoronary Artery DiseaseExercise TestFemaleHumansMaleMiddle AgedPredictive Value of TestsProspective StudiesRisk AssessmentArtificial neural networks (ANN)Diamond–Forrester scoreMorise scorenuclear stress teststress echocardiography

Identifiers

PMID28224450

What OpenQuestion holds

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Registered trials

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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.