Evidence map›Paper›PMID 31853543›Full record

Trial reportCardiovascular research2020

Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study.

Frederic Commandeur, Piotr J Slomka, Markus Goeller, Xi Chen, Sebastien Cadet, Aryabod Razipour, Priscilla McElhinney, Heidi Gransar, Stephanie Cantu, Robert J H Miller and 5 more

Registry-linked trialOpen access · bronzeAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Cardiovascular research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06062316 (Early Warning Model of Myocardial Remodeling After Acute Myocardial Infarction Based on Multimodal Feature Structure Technology), which is not on this map. Cited by 77 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
77citing papers in PubMed, 5 pooled it
9.8field-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.

NCT06062316 unknown statusnot on this mapstarted 2022, after this paper: background citation

Early Warning Model of Myocardial Remodeling After Acute Myocardial Infarction Based on Multimodal Feature Structure Technology

TypeobservationalSponsorXuanwu Hospital, BeijingRan2022 to 2024Enrolled4,000ConditionsMyocardial Infarction
3 · Its place in the literature

Who cites it

77 citing papers in PubMed, 5 syntheses or guidelines pooled it, 139 citations in OpenAlex.

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors at 3 institutions in 2 countries.

Frederic CommandeurDepartment of Biomedical Sciences, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA 90048, USA.
Piotr J SlomkaDepartment of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Markus GoellerDepartment of Cardiology, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
Xi ChenDepartment of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Sebastien CadetDepartment of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Aryabod RazipourDepartment of Biomedical Sciences, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA 90048, USA.
Priscilla McElhinneyDepartment of Biomedical Sciences, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA 90048, USA.
Heidi GransarDepartment of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Stephanie CantuDepartment of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Robert J H MillerDepartment of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Alan RozanskiDivision of Cardiology, Mount Sinai St Lukes Hospital, New York, NY, USA.
Stephan AchenbachDepartment of Cardiology, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
Balaji K TamarappooDepartment of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Daniel S BermanDepartment of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Damini DeyDepartment of Biomedical Sciences, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 116 N Robertson Blvd, Los Angeles, CA 90048, USA.
Cedars-Sinai Medical Center · USFriedrich-Alexander-Universität Erlangen-Nürnberg · DESt. Luke's Hospital · US

Funding

Automated quantitative CT imaging of epicardial adipose tissue and risk of cardiac eventsR01HL133616 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI DEY, DAMINI · 2016 to 2019
$2.6M
NHLBI NIH HHS R01 HL133616
6 · The paper itself

Abstract

aimsOur aim was to evaluate the performance of machine learning (ML), integrating clinical parameters with coronary artery calcium (CAC), and automated epicardial adipose tissue (EAT) quantification, for the prediction of long-term risk of myocardial infarction (MI) and cardiac death in asymptomatic subjects. METHODS AND

resultsOur study included 1912 asymptomatic subjects [1117 (58.4%) male, age: 55.8 ± 9.1 years] from the prospective EISNER trial with long-term follow-up after CAC scoring. EAT volume and density were quantified using a fully automated deep learning method. ML extreme gradient boosting was trained using clinical co-variates, plasma lipid panel measurements, risk factors, CAC, aortic calcium, and automated EAT measures, and validated using repeated 10-fold cross validation. During mean follow-up of 14.5 ± 2 years, 76 events of MI and/or cardiac death occurred. ML obtained a significantly higher AUC than atherosclerotic cardiovascular disease (ASCVD) risk and CAC score for predicting events (ML: 0.82; ASCVD: 0.77; CAC: 0.77, P < 0.05 for all). Subjects with a higher ML score (by Youden's index) had high hazard of suffering events (HR: 10.38, P < 0.001); the relationships persisted in multivariable analysis including ASCVD-risk and CAC measures (HR: 2.94, P = 0.005). Age, ASCVD-risk, and CAC were prognostically important for both genders. Systolic blood pressure was more important than cholesterol in women, and the opposite in men.

conclusionsIn this prospective study, machine learning used to integrate clinical and quantitative imaging-based variables significantly improves prediction of MI and cardiac death compared with standard clinical risk assessment. Following further validation, such a personalized paradigm could potentially be used to improve cardiovascular risk assessment.

Indexed as

Computed Tomography AngiographyCoronary AngiographyMachine LearningMultidetector Computed TomographyRadiographic Image Interpretation, Computer-AssistedAdipose TissueAgedCause of DeathCoronary Artery DiseaseDecision Support TechniquesFemaleHumansMaleMiddle AgedMyocardial InfarctionPericardiumCoronary calcium scoringEpicardial adipose tissueMachine learningMyocardial infarction and cardiac death

Identifiers

PMID31853543
PMCPMC7750990
OpenAlexW2995412382

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

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 Socratic graph.