Evidence map›Paper›PMID 38527287›Full record

ArticleAnnals of internal medicine2024

Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs : A Risk Prediction Study.

Jakob Weiss, Vineet K Raghu, Kaavya Paruchuri, Aniket Zinzuwadia, Pradeep Natarajan, Hugo J W L Aerts, Michael T Lu

Erratum issuedOpen access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of internal medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 pooled it
14.1field-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

22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.

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  15. Artificial Intelligence in Coronary Artery Interventions: Preprocedural Planning and Procedural Assistance.Journal of the Society for Cardiovascular Angiography & Interventions · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 2 countries.

Jakob WeissCardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, and Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts, and Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany (J.W.).
Vineet K RaghuCardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, and Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts (V.K.R., M.T.L.).ORCID 0000-0003-3524-3945
Kaavya ParuchuriCardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, and Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts (K.P., P.N.).ORCID 0000-0003-1228-1674
Aniket ZinzuwadiaCardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts (A.Z.).ORCID 0000-0002-8337-8089
Pradeep NatarajanCardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, and Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts (K.P., P.N.).ORCID 0000-0001-8402-7435
Hugo J W L AertsCardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School; and Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, and Department of Radiology and Nuclear Medicine, CARIM and GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.).ORCID 0000-0002-2122-2003
Michael T LuCardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, and Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts (V.K.R., M.T.L.).ORCID 0000-0003-4696-9610
Harvard University · USBroad Institute · USMassachusetts General Hospital · US

Funding

MULTIDISCIPLINARY RESEARCH TRAINING IN CARDIOLOGYT32HL007208 · MASSACHUSETTS GENERAL HOSPITAL · 1985 to 2025
$3.9M
Clonal Hematopoiesis in the Women's Health InitiativeR01HL148565 · FRED HUTCHINSON CANCER CENTER · 2025 to 2025
$727k
Whole genome sequences in ethnically diverse individuals to comprehensively characterize the genetic mechanisms of dyslipidemiasR01HL142711 · MASSACHUSETTS GENERAL HOSPITAL · 2025 to 2025
$641k
Deep learning to assess cardiovascular disease risk from chest imagingK01HL168231 · MASSACHUSETTS GENERAL HOSPITAL · 2025 to 2025
$175k
NHLBI NIH HHS K01 HL168231NHLBI NIH HHS R01 HL142711NHLBI NIH HHS R01 HL148050NHLBI NIH HHS R01 HL148565NHLBI NIH HHS R01 HL151283NHLBI NIH HHS T32 HL007208
6 · The paper itself

Abstract

backgroundGuidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) recommend a risk calculator (ASCVD risk score) to estimate 10-year risk for major adverse cardiovascular events (MACE). Because the necessary inputs are often missing, complementary approaches for opportunistic risk assessment are desirable.

objectiveTo develop and test a deep-learning model (CXR CVD-Risk) that estimates 10-year risk for MACE from a routine chest radiograph (CXR) and compare its performance with that of the traditional ASCVD risk score for implications for statin eligibility.

designRisk prediction study.

settingOutpatients potentially eligible for primary cardiovascular prevention.

participantsThe CXR CVD-Risk model was developed using data from a cancer screening trial. It was externally validated in 8869 outpatients with unknown ASCVD risk because of missing inputs to calculate the ASCVD risk score and in 2132 outpatients with known risk whose ASCVD risk score could be calculated. MEASUREMENTS: 10-year MACE predicted by CXR CVD-Risk versus the ASCVD risk score.

resultsAmong 8869 outpatients with unknown ASCVD risk, those with a risk of 7.5% or higher as predicted by CXR CVD-Risk had higher 10-year risk for MACE after adjustment for risk factors (adjusted hazard ratio [HR], 1.73 [95% CI, 1.47 to 2.03]). In the additional 2132 outpatients with known ASCVD risk, CXR CVD-Risk predicted MACE beyond the traditional ASCVD risk score (adjusted HR, 1.88 [CI, 1.24 to 2.85]). LIMITATION: Retrospective study design using electronic medical records.

conclusionOn the basis of a single CXR, CXR CVD-Risk predicts 10-year MACE beyond the clinical standard and may help identify individuals at high risk whose ASCVD risk score cannot be calculated because of missing data. PRIMARY FUNDING SOURCE: None.

Indexed as

AtherosclerosisCardiovascular DiseasesDeep LearningHeart Disease Risk FactorsHumansRetrospective StudiesRisk AssessmentRisk Factors

Identifiers

PMID38527287
PMCPMC11613935
OpenAlexW4393139437

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.