• Ann. Intern. Med. · Apr 2024

    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, and Michael T Lu.
    • Cardiovascular 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.).
    • Ann. Intern. Med. 2024 Apr 1; 177 (4): 409417409-417.

    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.Measurements10-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]).LimitationRetrospective 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 SourceNone.

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