Deep learning to predict long-term mortality from plain chest X-ray in patients referred for suspected coronary artery disease

Journal of Thoracic Disease, vol. 16, pp. 4914–4923

Abstract

Background: The hypothesis that a deep learning (DL) model can produce long-term prognostic information from chest X-ray (CXR) has already been confirmed within cancer screening programs. We summarize our experience with DL prediction of long-term mortality, from plain CXR, in patients referred for angina and coronary angiography. Methods: Data of patients referred to an Italian academic hospital were analyzed retrospectively. We designed a deep convolutional neural network (DCNN) that, from CXR, could predict long-term mortality. External validation was performed on patients referred to a Dutch academic hospital. Results: A total of 6,031 were used for model training (71%; n=4,259) and fine-tuning/validation (10%; n=602). Internal validation was performed with the remaining patients (19%; n=1,170). Patients' stratification followed the DL-CXR risk score quartiles division. Median follow-up was 6.1 years [interquartile range (IQR), 3.3-8.7 years]. We observed an increment in estimated mortality with the increase of DL-CXR risk score (low-risk 5%, moderate 17%, high 29%, very high 46%; P<0.001). The DL-CXR risk score predicted median follow-up outcome with an area under the curve (AUC) of 0.793 [95% confidence interval (CI): 0.759-0.827, sensitivity 78%, specificity 68%]. Prediction was better than that achieved using coronary angiography findings (AUC: 0.569, 95% CI: 0.52-0.61, P<0.001) and age (AUC: 0.735, 95% CI: 0.69-0.77, P<0.004). At Cox regression, the DL-CXR risk score predicted follow-up mortality (P<0.005, hazard ratio: 3.30, 95% CI: 2.35-4.64). External validation confirmed the DL-CXR risk score performance (AUC: 0.71, 95% CI: 0.49-0.92; sensitivity 0.838; specificity 0.338). Conclusions: In patients referred for coronary angiogram because of angina, the DL-CXR risk score could be used to stratify mortality risk and predict long-term outcome better than age and coronary artery disease status.

Authors 12

1. Giuseppe D'Ancona corresponding

   Vivantes Klinikum

   Affiliation as printed

   Department of Cardiology and Cardiovascular Clinical Research Unit, Vivantes Klinikum Urban and Neukölln, Berlin, Germany;

   Department of Cardiology and Cardiovascular Clinical Research Unit, Vivantes Klinikum Urban and Neukölln, Berlin, Germany

2. Mattia Savardi

   University of Brescia

   Affiliation as printed

   Department of Information Engineering, University of Brescia, Brescia, Italy

   Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, Brescia, Italy

3. Mauro Massussi

   Azienda Socio Sanitaria Territoriale degli Spedali Civili di Brescia

   Affiliation as printed

   Cardiac Catheterization Laboratory, Department of Cardiothoracic, ASST Spedali Civili, Brescia, Italy

4. Viktor van der Valk Aachen Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands

5. Roderick W.C. Scherptong Aachen Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands

6. Alberto Signoroni

   University of Brescia

   Affiliation as printed

   Department of Information Engineering, University of Brescia, Brescia, Italy

   Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, Brescia, Italy

7. Davide Farina

   Azienda Socio Sanitaria Territoriale degli Spedali Civili di Brescia · University of Brescia

   Affiliation as printed

   Radiology 2, ASST Spedali Civili and Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy

8. Monica Murero

   University of Naples Federico II

   Affiliation as printed

   Department of Excellence in Social Sciences, University Federico II, Neaples, Italy

9. Hüseyin İnce

   Vivantes Klinikum

   Affiliation as printed

   Department of Cardiology and Cardiovascular Clinical Research Unit, Vivantes Klinikum Urban and Neukölln, Berlin, Germany

10. Stefano Benussi

    Azienda Socio Sanitaria Territoriale degli Spedali Civili di Brescia · University of Brescia

    Affiliation as printed

    Department of Cardiac Surgery, Spedali Civili Brescia and University of Brescia, Brescia, Italy

11. Salvatore Curello

    Azienda Socio Sanitaria Territoriale degli Spedali Civili di Brescia

    Affiliation as printed

    Cardiac Catheterization Laboratory, Department of Cardiothoracic, ASST Spedali Civili, Brescia, Italy

12. Fatih Arslan Aachen Department of Cardiology, Leiden University Medical Center

    Leiden University Medical Center

    Affiliation as printed

    Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands