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Influence of diabetes mellitus on the diagnostic performance of machine learning–based coronary CT angiography–derived fractional flow reserve: a multicenter study

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To examine the diagnostic accuracy of machine learning–based coronary CT angiography–derived fractional flow reserve (FFRCT) in diabetes mellitus (DM) patients.

Methods

In total, 484 patients with suspected or known coronary artery disease from 11 Chinese medical centers were retrospectively analyzed. All patients underwent CCTA, FFRCT, and invasive FFR. The patients were further grouped into mild (25~49 %), moderate (50~69 %), and severe (≥ 70 %) according to CCTA stenosis degree and Agatston score < 400 and Agatston score ≥ 400 groups according to coronary artery calcium severity. Propensity score matching (PSM) was used to match DM (n  = 112) and non-DM (n  = 214) groups. Sensitivity, specificity, accuracy, and area under the curve (AUC) with 95 % confidence interval (CI) were calculated and compared.

Results

Sensitivity, specificity, accuracy, and AUC of FFRCT were 0.79, 0.96, 0.87, and 0.91 in DM patients and 0.82, 0.93, 0.89, and 0.89 in non-DM patients without significant difference (all p > 0.05) on a per-patient level. The accuracies of FFRCT had no significant difference among different coronary stenosis subgroups and between two coronary calcium subgroups (all p > 0.05) in the DM and non-DM groups. After PSM grou**, the accuracies of FFRCT were 0.88 in the DM group and 0.87 in the non-DM group without a statistical difference (p > 0.05).

Conclusions

DM has no negative impact on the diagnostic accuracy of machine learning–based FFRCT.

Key Points

• ML-based FFR CT has a high discriminative accuracy of hemodynamic ischemia, which is not affected by DM.

• FFR CT was superior to the CCTA alone for the detection of ischemia relevance of coronary artery stenosis in both DM and non-DM patients.

• Coronary calcification had no significant effect on the diagnostic accuracy of FFR CT to detect ischemia in DM patients.

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Abbreviations

AS:

Agatston score

CACS:

Coronary artery calcium score

CAD:

Coronary artery disease

CCTA:

Coronary CT angiography

DM:

Diabetes mellitus

FFR:

Fractional flow reserve

FFRCT :

Coronary CT angiography derived fractional flow reserve

ICA:

Invasive coronary angiography

ML:

Machine learning

PSM:

Propensity score matching

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Acknowledgements

We thank our colleagues from multi-centers for data support, Meng Jie Lu from **ling Hospital for statistical advice, Chang Sheng Zhou from **ling Hospital for technical assistance.

Funding

The work was supported by the National Key Research and Development Program of China (2017YFC0113400 for L.J.Z.) and General Project of the Chinese Postdoctoral Science Foundation (2020M673677 for C.X.T.)

Author information

Authors and Affiliations

  1. Department of Diagnostic Radiology, **ling Hospital, the First School of Clinical Medicine, Southern Medical University, Nan**g, 210002, Jiangsu, China

    Yi Xue, Chun **ang Tang & Long Jiang Zhang

  2. Department of Radiology, ****g Hospital, Fourth Military Medical University, **’an, Shanxi, China

    Min Wen Zheng

  3. Department of Radiology, Sheng**g Hospital of China Medical University, Shenyang, 110001, China

    Yang Hou

  4. Department of Diagnostic Radiology, **ling Hospital, Medical School of Nan**g University, Nan**g, 210002, Jiangsu, China

    Fan Zhou, Chun Yu Liu, Chang Sheng Zhou, Guang Ming Lu, Chun **ang Tang & Long Jiang Zhang

  5. Department of Cardiology, **ling Hospital, Medical School of Nan**g University, Nan**g, 210002, Jiangsu, China

    Jian Hua Li

  6. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Bei**g, 100730, China

    Yi Ning Wang & Yan Yi

  7. Institute of Diagnostic and Interventional Radiology, and Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, 200233, China

    Jia Yin Zhang & Meng Meng Yu

  8. Department of Radiology, Jiangsu Taizhou People’s Hospital, Taizhou, 225300, China

    Bo Zhang

  9. Department of Cardiology, Nan**g First Hospital, Nan**g Medical University, Nan**g, 210006, Jiangsu, China

    Dai Min Zhang

  10. Department of Radiology, Bei**g Anzhen Hospital, Capital Medical University, Bei**g, 100029, China

    Lei Xu

  11. Shaoyifu Hospital Affiliated to Medical College of Zhejiang University, Hangzhou, 310016, China

    **u Hua Hu

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  1. Yi Xue
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Corresponding author

Correspondence to Long Jiang Zhang.

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Guarantor

The scientific guarantor of this publication is Long Jiang Zhang, who is from the Department of Diagnostic Radiology, **ling Hospital, Southern Medical University, is deemed to take overall responsibility for all aspects of the study (ethics, consent, data handling and storage, and all other aspects of Good Research Practice).

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Meng Jie Lu kindly provided statistical advice for this manuscript.

One of the authors has significant statistical expertise.

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in

Xu PP, Li JH, Zhou F et al (2020) The influence of image quality on diagnostic performance of a machine learning-based fractional flow reserve derived from coronary CT angiography. Eur Radiol 30:2525-2534

Di Jiang M, Zhang XL, Liu H et al (2020) The effect of coronary calcification on diagnostic performance of machine learning-based CT-FFR: a Chinese multicenter study. Eur Radiol 31:1482-1493

Zhou F, Wang YN, Schoepf UJ, et al (2019) Diagnostic performance of machine learning based CT-FFR in detecting ischemia in myocardial bridging and concomitant proximal atherosclerotic disease. Can J Cardiol 35:1523-1533

Tang CX, Liu CY, Lu MJ, et al (2020) CT FFR for ischemia-specific CAD with a new computational fluid dynamics algorithm: a Chinese multicenter study. JACC Cardiovasc Imaging 13:980-990

Tang CX, Wang YN, Zhou F, et al (2019) Diagnostic performance of fractional flow reserve derived from coronary CT angiography for detection of lesion-specific ischemia: a multi-center study and meta-analysis. Eur J Radiol 116:90-97

Methodology

• retrospective

• diagnostic study

• multicenter study

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Yi Xue and Min Wen Zheng have contributed equally.

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Xue, Y., Zheng, M.W., Hou, Y. et al. Influence of diabetes mellitus on the diagnostic performance of machine learning–based coronary CT angiography–derived fractional flow reserve: a multicenter study. Eur Radiol 32, 3778–3789 (2022). https://doi.org/10.1007/s00330-021-08468-7

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