Structured reporting platform improves CAD-RADS assessment

Bálint Szilveszter, Márton Kolossváry, Júlia Karády, Ádám L. Jermendy, Mihály Károlyi, Alexisz Panajotu, Zsolt Bagyura, Milán Vecsey-Nagy, Ricardo C. Cury, Jonathon A. Leipsic, B. Merkely, Pál Maurovich-Horvat

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Background Structured reporting in cardiac imaging is strongly encouraged to improve quality through consistency. The Coronary Artery Disease - Reporting and Data System (CAD-RADS) was recently introduced to facilitate interdisciplinary communication of coronary CT angiography (CTA) results. We aimed to assess the agreement between manual and automated CAD-RADS classification using a structured reporting platform. Methods Five readers prospectively interpreted 500 coronary CT angiographies using a structured reporting platform that automatically calculates the CAD-RADS score based on stenosis and plaque parameters manually entered by the reader. In addition, all readers manually assessed CAD-RADS blinded to the automatically derived results, which was used as the reference standard. We evaluated factors influencing reader performance including CAD-RADS training, clinical load, time of the day and level of expertise. Results Total agreement between manual and automated classification was 80.2%. Agreement in stenosis categories was 86.7%, whereas the agreement in modifiers was 95.8% for “N” 96.8% for “S” 95.6% for “V” and 99.4% for “G”. Agreement for V improved after CAD-RADS training (p = 0.047). Time of the day and clinical load did not influence reader performance (p > 0.05 both). Less experienced readers had a higher total agreement as compared to more experienced readers (87.0% vs 78.0%, respectively; p = 0.011). Conclusions Even though automated CAD-RADS classification uses data filled in by the readers, it outperforms manual classification by preventing human errors. Structured reporting platforms with automated calculation of the CAD-RADS score might improve data quality and support standardization of clinical decision making.

Original languageEnglish
Pages (from-to)449-454
Number of pages6
JournalJournal of Cardiovascular Computed Tomography
Volume11
Issue number6
DOIs
Publication statusPublished - Nov 1 2017

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Information Systems
Coronary Artery Disease
Coronary Angiography
Pathologic Constriction
Interdisciplinary Communication

Keywords

  • CAD-RADS
  • Coronary artery disease
  • Coronary CT angiography
  • Reporting and data system
  • Structured reporting

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Cardiology and Cardiovascular Medicine

Cite this

Szilveszter, B., Kolossváry, M., Karády, J., Jermendy, Á. L., Károlyi, M., Panajotu, A., ... Maurovich-Horvat, P. (2017). Structured reporting platform improves CAD-RADS assessment. Journal of Cardiovascular Computed Tomography, 11(6), 449-454. https://doi.org/10.1016/j.jcct.2017.09.008

Structured reporting platform improves CAD-RADS assessment. / Szilveszter, Bálint; Kolossváry, Márton; Karády, Júlia; Jermendy, Ádám L.; Károlyi, Mihály; Panajotu, Alexisz; Bagyura, Zsolt; Vecsey-Nagy, Milán; Cury, Ricardo C.; Leipsic, Jonathon A.; Merkely, B.; Maurovich-Horvat, Pál.

In: Journal of Cardiovascular Computed Tomography, Vol. 11, No. 6, 01.11.2017, p. 449-454.

Research output: Contribution to journalArticle

Szilveszter, B, Kolossváry, M, Karády, J, Jermendy, ÁL, Károlyi, M, Panajotu, A, Bagyura, Z, Vecsey-Nagy, M, Cury, RC, Leipsic, JA, Merkely, B & Maurovich-Horvat, P 2017, 'Structured reporting platform improves CAD-RADS assessment', Journal of Cardiovascular Computed Tomography, vol. 11, no. 6, pp. 449-454. https://doi.org/10.1016/j.jcct.2017.09.008
Szilveszter B, Kolossváry M, Karády J, Jermendy ÁL, Károlyi M, Panajotu A et al. Structured reporting platform improves CAD-RADS assessment. Journal of Cardiovascular Computed Tomography. 2017 Nov 1;11(6):449-454. https://doi.org/10.1016/j.jcct.2017.09.008
Szilveszter, Bálint ; Kolossváry, Márton ; Karády, Júlia ; Jermendy, Ádám L. ; Károlyi, Mihály ; Panajotu, Alexisz ; Bagyura, Zsolt ; Vecsey-Nagy, Milán ; Cury, Ricardo C. ; Leipsic, Jonathon A. ; Merkely, B. ; Maurovich-Horvat, Pál. / Structured reporting platform improves CAD-RADS assessment. In: Journal of Cardiovascular Computed Tomography. 2017 ; Vol. 11, No. 6. pp. 449-454.
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AU - Károlyi, Mihály

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AU - Maurovich-Horvat, Pál

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N2 - Background Structured reporting in cardiac imaging is strongly encouraged to improve quality through consistency. The Coronary Artery Disease - Reporting and Data System (CAD-RADS) was recently introduced to facilitate interdisciplinary communication of coronary CT angiography (CTA) results. We aimed to assess the agreement between manual and automated CAD-RADS classification using a structured reporting platform. Methods Five readers prospectively interpreted 500 coronary CT angiographies using a structured reporting platform that automatically calculates the CAD-RADS score based on stenosis and plaque parameters manually entered by the reader. In addition, all readers manually assessed CAD-RADS blinded to the automatically derived results, which was used as the reference standard. We evaluated factors influencing reader performance including CAD-RADS training, clinical load, time of the day and level of expertise. Results Total agreement between manual and automated classification was 80.2%. Agreement in stenosis categories was 86.7%, whereas the agreement in modifiers was 95.8% for “N” 96.8% for “S” 95.6% for “V” and 99.4% for “G”. Agreement for V improved after CAD-RADS training (p = 0.047). Time of the day and clinical load did not influence reader performance (p > 0.05 both). Less experienced readers had a higher total agreement as compared to more experienced readers (87.0% vs 78.0%, respectively; p = 0.011). Conclusions Even though automated CAD-RADS classification uses data filled in by the readers, it outperforms manual classification by preventing human errors. Structured reporting platforms with automated calculation of the CAD-RADS score might improve data quality and support standardization of clinical decision making.

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