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Gender differences in the diagnostic performance of machine learning coronary CT angiography-derived fractional flow reserve -results from the MACHINE registry
Med Univ South Carolina, SC 29425 USA; Univ Med Ctr Mannheim, Germany.
Med Univ South Carolina, SC 29425 USA; Kerckhoff Heart and Thorax Ctr, Germany.
Med Univ South Carolina, SC 29425 USA; Med Univ South Carolina, SC 29425 USA.
Med Univ South Carolina, SC 29425 USA.
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2019 (English)In: European Journal of Radiology, ISSN 0720-048X, E-ISSN 1872-7727, Vol. 119, article id UNSP 108657Article in journal (Refereed) Published
Abstract [en]

Purpose: This study investigated the impact of gender differences on the diagnostic performance of machine-learning based coronary CT angiography (cCTA)-derived fractional flow reserve (CT-FFR mL ) for the detection of lesion-specific ischemia. Method: Five centers enrolled 351 patients (73.5% male) with 525 vessels in the MACHINE (Machine leArning Based CT angiograpHy derIved FFR: a Multi-ceNtEr) registry. CT-FFRML and invasive FFR amp;lt;= 0.80 were considered hemodynamically significant, whereas cCTA luminal stenosis amp;gt;= 50% was considered obstructive. The diagnostic performance to assess lesion-specific ischemia in both men and women was assessed on a per-vessel basis. Results: In total, 398 vessels in men and 127 vessels in women were included. Compared to invasive FFR, CT-FFRML reached a sensitivity, specificity, positive predictive value, and negative predictive value of 78% (95%CI 72-84), 79% (95%CI 73-84), 75% (95%CI 69-79), and 82% (95%CI: 76-86) in men vs. 75% (95%CI 58-88), 81 (95%CI 72-89), 61% (95%CI 50-72) and 89% (95%CI 82-94) in women, respectively. CT-FFRML showed no statistically significant difference in the area under the receiver-operating characteristic curve (AUC) in men vs. women (AUC: 0.83 [95%CI 0.79-0.87] vs. 0.83 [95%CI 0.75-0.89], p = 0.89). CT-FFRML was not superior to cCTA alone [AUC: 0.83 (95%CI: 0.75-0.89) vs. 0.74 (95%CI: 0.65-0.81), p = 0.12] in women, but showed a statistically significant improvement in men [0.83 (95%CI: 0.79-0.87) vs. 0.76 (95%CI: 0.71-0.80), p = 0.007]. Conclusions: Machine-learning based CT-FFR performs equally in men and women with superior diagnostic performance over cCTA alone for the detection of lesion-specific ischemia.

Place, publisher, year, edition, pages
ELSEVIER IRELAND LTD , 2019. Vol. 119, article id UNSP 108657
Keywords [en]
Coronary artery disease; Machine learning; Spiral computed tomography; Fractional flow reserve
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:liu:diva-161001DOI: 10.1016/j.ejrad.2019.108657ISI: 000487022000002PubMedID: 31521876OAI: oai:DiVA.org:liu-161001DiVA, id: diva2:1367001
Available from: 2019-10-31 Created: 2019-10-31 Last updated: 2019-10-31

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de Geer, JakobPersson, Anders
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Division of Radiological SciencesFaculty of Medicine and Health SciencesDepartment of Radiology in LinköpingCenter for Medical Image Science and Visualization (CMIV)
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