Evidence map›Paper›PMID 32929641›Full record

ArticleEuropean radiology2021

The effect of coronary calcification on diagnostic performance of machine learning-based CT-FFR: a Chinese multicenter study.

Meng Di Jiang, Xiao Lei Zhang, Hui Liu, Chun Xiang Tang, Jian Hua Li, Yi Ning Wang, Peng Peng Xu, Chang Sheng Zhou, Fan Zhou, Meng Jie Lu and 13 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
2.7field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors at 11 institutions in 1 country.

Meng Di JiangDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Xiao Lei ZhangDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Hui LiuDepartment of Radiology, Guangdong General Hospital, Guangzhou, 510080, China.
Chun Xiang TangDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Jian Hua LiDepartment of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Yi Ning WangDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Peng Peng XuDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Chang Sheng ZhouDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Fan ZhouDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Meng Jie LuDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Jia Yin ZhangInstitute of Diagnostic and Interventional Radiology and Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China.
Meng Meng YuInstitute of Diagnostic and Interventional Radiology and Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China.
Yang HouDepartment of Radiology, Shengjing Hospital of China Medical University, Shenyang, 110001, China.
Min Wen ZhengDepartment of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shanxi, China.
Bo ZhangDepartment of Radiology, Jiangsu Taizhou People's Hospital, Taizhou, 225300, China.
Dai Min ZhangDepartment of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, Jiangsu, China.
Yan YiInstitute of Diagnostic and Interventional Radiology and Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China.
Lei XuDepartment of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 10029, China.
Xiu Hua HuDepartment of Radiology, Shaoyifu Hospital Affiliated to Medical College of Zhejiang University, Hangzhou, 310016, China.
Jian YangDepartment of Radiology, the First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, 710061, China.
Guang Ming LuDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Qian Qian NiDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China. nqqnjumed@hotmail.com.
Long Jiang ZhangDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China. kevinzhlj@163.com.ORCID http://orcid.org/0000-0002-6664-7224
Nanjing General Hospital of Nanjing Military Command · CNShanghai Jiao Tong University · CNCapital Medical University · CNChina Medical University · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNFirst Affiliated Hospital of Xi'an Jiaotong University · CNGuangdong General Hospital · CNHangzhou Medical College · CNNanjing Medical University · CNNantong University · CNXijing Hospital · CN

Funding

Key Program of the National Natural Science Foundation of China 81830057National Key Scientific Instrument and Equipment Development Projects of China (CN) 2017YFC0113400
6 · The paper itself

Abstract

objectiveTo investigate the effect of coronary calcification morphology and severity on the diagnostic performance of machine learning (ML)-based coronary CT angiography (CCTA)-derived fractional flow reserve (CT-FFR) with FFR as a reference standard.

methodsA total of 442 patients (61.2 ± 9.1 years, 70% men) with 544 vessels who underwent CCTA, ML-based CT-FFR, and invasive FFR from China multicenter CT-FFR study were enrolled. The effect of calcification arc, calcification remodeling index (CRI), and Agatston score (AS) on the diagnostic performance of CT-FFR was investigated. CT-FFR ≤ 0.80 and lumen reduction ≥ 50% determined by CCTA were identified as vessel-specific ischemia with invasive FFR as a reference standard.

resultsCompared with invasive FFR, ML-based CT-FFR yielded an overall sensitivity of 0.84, specificity of 0.94, and accuracy of 0.90 in a total of 344 calcification lesions. There was no statistical difference in diagnostic accuracy, sensitivity, or specificity of CT-FFR across different calcification arc, CRI, or AS levels. CT-FFR exhibited improved discrimination of ischemia compared with CCTA alone in lesions with mild-to-moderate calcification (AUC, 0.89 vs. 0.69, p < 0.001) and lesions with CRI ≥ 1 (AUC, 0.89 vs. 0.71, p < 0.001). The diagnostic accuracy and specificity of CT-FFR were higher than CCTA alone in patients and vessels with mid (100 to 299) or high (≥ 300) AS.

conclusionCoronary calcification morphology and severity did not influence diagnostic performance of CT-FFR in ischemia detection, and CT-FFR showed marked improved discrimination of ischemia compared with CCTA alone in the setting of calcification. KEY POINTS: • CT-FFR provides superior diagnostic performance than CCTA alone regardless of coronary calcification. • No significant differences in the diagnostic performance of CT-FFR were observed in coronary arteries with different coronary calcification arcs and calcified remodeling indexes. • No significant differences in the diagnostic accuracy of CT-FFR were observed in coronary arteries with different coronary calcification score levels.

Indexed as

Coronary Artery DiseaseCoronary StenosisFractional Flow Reserve, MyocardialChinaComputed Tomography AngiographyCoronary AngiographyFemaleHumansMachine LearningMalePredictive Value of TestsSeverity of Illness IndexTomography, X-Ray ComputedCalciumComputed tomography angiographyCoronary diseaseData accuracyIschemia

Identifiers

PMID32929641
OpenAlexW3084846387

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.