Evidence map›Paper›PMID 34079730›Full record

ArticleQuantitative imaging in medicine and surgery2021

Clinical prediction models of fractional flow reserve: an exploration of the current evidence and appraisal of model performance.

Wenjie Zuo, Rui Zhang, Mingming Yang, Zhenjun Ji, Yanru He, Yamin Su, Yangyang Qu, Zaixiao Tao, Genshan Ma

Abstract read
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Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Wenjie ZuoDepartment of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
Rui ZhangDepartment of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
Mingming YangDepartment of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
Zhenjun JiDepartment of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
Yanru HeDepartment of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
Yamin SuDepartment of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
Yangyang QuDepartment of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
Zaixiao TaoDepartment of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
Genshan MaDepartment of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInvasive fractional flow reserve (FFR) is a standard indicator of coronary stenoses' hemodynamic severity. Clinical prediction models (CPMs) may help differentiate ischemic from non-ischemic lesions without using a pressure wire but by integrating related variables. This approach differs from that of physics-based models. However, it is not yet known which CPMs are the most reliable at detecting hemodynamic significance.

methodsA systematic review was performed of relevant publications that developed or validated any FFR CPMs from inception to April 2019 in the PubMed, EMBASE, and Cochrane Library databases by two independent authors. The risk of bias and applicability were assessed using the prediction model risk of the bias assessment tool (PROBAST).

resultsA total of 11 unique CPMs and 5 subsequent external validation studies were identified. The prevalence of hemodynamically significant lesions (FFR ≤0.80) across the studies had a median of 37.1% (range: 20.7-68.0%). Lesion length, percent diameter stenosis, and minimal lumen diameter were the three most frequently used variables in the CPMs. Of the 11 FFR CPMs, 9 (82%) exhibited strong discrimination [area under the curve (AUC) >0.75], and 5 (45%) had been subject to external validation; however, calibration was only available for 3 models (27%). There was a high degree of applicability; however, none of the studies was assessed as having a low risk of bias. A CPM was identified that had undergone rigorous validation and calibration: the DILEMMA score (three validations; median AUC, 0.83).

conclusionsAlmost half of the existing FFR CPMs had been externally validated. Due to their good discrimination abilities, these FFR CPMs are useful tools that could reduce the need for invasive hemodynamic measurements. Future research that adheres to methodological guidelines should be undertaken to develop high-quality models in this setting. (PROSPERO registration number: CRD42019125011).

Indexed as

clinical prediction models (CPMs)Fractional flow reserve (FFR)risksystematic review

Identifiers

PMID34079730
PMCPMC8107300

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