Literature DB >> 34079730

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

Wenjie Zuo1, Rui Zhang1, Mingming Yang1, Zhenjun Ji1, Yanru He1, Yamin Su1, Yangyang Qu1, Zaixiao Tao1, Genshan Ma1.   

Abstract

BACKGROUND: Invasive 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.
METHODS: A 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).
RESULTS: A 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).
CONCLUSIONS: Almost 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). 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.

Entities:  

Keywords:  Fractional flow reserve (FFR); clinical prediction models (CPMs); risk; systematic review

Year:  2021        PMID: 34079730      PMCID: PMC8107300          DOI: 10.21037/qims-20-1274

Source DB:  PubMed          Journal:  Quant Imaging Med Surg        ISSN: 2223-4306


  47 in total

Review 1.  Risk prediction models: II. External validation, model updating, and impact assessment.

Authors:  Karel G M Moons; Andre Pascal Kengne; Diederick E Grobbee; Patrick Royston; Yvonne Vergouwe; Douglas G Altman; Mark Woodward
Journal:  Heart       Date:  2012-03-07       Impact factor: 5.994

2.  Prognosis and prognostic research: Developing a prognostic model.

Authors:  Patrick Royston; Karel G M Moons; Douglas G Altman; Yvonne Vergouwe
Journal:  BMJ       Date:  2009-03-31

3.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.

Authors:  David Moher; Alessandro Liberati; Jennifer Tetzlaff; Douglas G Altman
Journal:  Ann Intern Med       Date:  2009-07-20       Impact factor: 25.391

4.  Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis.

Authors:  G W Sun; T L Shook; G L Kay
Journal:  J Clin Epidemiol       Date:  1996-08       Impact factor: 6.437

Review 5.  Clinical prediction rules. A review and suggested modifications of methodological standards.

Authors:  A Laupacis; N Sekar; I G Stiell
Journal:  JAMA       Date:  1997-02-12       Impact factor: 56.272

6.  When should fractional flow reserve be performed to assess the significance of borderline coronary artery lesions: Derivation of a simplified scoring system.

Authors:  Fadi A Matar; Shayan Falasiri; Charles B Glover; Asma Khaliq; Calvin C Leung; Jad Mroue; George Ebra
Journal:  Int J Cardiol       Date:  2016-08-02       Impact factor: 4.164

7.  Diagnostic Accuracy of Fast Computational Approaches to Derive Fractional Flow Reserve From Diagnostic Coronary Angiography: The International Multicenter FAVOR Pilot Study.

Authors:  Shengxian Tu; Jelmer Westra; Junqing Yang; Clemens von Birgelen; Angela Ferrara; Mariano Pellicano; Holger Nef; Matteo Tebaldi; Yoshinobu Murasato; Alexandra Lansky; Emanuele Barbato; Liefke C van der Heijden; Johan H C Reiber; Niels R Holm; William Wijns
Journal:  JACC Cardiovasc Interv       Date:  2016-10-10       Impact factor: 11.195

Review 8.  The Evolving Future of Instantaneous Wave-Free Ratio and Fractional Flow Reserve.

Authors:  Matthias Götberg; Christopher M Cook; Sayan Sen; Sukhjinder Nijjer; Javier Escaned; Justin E Davies
Journal:  J Am Coll Cardiol       Date:  2017-09-12       Impact factor: 24.094

9.  Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration.

Authors:  Karel G M Moons; Douglas G Altman; Johannes B Reitsma; John P A Ioannidis; Petra Macaskill; Ewout W Steyerberg; Andrew J Vickers; David F Ransohoff; Gary S Collins
Journal:  Ann Intern Med       Date:  2015-01-06       Impact factor: 25.391

10.  Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation.

Authors:  Hyeonyong Hae; Soo-Jin Kang; Won-Jang Kim; So-Yeon Choi; June-Goo Lee; Youngoh Bae; Hyungjoo Cho; Dong Hyun Yang; Joon-Won Kang; Tae-Hwan Lim; Cheol Hyun Lee; Do-Yoon Kang; Pil Hyung Lee; Jung-Min Ahn; Duk-Woo Park; Seung-Whan Lee; Young-Hak Kim; Cheol Whan Lee; Seong-Wook Park; Seung-Jung Park
Journal:  PLoS Med       Date:  2018-11-13       Impact factor: 11.069

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  2 in total

1.  Effect of 320-row CT reconstruction technology on fractional flow reserve derived from coronary CT angiography based on machine learning: single- versus multiple-cardiac periodic images.

Authors:  Ke Shi; Feng-Feng Yang; Nuo Si; Chen-Tao Zhu; Na Li; Xiao-Lin Dong; Yan Guo; Tong Zhang
Journal:  Quant Imaging Med Surg       Date:  2022-06

2.  Diagnostic performance of AccuFFRangio in the functional assessment of coronary stenosis compared with pressure wire-derived fractional flow reserve.

Authors:  Jun Jiang; Lijiang Tang; Changqing Du; Xiaochang Leng; Jingsong He; Yumeng Hu; Liang Dong; Yong Sun; Changling Li; Jianping Xiang; Jian'an Wang
Journal:  Quant Imaging Med Surg       Date:  2022-02
  2 in total

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