Determinants of Rejection Rate for Coronary CT Angiography Fractional Flow Reserve Analysis

Gianluca Pontone1, Jonathan R Weir-McCall1, Andrea Baggiano1

  • 1From the Department of Cardiovascular Imaging, Centro Cardiologico Monzino, IRCCS, Via C. Parea 4, 20138 Milan, Italy (G.P., A.B., A.D.T., L.F., M.G., G.M., D.A.); Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge, England (J.R.W.); Institute of Cardiovascular Disease, Department of Emergency and Organ Transplantation, University Hospital Policlinico of Bari, Bari, Italy (A.I.G.); Duke University School of Medicine, Durham, NC (M.P., L.H.K.); Department of Cardiology, Stanford University School of Medicine, Stanford, Calif (K.N.); Wakayama Medical University, Wakayama, Japan (T.A.); HeartFlow, Redwood City, Calif (C.R.); Department of Cardiology, Aarhus University Hospital, Aarhus Skejby, Denmark (B.L.N.); Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands (J.B.); William Beaumont Hospital, Royal Oak, Mich (G.L.R., K.C.); Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, Calif (D.B.); Liverpool Heart and Chest Hospital, Liverpool, England (T.F.); and Department of Radiology, University of British Columbia, Vancouver, Canada (J.L.).

Radiology
|July 24, 2019
PubMed

Insights

CT-derived fractional flow reserve (FFRCT) analysis rejection rates were 2.9% in a registry and 8.4% in clinical practice. Lower heart rate and thinner CT sections improve FFRCT success rates.

Area of Science:

  • Cardiovascular imaging
  • Medical diagnostics
  • Interventional cardiology

Background:

  • Coronary artery fractional flow reserve (FFRCT) assesses coronary stenosis using CT angiography.
  • Previous studies indicated 13%-33% of CT angiography scans were unsuitable for FFRCT analysis due to image quality.
  • Motion artifacts are a common cause of poor image quality in coronary CT angiography.

Purpose of the Study:

  • To determine the rejection rate of FFRCT analysis in a clinical setting.
  • To identify factors associated with technically unsuccessful FFRCT calculations.
  • To compare rejection rates between a clinical trial registry and routine clinical practice.

Main Methods:

  • Prospective coronary CT angiography scans from the ADVANCE registry and routine clinical analyses were included.
  • The primary outcome was the FFRCT rejection rate, defined as the inability to perform quantitative analysis.
  • Multiple linear regression was used to assess factors associated with FFRCT rejection.

Main Results:

  • The FFRCT rejection rate was 2.9% in the ADVANCE registry and 8.4% in the clinical cohort.
  • Motion artifacts were the primary reason for rejection in both cohorts (78% and 64%, respectively).
  • Thinner CT section thickness and lower heart rate were independent predictors of successful FFRCT analysis.

Conclusions:

  • Technically unsuccessful CT-derived fractional flow reserve rates were 2.9% (ADVANCE registry) and 8.4% (clinical cohort).
  • Optimizing CT acquisition parameters, such as thinner section thickness and lower heart rate, can improve FFRCT analysis success.
  • These findings highlight the importance of image quality in the successful application of FFRCT.

Related Concept Videos

Rate-Determining Steps03:08

Rate-Determining Steps

Relating Reaction Mechanisms
In a multistep reaction mechanism, one of the elementary steps progresses significantly slower than the others. This slowest step is called the rate-limiting step (or rate-determining step). A reaction cannot proceed faster than its slowest step, and hence, the rate-determining step limits the overall reaction rate.
The concept of rate-determining step can be understood from the analogy of a 4-lane freeway with a short-stretch of traffic-bottleneck caused due to...
36.8K
Determination of Michaelis Constant and Maximum Elimination Rate01:20

Determination of Michaelis Constant and Maximum Elimination Rate

The Michaelis constant (KM) and the theoretical maximum process rate (Vmax) are vital parameters in the Michaelis-Menten equation, central to many biochemical reactions. They provide essential insights into enzyme kinetics and drug metabolism.
These parameters can be estimated by analyzing plasma concentration data post-drug administration. A notable example of this application is phenytoin, a drug with capacity-limited kinetics. It's recommended that phenytoin should be administered at two...
444
Renal Drug Excretion: Effect of Urine pH, Flow Rate, and Drug pKa01:22

Renal Drug Excretion: Effect of Urine pH, Flow Rate, and Drug pKa

The pH of urine, the drug's pKa, and the urine flow rate are vital parameters for drug reabsorption and excretion. Urinary pH varies between 4.6 and 8.0 and is influenced by diet, drug intake, and the patient's pathophysiology. It affects a drug's ionization state and reabsorption. For instance, carbohydrate-rich food produces alkaline urine promoting drug excretion, while proteins and certain medications like ascorbic acid lead to acidic urine enhancing reabsorption.
The pKa of a...
545
Reaction Rate02:53

Reaction Rate

The rate of reaction is the change in the amount of a reactant or product per unit time. Reaction rates are therefore determined by measuring the time dependence of some property that can be related to reactant or product amounts. Rates of reactions that consume or produce gaseous substances, for example, are conveniently determined by measuring changes in volume or pressure.
The mathematical representation of the change in the concentration of reactants and products, over time, is the rate...
62.4K
Speciation Rates01:07

Speciation Rates

Overview
22.6K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.6K