(387 days)
Not Found
No
The description focuses on computational fluid dynamics and mathematical modeling, not AI/ML algorithms.
No
The device is a diagnostic tool that aids in the evaluation and assessment of coronary artery disease, rather than providing therapy. It provides functional evaluation and analysis of previously acquired CT data to support clinicians in their assessment.
Yes
The "Intended Use / Indications for Use" section states that the device is "intended to support the functional evaluation of coronary artery disease" and to "aid in the evaluation and assessment of coronary arteries." It also specifically mentions "diagnostic performance" and "diagnosis of ischemia" in the performance studies. These phrases indicate that the device is used to diagnose a medical condition.
Yes
The device is described as "post-processing software" that analyzes previously acquired CT data and provides results electronically. While it relies on input from a CT scanner (hardware), the device itself is the software performing the analysis and generating the report. The processing is explicitly stated to be housed at HeartFlow, Inc., indicating it's a software service rather than a physical device provided to the user.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs analyze biological samples: IVDs are designed to examine specimens taken from the human body, such as blood, urine, tissue, etc., to provide information about a person's health.
- This device analyzes medical images: The HeartFlow FFRcT software analyzes previously acquired Computed Tomography (CT) DICOM data, which are medical images, not biological samples.
- The output is derived from image analysis and simulation: The FFRcT value is a mathematically derived quantity computed from a 3D computer model generated from the CT images and simulated blood flow. This is distinct from analyzing the chemical or biological properties of a sample.
Therefore, while this device is a medical device used for diagnosis, it falls under the category of medical image analysis software rather than an In Vitro Diagnostic.
N/A
Intended Use / Indications for Use
HeartFlow FFRcT is a post-processing software for the clinical quantitative and qualitative analysis of previously acquired Computed Tomography (CT) DICOM data for clinically stable symptomatic patients with coronary artery disease. It provides FFRcT, a mathematically derived quantity, computed from simulated pressure, velocity and blood flow information obtained from a 3D computer model generated from static coronary CT images. FFRcT analysis is intended to support the functional evaluation of coronary artery disease.
The results of this analysis are provided to support qualified clinicians to aid in the evaluation and assessment of coronary arteries. The results of HeartFlow FFR-c are intended to be used by qualified clinicians in conjunction with the patient's clinical history, symptoms, and other diagnostic tests, as well as the clinician's professional judgment.
The device is only for prescription use.
Product codes
PJA
Device Description
FFR v1.4 is post-processing image analysis software developed for the clinical quantitative and qualitative analysis of previously physician-acquired DICOM-compliant cardiac CT images and data, to assess the anatomy and function of the coronary arteries. The software displays the resulting coronary anatomy combined with functional information using graphics and text, including a computed and derived quantification of blood flow. termed FFR - to aid the clinician in the assessment of coronary artery disease.
The HeartFlow FFR cr software is housed at Heart Flow, Inc. The health care provider electronically sends the patient's CT scan data to HeartFlow. Inc. where a 3D computer model of the coronary arteries is developed and simulates blood flow in the models using computational fluid dynamics. A resulting report is electronically sent to the physician with the estimated fractional flow reserve (FFR) values (called FFRct values) displayed as color images of the patient's heart (Figure 1) and an associated color interpretation table (Table 1)Table 1: Error from the HFNXT Study Population. Not indicative of all patient populations. Please refer to the summary of clinical data to determine the population in which the FFRcm technology has been validated. indicating the error associated with each measurement range in the HFNXT clinical study.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Not Found
Input Imaging Modality
Computed Tomography (CT)
Anatomical Site
coronary arteries / coronary vascular system / heart
Indicated Patient Age Range
Adult subjects
Intended User / Care Setting
Qualified clinicians; prescription use only. HeartFlow, Inc. performs the analysis and electronically sends the results to the physician.
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
DeFACTO Study:
- Sample Size: 252 stable patients
- Data Source: 17 centers in 5 countries; CT, invasive coronary angiography (ICA), FFR, and FFRcT data collected between October 2010 and October 2011.
- Annotation Protocol: All CT, FFR, and angiographic data were interpreted in a blinded fashion by independent core laboratories. Ischemia was defined by an FFR or FFRcm 50% stenosis severity for site-read cCTA.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
DeFACTO Study:
- Study Type: Prospective, international, multicenter study.
- Sample Size: 252 patients.
- Key Results: Primary endpoint was improvement in per-patient diagnostic accuracy (sensitivity and specificity) such that the lower boundary of the one-sided 95% confidence interval exceeded 70%. Although the specific percentage results were not listed here, it stated that this study provided "supportive data" but was "not central to the review of FFR . v. 1.4" as it assessed a previous version.
HeartFlowNXT (HFNXT) Study:
- Study Type: Prospective, multicenter, nonrandomized study.
- Sample Size: 254 adult subjects (ITD population).
- Standalone Performance: Tested FFRcT as compared to cCTA alone for non-invasive determination of hemodynamically significant coronary lesions using direct measurement of FFR (≤0.80) during cardiac catheterization as the reference standard.
- Key Results:
- Per-Vessel Performance (FFRcT vs. Invasive FFR ≤ 0.80):
- Sensitivity: 83.5% (lower one-sided 95% Cl of 75.3%) - MET (above target of 65%)
- Specificity: 85.8% (lower one-sided 95% CI of 81.5%) - MET (above target of 55%)
- Per-Subject Diagnostic Performance (FFRcT vs. Site-Read cCTA with FFR ≤ 0.80 as Reference):
- FFRcT demonstrated superior diagnostic ability compared to site-read cCTA (p 50%:**
- Diagnostic Accuracy: 52.8% (46.6%-58.8% Wilson CI)
- Sensitivity: 93.8% (86.2%-97.3% Wilson CI)
- Specificity: 33.9% (27.3%-41.2% Wilson CI)
- PPV: 39.5% (32.8%-46.6% Wilson CI)
- NPV: 92.2% (83.0%-96.6% Wilson CI)
- FFRcT demonstrated superior diagnostic ability compared to site-read cCTA (p 50%:**
- Bland-Altman Plot: FFRcT values were not found to be precisely correlated with the FFR value across the range of measurement.
- Per-Vessel Performance (FFRcT vs. Invasive FFR ≤ 0.80):
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
HFNXT Study - Primary Endpoint Results: Per-Vessel Sensitivity of FFRCT Intent to Diagnose Population
- Sensitivity: 83.5% (lower one-sided 95% CONFIDENCE BOUND: 75.3%)
- Specificity: 85.8% (lower one-sided 95% CONFIDENCE BOUND: 81.5%)
HFNXT Study - Per-Subject Diagnostic Performance Analysis with FFR ≤ 0.80 as the Reference Standard. Intent to Diagnose Population.
-
FFRCT ≤ 0.80:
- Diagnostic Accuracy: 81.1% (95% Wilson CI: 95.8%-85.4%)
- Sensitivity: 86.3% (95% Wilson CI: 77.0%-92.1%)
- Specificity: 78.7% (95% Wilson CI: 72.1%-84.2%)
- PPV: 65.1% (95% Wilson CI: 55.6%-73.5%)
- NPV: 92.6% (95% Wilson CI: 87.2%-95.8%)
-
SITE-READ cCTA > 50%:
- Diagnostic Accuracy: 52.8% (95% Wilson CI: 46.6%-58.8%)
- Sensitivity: 93.8% (95% Wilson CI: 86.2%-97.3%)
- Specificity: 33.9% (95% Wilson CI: 27.3%-41.2%)
- PPV: 39.5% (95% Wilson CI: 32.8%-46.6%)
- NPV: 92.2% (95% Wilson CI: 83.0%-96.6%)
Predicate Device(s):
Not Found
Reference Device(s):
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 870.1415 Coronary vascular physiologic simulation software device.
(a)
Identification. A coronary vascular physiologic simulation software device is a prescription device that provides simulated functional assessment of blood flow in the coronary vascular system using data extracted from medical device imaging to solve algorithms and yield simulated metrics of physiologic information (e.g., blood flow, coronary flow reserve, fractional flow reserve, myocardial perfusion). A coronary vascular physiologic simulation software device is intended to generate results for use and review by a qualified clinician.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Adequate software verification and validation based on comprehensive hazard analysis, with identification of appropriate mitigations, must be performed, including:
(i) Full characterization of the technical parameters of the software, including:
(A) Any proprietary algorithm(s) used to model the vascular anatomy; and
(B) Adequate description of the expected impact of all applicable image acquisition hardware features and characteristics on performance and any associated minimum specifications;
(ii) Adequate consideration of privacy and security issues in the system design; and
(iii) Adequate mitigation of the impact of failure of any subsystem components (
e.g., signal detection and analysis, data storage, system communications and cybersecurity) with respect to incorrect patient reports and operator failures.(2) Adequate non-clinical performance testing must be provided to demonstrate the validity of computational modeling methods for flow measurement; and
(3) Clinical data supporting the proposed intended use must be provided, including the following:
(i) Output measure(s) must be compared to a clinically acceptable method and must adequately represent the simulated measure(s) the device provides in an accurate and reproducible manner;
(ii) Clinical utility of the device measurement accuracy must be demonstrated by comparison to that of other available diagnostic tests (
e.g., from literature analysis);(iii) Statistical performance of the device within clinical risk strata (
e.g., age, relevant comorbidities, disease stability) must be reported;(iv) The dataset must be adequately representative of the intended use population for the device (
e.g., patients, range of vessel sizes, imaging device models). Any selection criteria or limitations of the samples must be fully described and justified;(v) Statistical methods must consider the predefined endpoints:
(A) Estimates of probabilities of incorrect results must be provided for each endpoint,
(B) Where multiple samples from the same patient are used, statistical analysis must not assume statistical independence without adequate justification, and
(C) The report must provide appropriate confidence intervals for each performance metric;
(vi) Sensitivity and specificity must be characterized across the range of available measurements;
(vii) Agreement of the simulated measure(s) with clinically acceptable measure(s) must be assessed across the full range of measurements;
(viii) Comparison of the measurement performance must be provided across the range of intended image acquisition hardware; and
(ix) If the device uses a cutoff threshold or operates across a spectrum of disease, it must be established prior to validation, and it must be justified as to how it was determined and clinically validated;
(4) Adequate validation must be performed and controls implemented to characterize and ensure consistency (
i.e., repeatability and reproducibility) of measurement outputs:(i) Acceptable incoming image quality control measures and the resulting image rejection rate for the clinical data must be specified, and
(ii) Data must be provided within the clinical validation study or using equivalent datasets demonstrating the consistency (
i.e., repeatability and reproducibility) of the output that is representative of the range of data quality likely to be encountered in the intended use population and relevant use conditions in the intended use environment;(A) Testing must be performed using multiple operators meeting planned qualification criteria and using the procedure that will be implemented in the production use of the device, and
(B) The factors (
e.g., medical imaging dataset, operator) must be identified regarding which were held constant and which were varied during the evaluation, and a description must be provided for the computations and statistical analyses used to evaluate the data;(5) Human factors evaluation and validation must be provided to demonstrate adequate performance of the user interface to allow for users to accurately measure intended parameters, particularly where parameter settings that have impact on measurements require significant user intervention; and
(6) Device labeling must be provided that adequately describes the following:
(i) The device's intended use, including the type of imaging data used, what the device measures and outputs to the user, whether the measure is qualitative or quantitative, the clinical indications for which it is to be used, and the specific population for which the device use is intended;
(ii) Appropriate warnings specifying the intended patient population, identifying anatomy and image acquisition factors that may impact measurement results, and providing cautionary guidance for interpretation of the provided measurements;
(iii) Key assumptions made in the calculation and determination of simulated measurements;
(iv) The measurement performance of the device for all presented parameters, with appropriate confidence intervals, and the supporting evidence for this performance. Per-vessel clinical performance, including where applicable localized performance according to vessel and segment, must be included as well as a characterization of the measurement error across the expected range of measurement for key parameters based on the clinical data;
(v) A detailed description of the patients studied in the clinical validation (
e.g., age, gender, race or ethnicity, clinical stability, current treatment regimen) as well as procedural details of the clinical study (e.g., scanner representation, calcium scores, use of beta-blockers or nitrates); and(vi) Where significant human interface is necessary for accurate analysis, adequately detailed description of the analysis procedure using the device and any data features that could affect accuracy of results.
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DE NOVO CLASSIFICATION REQUEST FOR FFRCT V. 1.4
REGULATORY INFORMATION
FDA identifies this generic type of device as:
Coronary Physiologic Simulation Software Device - A coronary vascular physiologic simulation software device is a prescription device that provides simulated functional assessment of blood flow in the coronary vascular system using data extracted from medical device imaging to solve algorithms and yield simulated metrics of physiologic information (e.g., blood flow, coronary flow reserve, fractional flow reserve, myocardial perfusion). A coronary vascular physiologic simulation software device is intended to generate results for use and review by a qualified clinician.
NEW REGULATION NUMBER: 870.1415
CLASSIFICATION: II
PRODUCT CODE: PJA
BACKGROUND
DEVICE NAME: FFRCT V. 1.4
SUBMISSION NUMBER: DEN130045
DATE OF DE NOVO: November 6, 2013
CONTACT: HeartFlow, Inc. Mr. Dustin Michaels Vice President Clinical, Quality & Regulatory 1400 Seaport Boulevard, Building B Redwood Citv. CA 94063
REQUESTER'S RECOMMENDED CLASSIFICATION: II
INDICATIONS FOR USE
HeartFlow FFRcT is a post-processing software for the clinical quantitative and qualitative analysis of previously acquired Computed Tomography (CT) DICOM data for clinically stable symptomatic patients with coronary artery disease. It provides FFRcT, a mathematically derived quantity, computed from simulated pressure, velocity and blood flow information obtained from a 3D computer model generated from static coronary CT
1 Digital Imaging and Communications in Medicine (standard for the communication and management of medical imaging information and related data)
1
images. FFRcT analysis is intended to support the functional evaluation of coronary artery disease.
The results of this analysis are provided to support qualified clinicians to aid in the evaluation and assessment of coronary arteries. The results of HeartFlow FFR-c are intended to be used by qualified clinicians in conjunction with the patient's clinical history, symptoms, and other diagnostic tests, as well as the clinician's professional judgment.
The device is only for prescription use.
LIMITATIONS
The safety and effectiveness of the FFRCT analysis has not been evaluated for the following populations:
- Suspicion of acute coronary syndrome (where acute myocardial infarction or unstable angina have not been ruled out)
-
- Recent prior myocardial infarction within 30 days
-
- Complex congenital heart disease
-
- Prior coronary artery bypass graft (CABG) surgery
-
- Patients with a Body Mass Index >35
- Patients who require emergent procedures or have any evidence of ongoing or active clinical instability, including acute chest pain (sudden onset), cardiogenic shock, unstable blood pressure with systolic blood pressure 2 Min, J.K., et. al., Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA, 2012. 308(12): p. 1237-45.
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The study was conducted at 11 sites in 8 countries in Canada, Europe and Asia from September 2012 to August 2013, with 276 subjects enrolled. A total of 254 adult subjects with known or suspected coronary artery disease who were scheduled for clinicated invasive coronary angiography comprised the intention-to-diagnose (ITD) population. Subjects had an overall mean age of 63.7 years and 63.8% were men. Similar to many interventional cardiology trials, few minority patients were enrolled in HFNXT. Only 1.2% of patients were Hispanic and there were no Black patients. Because higher calcium scores could be expected among these patients due to higher incidence of high body mass index (BMI), hypertension and diabetes, FFR-T performance across calcium scores was compared that in US patients in the DeFACTO study (comprised with 11% Hispanic and 4% Black patients). Performance was maintained at all levels of calcium scores below 1000. No significant differences in diagnostic accuracy were observed in subjects with or without high BMI, hypertension, or diabetes.
A total of 22.8% of patients in the trial had diabetes mellitus, 68.5% had hypertension, 78.7% had hyperlipidemia, 57.1% were current or former smokers. Also, 77.6% presented with angina in the 30 days prior to enrollment; 77.7% of subjects with angina had stable angina and 22.3% had unstable angina. Only 2% had documented prior history of myocardial infarction and no patients had renal dysfunction, defined as creatinine >1.5 mg/dL. The mean body mass index for enrolled subjects was 25.6 ± 3.7 kg/m2. Left ventricular ejection was reported for 76% of the enrolled subjects with a mean value of 61.8%. The time from the cCTA scan to the ICA procedure was between 1 to 30 days in 87% of the ITD patients with a mean of 18.1 days. Sublingual or intravenous nitrates were administered in 99.6% of subjects undergoing coronary artery CT scanning. In 78% of the subjects beta blockers were administered to reduce heart rate prior to scan. The mean calcium score for ITD subjects was 302 (±468) Agatston units. A calcium score was reported for 84.3% of subjects, and of these, 25.7% had a calcium score > 400 Agatston units.
Direct comparison of invasive FFR and FFR - was performed in 484 vessels. At least one invasive FFR measurement was collected in all ITD subjects with an average of 1.9 measurements per subject. All invasive FFR data was reviewed by an independent FFR/QCA core laboratory.
The primary endpoint was the per-vessel sensitivity and specificity of FFR-T to detect hemodynamically significant obstruction when FFR was used as the reference standard. The prespecified target goals identified by the sponsor for sensitivity and specificity were 65%. respectively. As this study was conducted OUS, these target goals were not agreed upon by the FDA.
Primary endpoint success required both sensitivity and specificity hypotheses to be met. The pervessel sensitivity of FFR-r in the ITD population was 83.5% with a lower one-sided 95% Cl of 75.3%. The per-vessel specificity of FFR in the ITD population was 85.8% with a lower onesided 95% CI of 81.5%. Both of the lower one-sided confidence limits for sensitivity and specificity were significantly above the pre-specified target goals of 65% and 55%, respectively. and were considered acceptable. The results are shown in Table 2 below.
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| | ESTIMATE, % | LOWER ONE-SIDED 95%
CONFIDENCE BOUND | TARGET RATE | MET¹
NOT MET |
|---------------------------------------------------------------------------------------------------------------|-------------|-----------------------------------------|-------------|-----------------|
| Sensitivity | 83.5% | 75.3% | 65% | MET |
| Specificity | 85.8% | 81.5% | 55% | MET |
| FFR is used as the reference standard
FFRCT: Diseased if hemodynamically-significant obstruction is ≤ 0.80 | | | | |
| FFR: Diseased if hemodynamically-significant obstruction is ≤ 0.80
¹MET if 95% LCL > Target Rate | | | | |
Table 2: Primary Endpoint Results: Per-Vessel Sensitivity of FFRCT Intent to Diagnose Donulation
Per-subject FFRcr specificity compared to site-read cCTA demonstrated superior diagnostic ability (p 50% stenosis severity for site-read cCTA. Diagnostic performance of FFR-T compared to site-read cCTA on the subject level is shown in Table 3 below.
| Table 3: Per-Subject Diagnostic Performance Analysis with FFR ≤ 0.80 as the Reference Standard. Intent t |
|---|
| Diagnose Population. |
| | FFRCT ≤ 0.80
ESTIMATE % (95% Wilson CI) | SITE-READ cCTA > 50%
ESTIMATE % (95% Wilson CI) |
|---------------------|--------------------------------------------|----------------------------------------------------|
| Diagnostic Accuracy | 81.1% (95.8%-85.4%) | 52.8% (46.6%-58.8%) |
| Sensitivity | 86.3% (77.0%-92.1%) | 93.8% (86.2%-97.3%) |
| Specificity | 78.7% (72.1%-84.2%) | 33.9% (27.3%-41.2%) |
| PPV | 65.1% (55.6%-73.5%) | 39.5% (32.8%-46.6%) |
| NPV | 92.6% (87.2%-95.8%) | 92.2% (83.0%-96.6%) |
FFR T values were not found to be precisely correlated with the FFR value across the range of measurement. The Bland-Altman plot of FFR vs. FFRCT for all measurements is shown in Figure 2.
The HeartFlowNXT study demonstrated good diagnostic performance for FFRcT when all vessels were included, irrespective of size, location, or territory, and across a range of cCTA image quality measures.
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Figure 2: FFRCT Bland-Altman Plot
Image /page/10/Figure/1 description: This image is a scatter plot that compares the method difference versus the method mean. The x-axis is labeled "Method Mean" and ranges from 0.0 to 1.0. The y-axis is labeled "Method Difference" and ranges from -0.5 to 0.5. There are three horizontal lines on the plot, representing the mean difference, method difference = 0, mean + 1.96SD, and mean - 1.96SD.
LABELING
The device is labeled for clinically stable symptomatic patients with coronary artery disease that have a previously-collected DICOM CT scan. Several product warnings are included in the labeling that carefully specify the intended patient population, identify anatomy and image acquisition factors that may impact FFRcm results, and provide cautionary guidance for interpretation of the FFRcT. These warnings were found to be appropriate.
The labeling also provided a detailed summary of the clinical trial procedures, patient population, and results. Per-vessel measurement performance of FFR-r with respect to invasive FFR was reported, including a localized performance summary by vessel and segment. A PDF FFR T Results summary is provided to the physician for each patient scan. This summary includes prominent warnings regarding interpretation of the output as well as a representation of average error observed in the clinical data for various ranges of FFR-T measurement.
Labeling intended for internal case analysts was also provided, which adequately described detailed data processing steps and data features that could affect accuracy of results.
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RISKS TO HEALTH
Table 4 below identifies the risks to health that may be associated with use of a Coronary Physiologic Simulation Software Device and the measures necessary to mitigate these risks.
TABLE 4: RISK/MITIGATION MEASURES
| Identified Risk | Mitigation Measures |
|---|---|
| False negative results improperly indicating diseased vessel as low probability for significant disease leads to delay of further evaluation/treatment | Software Verification, Validation, and Hazard Analysis |
| Non-clinical Performance Testing | |
| Clinical Testing | |
| False positive results improperly indicating diseased vessel as high probability for significant disease leads to incorrect patient management | Consistency (Repeatability/Reproducibility) |
| Evaluation | |
| Labeling | |
| Delayed delivery of results leading to delay of further evaluation/treatment | |
| Failure to properly interpret device results leads to incorrect patient management | Human Factors Testing |
| Labeling |
SPECIAL CONTROLS:
In combination with the general controls of the FD&C Act. the Coronary Physiologic Simulation Software Device is subject to the following special controls:
-
- Adequate software verification & validation based on comprehensive hazard analysis with identification of appropriate mitigations must be performed including:
- a. Full characterization of technical parameters of the software, including any proprietary algorithm(s) used to model the vascular anatomy.
- i. Adequate description of the expected impact of all applicable image acquisition hardware features and characteristics on performance and any associated minimum specifications.
- b. Adequate consideration of privacy and security issues in the system design.
- i. Adequate mitigation of impact of failure of any subsystem components (signal detection and analysis, data storage, system communications and cybersecurity) with respect to incorrect patient reports and operator failures.
-
- Adequate non-clinical performance testing must be provided to demonstrate the validity of computational modeling methods for flow measurement.
-
- Clinical data supporting the proposed intended use must be provided, including the following:
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- Output measure(s) must be compared to a clinically acceptable method and must a. adequately represent the simulated measure(s) the device provides in an accurate and reproducible manner.
- Clinical utility of the device measurement accuracy must be demonstrated by b. comparison to that of other available diagnostic tests (from literature analysis).
- Statistical performance of the device within clinical risk strata (e.g., age, relevant C. comorbidities, disease stability) must be reported.
- d. The data set must be adequately representative of the intended use population for the device (e.g., patients, range of vessel sizes, imaging device models). Any selection criteria or limitations of the samples must be fully described and justified.
- e. Statistical methods must consider the pre-defined endpoints.
- i. Estimates of probabilities of incorrect results must be provided for each endpoint.
- ii. Where multiple samples from the same patient are used, statistical analysis must not assume statistical independence without adequate justification.
- iii. Report must provide appropriate confidence intervals for each performance metric.
- f. Sensitivity and specificity must be characterized across the range of available measurements.
- Agreement of the simulated measure(s) with clinically acceptable measure(s) must be g. assessed across the full range of measurements.
- Comparison of the measurement performance must be provided across the range of h. intended image acquisition hardware.
- i. If the device uses a cut-off threshold or operates across a spectrum of disease, it must be established prior to validation and it must be justified as to how it was determined and clinically validated.
-
- Adequate validation must be performed and controls implemented to characterize and ensure consistency (repeatability and reproducibility) of measurement outputs.
- Acceptable incoming image quality control measures and the resulting image a. rejection rate for the clinical data must be specified.
- b. Data must be provided within the clinical validation study or using equivalent datasets demonstrating the consistency (i.e., repeatability/reproducibility) of the output that is representative of the range of data quality likely to be encountered in the intended use population and relevant use conditions in the intended use environment.
- i. Testing must be performed using multiple operators meeting planned qualification criteria and using the procedure that will be implemented in the production use of the device.
- ii. The factors (e.g., medical imaging data set, operator) must be identified regarding which were held constant and which were varied during the evaluation, and a description must be provided for the computations and statistical analyses used to evaluate the data.
-
- Human factors evaluation and validation must be provided to demonstrate adequate performance of the user interface to allow for users to accurately measure intended
13
parameters, particularly where parameter settings that have impact on measurements require significant user intervention.
-
- Device labeling must be provided that adequately describes the following:
- The device's intended use, including the type of imaging data used, what the device a. measures and outputs to the user, whether the measure is qualitative and/or quantitative, the clinical indications for which it is to be used, and the specific population for which the device use is intended.
- Appropriate warnings specifying the intended patient population, identifying anatomy b. and image acquisition factors that may impact measurement results, and providing cautionary guidance for interpretation of the provided measurements.
- Key assumptions made in the calculation and determination of simulated C. measurements.
- d. The measurement performance of the device for all presented parameters, with appropriate confidence intervals, and the supporting evidence for this performance. Per-vessel clinical performance, including where applicable localized performance according to vessel and segment, must be included as well as a characterization of the measurement error across the expected range of measurement for key parameters based on the clinical data.
- e. A detailed description of the patients studied in the clinical validation (e.g., age, gender, race/ethnicity, clinical stability, current treatment regimen) as well as procedural details of the clinical study (e.g., scanner representation, calcium scores, use of beta-blockers/nitrates).
- f. Where significant human interface is necessary for accurate analysis, adequately detailed description of the analysis procedure using the device and any data features that could affect accuracy of results.
BENEFIT/RISK DETERMINATION
The probable risks of the device are based on data collected in clinical studies described above. Because the device uses a previously-obtained CT scan (