(386 days)
The intended use of the CADScor System is to record heart sounds, murmurs and vibration for calculation of a patient specific score, indicating the risk of presence of coronary stenosis, as an aid in cardiac analysis and diagnosis.
The CADScor System is a digital stethoscope for recording and quantifying acoustic noise from micro-turbulence originating from stenosis of the coronary arteries. Computational processing of a recording obtained from the chest surface of a patient is used to determine a coronary artery disease (CAD) risk score and associated risk classification.
Three components make up the CADScor System: the Docking station, the Acoustic Recording Sensor and the Patch. The docking station is responsible for charging and qualifying the sensor. These functionalities only happen when the Acoustic Recording Sensor is in direct contact with the docking station. An LED light indicates the status of the sensor. The Acoustic Recording Sensor contains a display, a microphone and an on/off button. The heart sound is recorded through a microphone and once the CAD risk score is calculated, it appears on the display screen. The patch is directly in contact with the patient's chest at the fourth left inter costal space (IC4-L) region. The patch provides a tight seal between the chest surface and the microphone to reduce the external noise that is picked up by the sensor. The patch is for single use only and is discarded after a CAD-score is calculated. A radiofrequency identification (RFID) tag in the patch is used to prevent the patch from being used more than once.
An algorithm is used to calculate a coronary artery disease (CAD) risk score. The algorithm analyzes 8 features of the acoustic recording and three clinical features that are manually inputted into the device by the clinician. The clinical inputs are age, gender and presence of hypertension. The eight acoustic features are put through a linear discriminate analysis and then put through a logistic regression with the clinical features. The risk score and associated categorization color are displayed on the acoustic sensor screen. There are two risk categories: low risk and elevated risk (Figure 2). Clinicians can use the score and categorization to aid in a clinician's determination of whether further testing is needed for a CAD diagnosis.
Here's a breakdown of the acceptance criteria and the study details for the CADScor System, based on the provided text:
CADScor System: Acceptance Criteria and Study Details
1. Table of Acceptance Criteria and Reported Device Performance
| Test Type | Purpose | Acceptance Criteria | Reported Device Performance |
|---|---|---|---|
| Biocompatibility | |||
| Cytotoxicity | To identify any cytotoxic effects to the patient | No evidence of toxicity or cell lysis | PASS |
| Sensitization | To identify tissue reactions from the patch | No evidence of tissue reaction | PASS |
| Irritation | To identify tissue irritation | No evidence of tissue irritation | PASS |
| EMC & Electrical Safety | To conform to FDA-recognized standards for basic safety and essential performance of Medical Electrical Equipment | Adherence to IEC 60601-1-2:2014 and IEC 60601-1:2006 + A1:2013 | Conforms to IEC 60601-1-2:2014 and IEC 60601-1:2006 + A1:2013 |
| Software | To demonstrate that the device software met the software requirements (Moderate Level of Concern) | Software verification and validation testing performed; Algorithm unit testing to demonstrate software requirements met | Verification and validation testing performed; Algorithm unit testing performed using privately collected patient data. |
| Acoustic Performance | |||
| Free Field Acoustic Testing | Ensure the manufactured parts of the CADScor systems are performing according to test specifications | Heart and ambient microphone sensitive within 5dB throughout the range of frequencies tested | PASS |
| Microphone Sensitivity | Test microphone sensitivity before and after production | Sensitivity of the heart and ambient microphones are stable at 5, 10, 20, 140 Hz before and after the plotting process | PASS |
| Acoustic Verification | Ensure the manufactured CADScor Sensor functionality and performance | The sensor and ambient noise detectors respond uniformly to all seven frequencies (b)(4) and (b)(4) Hz, and broad band white noise. | PASS |
| Mechanical Testing | Verify the specified geometric proportions and mechanical lifetime | Manufactured to correct dimension specifications; Force to remove sensor from docking station is 5N; Qualification successful before and after 1000 insertions of the sensor onto the docking station. | PASS (Specific dimensions and force tests confirmed to be met. Qualification successful before and after 1000 insertions of sensor onto docking station.) |
| Usability Testing | To demonstrate that the user can correctly use the device, including device placement, based solely on reading the directions for use. | Successful completion of tasks in a simulated-use environment (e.g., correct patch positioning, entering clinical features, reading display). | Usability testing performed with medical doctors/physicians, registered nurses, and service technicians. Tasks included correct patch positioning, entering clinical features, and reading the display. (Outcome generally implied as successful to meet guidance requirements). |
| Clinical Performance | To determine the specificity and sensitivity of the CAD-score in patients with low-intermediate risk for ischemic heart disease. The device should aid clinicians in determining whether further testing is needed for a CAD diagnosis, particularly as a "rule-out" device for patients with suspected ischemic heart disease. | Primary Objective: Determine specificity and sensitivity in patients with low-intermediate risk for ischemic heart disease. | |
| Effectiveness as a rule-out device: High Negative Predictive Value (NPV). | Sensitivity: 87.5% (for CAD-score threshold of 20) | ||
| Specificity: 37.5% (for CAD-score threshold of 20) | |||
| Area Under the Curve (AUC): 71.5% | |||
| Positive Predictive Value (PPV): 14.4% | |||
| Negative Predictive Value (NPV): 96.2% |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: 100 patients
- Data Provenance: Prospective, multicenter, randomized clinical study. The country of origin of the data is not explicitly stated in the provided text, but the sponsor is based in Denmark.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
The text does not specify the number of experts or their qualifications used to establish the ground truth for the test set.
4. Adjudication Method for the Test Set
The text does not explicitly state an adjudication method. It indicates that the patient's diagnosis (ground truth) was determined by Coronary Computed Tomography Angiography (CCTA).
- CAD diagnosis: At least one significant stenosis (minimum 11% diameter reduction) of a coronary vessel.
- Non-CAD diagnosis: CT calcium score of zero and no CT stenosis.
This suggests CCTA served as the definitive diagnostic reference, rather than a multi-expert consensus on the CADScor results themselves.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly described. The clinical study focused on the standalone performance of the CADScor system against CCTA as ground truth, not on how human readers' performance improved with AI assistance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
Yes, a standalone performance study was done. The clinical study reported the sensitivity, specificity, AUC, PPV, and NPV of the CADScor system (algorithm output) for predicting CAD, which is a standalone assessment of the algorithm's performance. The results (87.5% sensitivity, 37.5% specificity, 96.2% NPV) are for the device's output itself, not for human interpretation aided by the device.
7. The Type of Ground Truth Used
The type of ground truth used for the clinical study was Coronary Computed Tomography Angiography (CCTA), with specific criteria for CAD (≥11% diameter reduction stenosis) and Non-CAD (CT calcium score of zero and no CT stenosis). This is a definitive diagnostic imaging modality.
8. The Sample Size for the Training Set
The sample size for the training set is not explicitly stated. The text only mentions that the 100 patients in the test set "were independent from the algorithm training set" and that algorithm unit testing used "privately collected patient data," but no specific number is given for the training set.
9. How the Ground Truth for the Training Set Was Established
The text does not explicitly describe how the ground truth for the training set was established, other than mentioning that "privately collected patient data" was used for algorithm unit testing. Given the context of the clinical study, it is highly probable that similar diagnostic criteria (like CCTA) were used for establishing training set ground truth, but this is not confirmed in the provided document.
§ 870.1420 Coronary artery disease risk indicator using acoustic heart signals.
(a)
Identification. A coronary artery disease risk indicator using acoustic heart signals is a device that records heart sounds including murmurs and vibrations to calculate a patient-specific risk of presence of coronary artery disease, as an aid in cardiac analysis and diagnosis.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Clinical performance testing must fulfill the following:
(i) Testing must include a discussion of the patient population and any statistical techniques used for analyzing the data; and
(ii) Testing must be representative of the intended use population for the device. Any selection criteria or sample limitations must be fully described and justified.
(2) Acoustic performance testing must evaluate microphone sensitivity, sound acquisition bandwidth, and amplitude accuracy. The acoustic sensor specifications and mechanism used to capture heart sounds must be described.
(3) A scientific justification for the validity of the algorithm(s) must be provided. This justification must fulfill the following:
(i) All inputs and outputs of the algorithm must be fully described;
(ii) The procedure for segmenting, characterizing, and classifying the acoustic signal must be fully described; and
(iii) This justification must include verification of the algorithm calculations and validation using an independent data set.
(4) The patient-contacting components of the device must be demonstrated to be biocompatible.
(5) Software verification, validation, and hazard analysis must be performed.
(6) Human factors/usability testing must demonstrate that the user can correctly use the device, including device placement, based solely on reading the directions for use.
(7) Performance data must demonstrate the electromagnetic compatibility and electrical safety of the device.
(8) Labeling must include the following:
(i) A description of what the device measures and outputs to the user;
(ii) Instructions for proper placement of the device;
(iii) Instructions on care and cleaning of the device;
(iv) Warnings identifying sensor acquisition factors that may impact measurement results and instructions for mitigating these factors; and
(v) The expected performance of the device for all intended use populations and environments.