(232 days)
Not Found
Yes
The summary explicitly states that the device is an "automated machine learning-based decision support system" and that the classification decision is derived from an "AI algorithm developed using a convolutional neural network."
No
The device is a screening tool that provides information to alert a physician for referral to confirmatory investigations, rather than directly treating or mitigating a disease.
Yes
The "Intended Use" section states it is a "screening tool" and "provides information alerting the physician for referral to confirmatory investigations." The "Device Description" clarifies it provides a "binary classification decision suggestive of the presence of Cardiac Amyloidosis (CA)," which is a core function of a diagnostic device to aid in identifying a condition. Although it states "Patient management decisions should not be made solely on the results" and "The ultimate diagnostic decision remains the responsibility of the interpreting clinician," it clearly serves as a component of the diagnostic process.
Yes
The device description explicitly states that EchoGo Amyloidosis 1.0 is "fully automated without a graphical user interface" and takes a 2D echocardiogram as input, producing a binary classification decision as output. The description focuses solely on the software algorithm and its function, with no mention of accompanying hardware components required for its operation beyond the source of the echocardiogram data. The mention of an "Image Processing Library" further supports its software nature.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs analyze samples taken from the human body. The core function of an IVD is to examine biological specimens like blood, urine, tissue, etc., to provide information about a person's health.
- This device analyzes images of the human body. EchoGo Amyloidosis 1.0 takes a 2D echocardiogram (an image) as its input. It processes this image to provide a decision support output.
While the device provides information that aids in a medical diagnosis, it does so by analyzing imaging data, not biological samples. This places it outside the typical definition and regulatory scope of an In Vitro Diagnostic device. It falls under the category of medical imaging analysis software or a decision support system based on imaging.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
EchoGo Amyloidosis 1.0 is an automated machine learning-based decision support system, indicated as a screening tool for adult patients aged 65 years and over with heart failure undergoing cardiovascular assessment using echocardiography.
When utilised by an interpreting physician, this device provides information alerting the physician for referral to confirmatory investigations.
EchoGo Amyloidosis 1.0 is indicated in adult patients aged 65 years and over with heart failure. Patient management decisions should not be made solely on the results of the EchoGo Amyloidosis 1.0 analysis.
Product codes (comma separated list FDA assigned to the subject device)
SDJ
Device Description
EchoGo Amyloidosis 1.0 takes a 2D echocardiogram of an apical four chamber (A4C) as its input and reports as an output a binary classification decision suggestive of the presence of Cardiac Amyloidosis (CA).
The binary classification decision is derived from an AI algorithm developed using a convolutional neural network that was pre-trained on a large dataset of cases and controls.
The A4C echocardiogram should be acquired without contrast and contain at least one full cardiac cycle. Independent training, tune and test datasets were used for training and performance assessment of the device.
EchoGo Amyloidosis 1.0 is fully automated without a graphical user interface.
The ultimate diagnostic decision remains the responsibility of the interpreting clinician using patient presentation, medical history, and the results of available diagnostic tests, one of which may be EchoGo Amyloidosis 1.0.
EchoGo Amyloidosis 1.0 is a prescription only device.
Mentions image processing
Not Found
Mentions AI, DNN, or ML
EchoGo Amyloidosis 1.0 is an automated machine learning-based decision support system
The binary classification decision is derived from an AI algorithm developed using a convolutional neural network
Input Imaging Modality
2D echocardiogram
Anatomical Site
Cardiovascular
Indicated Patient Age Range
adult patients aged 65 years and over
Intended User / Care Setting
Interpreting physician
Description of the training set, sample size, data source, and annotation protocol
Algorithm training data was collected from collaborating centres. The clinical characteristics of the training data are provided in the table below. The training dataset included both controls and cases across a broad age range, consisting of males and females, with most participants aged between 61-80 years. Racial demographics included 79.7-80.6% White, 13.3% Black, 2.3-3.0% Asian, and 3.7-4.0% Other.
Description of the test set, sample size, data source, and annotation protocol
An independent clinical validation study was conducted to complile a clinical test dataset (testing data). For testing and intended use, the data comprised male and female patients aged 65+ years with heart failure (HF). The testing dataset included both controls and cases, consisting of male and female participants, with most individuals aged between 71-80 years. Racial demographics included 58.4-66.0% White, 24.6-26.8% Black, 1.9-11.8% Asian, and 5.2-5.3% Other. The data was intended to be representative of the intended use population and containing a range of data sources and data quality likely to be encountered in the intended use population and relevant use conditions in the intended use environment (see table below). The clinical validation study was used to demonstrate consistency of the device output as well as to assess agreement with reference ground truth.
Device performance was determined according to a retrospective case:control study including multiple sites spanning nine states in the USA. The final testing data cohort for patients >65 years of age with evidence of heart failure amounted to 1,164 patients, comprising 749 controls and 415 cases.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Study Type: Retrospective case:control study
Sample Size: 1,164 patients (749 controls and 415 cases)
Standalone performance:
EchoGo Amyloidosis 1.0 correctly identified 310 true positives, and 569 true negatives, alongside 65 false positives and 57 false negatives.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Sensitivity: 84.5% (95% C1: 80.3%, 88.5%)
Specificity: 89.7% (95% Cl: 87.0%, 92.4%)
PPV: 82.7% (95% Cl: 78.8%, 86.5%)
NPV: 90.9% (95% CI: 88.8%, 93.2%)
Positive Predictive Value at 2.2% prevalence: 15.6% (11.0%, 20.8%)
Negative Predictive Value at 2.2% prevalence: 99.6% (99.5%, 99.7%)
Positive Agreement (Repeatability): 100%
Negative Agreement (Repeatability): 100%
Positive Agreement (Reproducibility): 85.5% (82.4%, 88.2%)
Negative Agreement (Reproducibility): 79.9% (76.5%, 83.2%)
No-classifications: 14.0%
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 870.2200 Adjunctive cardiovascular status indicator.
(a)
Identification. The adjunctive cardiovascular status indicator is a prescription device based on sensor technology for the measurement of a physical parameter(s). This device is intended for adjunctive use with other physical vital sign parameters and patient information and is not intended to independently direct therapy.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Software description, verification, and validation based on comprehensive hazard analysis must be provided, including:
(i) Full characterization of technical parameters of the software, including any proprietary algorithm(s);
(ii) Description of the expected impact of all applicable sensor acquisition hardware characteristics on performance and any associated hardware specifications;
(iii) Specification of acceptable incoming sensor data quality control measures; and
(iv) Mitigation of impact of user error or failure of any subsystem components (signal detection and analysis, data display, and storage) on accuracy of patient reports.
(2) Scientific justification for the validity of the status indicator algorithm(s) must be provided. Verification of algorithm calculations and validation testing of the algorithm using a data set separate from the training data must demonstrate the validity of modeling.
(3) Usability assessment must be provided to demonstrate that risk of misinterpretation of the status indicator is appropriately mitigated.
(4) Clinical data must be provided in support of the intended use and include the following:
(i) Output measure(s) must be compared to an acceptable reference method to demonstrate that the output measure(s) represent(s) the predictive measure(s) that the device provides in an accurate and reproducible manner;
(ii) The data set must be representative of the intended use population for the device. Any selection criteria or limitations of the samples must be fully described and justified;
(iii) Agreement of the measure(s) with the reference measure(s) must be assessed across the full measurement range; and
(iv) Data must be provided within the clinical validation study or using equivalent datasets to demonstrate the consistency of the output and be representative of the range of data sources and data quality likely to be encountered in the intended use population and relevant use conditions in the intended use environment.
(5) Labeling must include the following:
(i) The type of sensor data used, including specification of compatible sensors for data acquisition;
(ii) A description of what the device measures and outputs to the user;
(iii) Warnings identifying sensor reading acquisition factors that may impact measurement results;
(iv) Guidance for interpretation of the measurements, including warning(s) specifying adjunctive use of the measurements;
(v) Key assumptions made in the calculation and determination of measurements;
(vi) The measurement performance of the device for all presented parameters, with appropriate confidence intervals, and the supporting evidence for this performance; and
(vii) A detailed description of the patients studied in the clinical validation (
e.g., age, gender, race/ethnicity, clinical stability) as well as procedural details of the clinical study.
0
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo is in blue and includes the letters "FDA" in a square and the words "U.S. FOOD & DRUG ADMINISTRATION".
November 15, 2024
Ultromics Limited Jaco Jacobs Chief Regulatory and Compliance Officer 4630 Kingsgate Cascade Way, Oxford Business Park Oxford, OX4 2SU United Kingdom
Re: K240860
Trade/Device Name: EchoGo Amyloidosis (1.0) Regulation Number: 21 CFR 870.2200 Regulation Name: Adjunctive Cardiovasular Status Indicator Regulatory Class: Class II Product Code: SDJ Dated: November 13, 2024 Received: November 13, 2024
Dear Jaco Jacobs:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
1
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30. Design controls; 21 CFR 820.90. Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatory
2
assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Stephen C. Browning -S
LCDR Stephen Browning Assistant Director Division of Cardiac Electrophysiology, Diagnostics, and Monitoring Devices Office of Cardiovascular Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
3
Indications for Use
Submission Number (if known)
Device Name
EchoGo Amyloidosis (1.0)
Indications for Use (Describe)
EchoGo Amyloidosis 1.0 is an automated machine learning-based decision support system, indicated as a screening tool for adult patients aged 65 years and over with heart failure undergoing cardiovascular assessment using echocardiography.
When utilised by an interpreting physician, this device provides information alerting the physician for referral to confirmatory investigations.
EchoGo Amyloidosis 1.0 is indicated in adult patients aged 65 years and over with heart failure. Patient management decisions should not be made solely on the results of the EchoGo Amyloidosis 1.0 analysis.
Type of Use (Select one or both, as applicable)
( Prescription Use (Part 21 CFR 801 Subpart D)
Over The-Counter Use (21 CFR 801 Subpart C)
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Image /page/4/Picture/0 description: The image shows the logo for ULTROMICS. The logo consists of a circular graphic on the left and the word "ULTROMICS" in bold, dark blue letters on the right. The circular graphic is a gradient of blue and green, creating a swirling effect.
1 Submitter
| Company | Ultromics Limited
4630 Kingsgate Cascade Way, Oxford Business Park South, Oxford,
Oxfordshire, United Kingdom, OX4 2SU |
|---------------|------------------------------------------------------------------------------------------------------------------------------|
| Contact | Dr. Jaco Jacobs |
| Date Prepared | 15 Nov 2024 |
2 Subject Device
| Product Trade Name | EchoGo Amyloidosis |
|---|---|
| Model Number | 1.0 |
| 510(k) | K240860 |
| Manufacturer | Ultromics Limited |
| Medical Speciality | Cardiology |
| Regulation | 21 CFR 870.2200 - Cardiovascular Monitoring Devices |
| Product Code | SDJ - Adjunctive Cardiac Amyloidosis Status Indicator |
| Regulatory Class | II |
EchoGo Amyloidosis is the product trade name and 1.0 is the model number. For the avoidance of doubt, in this submission we combine the product trade name and model number and refer to the subject device as EchoGo Amyloidosis 1.0.
3 Predicate Device
| Predicate Device | EchoGo Heart Failure |
|---|---|
| 510(k) | K222463 |
| Manufacturer | Ultromics Limited |
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4 Device Description
EchoGo Amyloidosis 1.0 takes a 2D echocardiogram of an apical four chamber (A4C) as its input and reports as an output a binary classification decision suggestive of the presence of Cardiac Amyloidosis (CA).
The binary classification decision is derived from an AI algorithm developed using a convolutional neural network that was pre-trained on a large dataset of cases and controls.
The A4C echocardiogram should be acquired without contrast and contain at least one full cardiac cycle. Independent training, tune and test datasets were used for training and performance assessment of the device.
EchoGo Amyloidosis 1.0 is fully automated without a graphical user interface.
The ultimate diagnostic decision remains the responsibility of the interpreting clinician using patient presentation, medical history, and the results of available diagnostic tests, one of which may be EchoGo Amyloidosis 1.0.
EchoGo Amyloidosis 1.0 is a prescription only device.
5 Comparison of Intended Use
5.1 Indications for Use
We discuss similarities and differences of the subject and predicate devices.
5.1.1 Subject Device
EchoGo Amyloidosis 1.0 is an automated machine learning-based decision support system, indicated as a screening tool for adult patients aged 65 years and over with heart failure undergoing cardiovascular assessment using echocardiography.
When utilised by an interpreting physician, this device provides information alerting the physician for referral to confirmatory investigations.
EchoGo Amyloidosis 1.0 is indicated in adult populations over 65 years of age with heart failure. Patient management decisions should not be made solely on the results of the EchoGo Amyloidosis 1.0 analysis.
5.1.2 Predicate Device
EchoGo Heart Failure 1.0 is an automated machine learning-based decision support system, indicated as a diagnostic aid for patients undergoing routine functional cardiovascular assessment using echocardiography. When utilised by an interpreting clinician, this device provides information that may be useful in detecting heart failure with preserved ejection fraction (HFpEF).EchoGo Heart Failure 1.0 is indicated in adult populations over 25 years of age. Patient management
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decisions should not be made solely on the results of the EchoGo Heart Failure 1.0 analysis. EchoGo Heart Failure 1.0 takes as input an apical 4-chamber view of the heart that has been captured and assessed to have an ejection fraction ≥50%.
6 Discussion
Both subject and primary predicate devices are software-only devices and automated machine learning-based decision support system, indicated for patients undergoing routine cardiovascular assessment using echocardiography.
Both subject and primary predicate are indicated to provide information that may be useful for healthcare professionals in forming a diagnostic work up decisioning. The subject device is intended to be used "as a screening tool for patients undergoing routine cardiovascular assessment using echocardiography" and the primary predicate device is intended to be used "as diagnostic aid for patients undergoing routine functional cardiovascular assessment".
Both subject and primary predicate are intended to provide information on the cardiovascular state of patients. Both subject and primary predicate are used for "patients undergoing routine cardiovascular assessment using echocardiography ".
Both subject and primary predicate devices are indicated in adult populations. The subject device "EchoGo Amyloidosis 1.0 is indicated in adult populations over 65 years of age" and the primary predicate is indicated in adult populations over 25 years of age".
For both subject and primary predicate devices, the patient management decisions should not be made solely on device recommendations.
For purposes of substantial equivalence, the term intended use means the general purpose of the device or its function and encompasses the indications for use. The term indications for use describes the disease the device is intended to serve as a diagnostic aid including a description of the patient population for which the device is intended. Both devices are intended as adjunctive aids for cardiovascular states or conditions and both devices can be used on adult populations. It follows that both devices have intended use of providing adjunctive information on the cardiovascular condition of a patient.
Ultromics believes that general controls and special controls of the predicate device are sufficient to ensure the safety and effectiveness of the subject device.
7 Technological Characteristics
A full comparison of the technological characteristics of the subject and predicate devices follows:
- Both devices takes as input a DICOM file consisting of an echocardiogram from medical devices, such as Medical Ultrasound Equipment or Picture Archiving and Communication System (PACS). Both devices therefore receive as input data that is the output of a preceding medical device (e.g. Medical Ultrasound Equipment or PACS) .
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- The output of both devices is based on an artificial intelligence (AI) model developed using a convolutional neural network that produces a classification result.
- Both device reports a classification decision as suggestive or not suggestive of a specific cardiac condition. Both devices are adjunctive cardiovascular status indicators.
- The subject device reports a classification decision as suggestive or not suggestive of the presence of cardiac amyloidosis (CA), while the predicate device reports a classification decision as suggestive or not suggestive of the presence of heart failure with preserved ejection fraction (HFpEF).
- Both subject and primary predicate takes as input an apical 4-chamber view of the heart that has been captured. The subject device takes as input an apical 4-chamber view of the heart that has been captured, while the predicate device takes as input an apical 4chamber view of the heart that has been captured and assessed to have an ejection fraction ≥50%.
- Subject device includes application programming interfaces for input and output for interfacing with external applications and medical devices. This includes the DICOM network interfaces that were also present in the predicate device.
- Subject device allows deployment options permitting functionality to be distributed and replicated, ensuring the scalability, robustness and non-clinical performance of the device.
| Characteristic | Subject Device
EchoGo Amyloidosis 1.0 | Predicate Device
EchoGo Heart Failure
(K222463) |
|--------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Regulation | 21 CFR 870.2200 | 21 CFR 870.2200 |
| Generic Device Type | Adjunctive cardiovascular status
indicator | Adjunctive cardiovascular status
indicator |
| SaMD | Yes | Yes |
| Characteristic | Subject Device
EchoGo Amyloidosis 1.0 | Predicate Device
EchoGo Heart Failure
(K222463) |
| Indications for Use | EchoGo Amyloidosis 1.0 is an
automated machine learning-based
decision support system, indicated
as a screening tool for adult
patients over 65 years of age with
heart failure undergoing routine
cardiovascular assessment using
echocardiography.
When utilised by an interpreting
physician, this device provides
information alerting the physician
for referral to confirmatory
investigations.
EchoGo Amyloidosis 1.0 is indicated
in adult populations over 65 years
of age with heart failure. Patient
management decisions should not
be made solely on the results of
the EchoGo Amyloidosis 1.0
analysis. | EchoGo Heart Failure 1.0 is an
automated machine learning-
based decision support system,
indicated as a diagnostic aid for
patients undergoing routine
functional cardiovascular
assessment using
echocardiography.
When utilised by an interpreting
clinician, this device provides
information that may be useful in
detecting heart failure with
preserved ejection fraction
(HFpEF).
EchoGo Heart Failure 1.0 is
indicated in adult populations
over 25 years of age. Patient
management decisions should not
be made solely on the results of
the EchoGo Heart Failure 1.0
analysis.
EchoGo Heart Failure 1.0 takes as
input an apical 4-chamber view of
the heart that has been captured
and assessed to have an ejection
fraction ≥50%. |
| Population | Adults over the age of 65 | Adults over the age of 25 |
| Anatomical Site | Cardiovascular | Cardiovascular |
| Users | Interpreting clinician | Interpreting clinician |
| Machine Learning-
Based Algorithm | Yes | Yes |
| Operating platform | Hosted on Ultromics' platform or
on third party infrastructure. | Hosted on Ultromics' platform or
on third party infrastructure. |
| Interoperability | Interoperability testing conducted
with a device conformant with the
DICOM Standard (NEMA PS 3.1 -
3.20 2022d [Rec# 12-349]) | Interoperability testing conducted
with a device conformant with the
DICOM Standard (NEMA PS 3.1 -
3.20 2022d [Rec# 12-349]) |
| Characteristic | Subject Device
EchoGo Amyloidosis 1.0 | Predicate Device
EchoGo Heart Failure
(K222463) |
| Software | Complies with IEC 62304:2015 and
GPSV. Developed under an FDA
QSR and ISO 13485:2016 compliant
QMS incorporating risk
management per ISO 14971:2019.
Software verification and validation
testing conducted. | Complies with IEC 62304:2015 and
GPSV. Developed under an FDA
QSR and ISO 13485:2016 compliant
QMS incorporating risk
management per ISO 14971:2019.
Software verification and
validation testing conducted. |
| Risk Management | In accordance with ISO 14971:2019 | In accordance with ISO 14971:2019 |
| Cybersecurity | Post-market Management of
Cybersecurity in Medical Devices.
Content of Premarket Submissions
for Management of Cybersecurity in
Medical Devices.
Cybersecurity for Networked
Medical Devices Containing Off-
the-Shelf (OTS) Software:
Guidance for Industry. | Post-market Management of
Cybersecurity in Medical Devices.
Content of Premarket
Submissions for Management of
Cybersecurity in Medical Devices.
Cybersecurity for Networked
Medical Devices Containing Off-
the-Shelf (OTS) Software:
Guidance for Industry. |
| Usability | Complies with IEC 62366-1:2020
and general use of FDA guidance
documents on usability
engineering. Formative and
summative evaluations conducted
with clinicians specialising in
cardiology, with knowledge/
experience in echocardiography
(N=18). | Complies with IEC 62366-1:2020
and general use of FDA guidance
documents on usability
engineering. Formative and
summative evaluations conducted
with accredited cardiac
physiologists (N=2) and
cardiologists (N=5). |
| Pre-clinical
Performance Testing | No animal studies were conducted. | No animal studies were
conducted. |
| Bench Performance
Testing | Technical validation, numerical
stability, and regression testing. | Technical validation, numerical
stability, and regression testing. |
| Clinical Performance
Testing | Validated on a validation cohort
including 12 US sites and 3
International sites representative of
the intended use population. | Validated on a US cohort
population, comprising 8
independent clinical sites
representative of the intended use
population. |
The following table summarises technological characteristics.
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Any technological differences between the subject and predicate devices raise no new concerns with regards to safety and efficacy. In addition, Ultromics is of the view that general controls
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alongside special controls introduced under the primary product code of the predicate are sufficient to ensure safety and efficacy of the EchoGo Amyloidosis 1.0 device.
8 Special Controls
Special controls for regulation 21 CFR 870.2200 follows.
| Control | Description | Evidence |
|---|---|---|
| 1 | Software description, verification, and validation based on comprehensive hazard analysis: | |
| a | Full characterization of technical parameters of the software, including any proprietary algorithm(s); | A.22.1 ADP EchoGo Amyloidosis 1.0: |
| Algorithmic Design Plan | ||
| A.22.2 ADS EchoGo Amyloidosis 1.0: | ||
| Algorithm Design Specification | ||
| A.22.3 ADR EchoGo Amyloidosis 1.0: | ||
| Algorithmic Design Report | ||
| b | Description of the expected impact of all applicable acquisition hardware characteristics on performance and any associated hardware specifications; | A.7.1 EchoGo Amyloidosis 1.0 IFU: |
| Clinician Manual | ||
| A.23.2 PTR EchoGo Amyloidosis 1.0: | ||
| Clinical Performance Testing Report | ||
| c | Specification of acceptable data quality control measures | |
| d | Mitigation of impact of user error or failure of any components (data detection and analysis, data display, and storage) on accuracy of patient reports | 1. Data Validation for Prediction Safety |
- Fault Tolerance (Component Failure)
- Prevention of Misreporting
A.13.1 SDS EchoGo Amyloidosis 1.0:
Software Design Specification
A.13.2 SDS Ultromics Image Processing
Library 2.0: Software Design
Specification
A.13.3 SDS Ultromics Repository Library
2.0: Software Design Specification
A.13.4 SDS Ultromics Dicom Library 2.0:
Software Design Specification
A.13.5 SDS Ultromics Reporting Library
3.0: Software Design Specification
A.11.2 SRS EchoGo Amyloidosis 1.0:
Software Requirement Specification |
| Control | Description | Evidence |
| 2 | Scientific justification for the validity of the
status indicator algorithm(s) must be
provided. Verification of algorithm
calculations and validation testing of the
algorithm using a data set separate from
the training data must demonstrate the
validity of modelling. | A.22.1 ADP EchoGo Amyloidosis 1.0:
Algorithmic Design Plan
A.22.2 ADS EchoGo Amyloidosis 1.0:
Algorithm Design Specification
A.22.3 ADR EchoGo Amyloidosis 1.0:
Algorithmic Design Report
A.7.1 EchoGo Amyloidosis 1.0 IFU:
Clinician Manual
A.23.1 PTP EchoGo Amyloidosis 1.0:
Clinical Performance Testing Plan
A.23.2 PTR EchoGo Amyloidosis 1.0:
Clinical Performance Testing Report |
| 3 | Usability assessment must be provided to
demonstrate that risk of misinterpretation
of the status indicator is appropriately
mitigated. | A.22.8.1 HFUEP EchoGo Amyloidosis 1.0:
Human Factors and Usability
Engineering Plan
A.22.8.2 USER EchoGo Amyloidosis 1.0 :
Usability Engineering Report
A.22.8.9 UATP EchoGo Amyloidosis 1.0:
User Acceptance Testing Plan
A.22.8.11 UATR EchoGo Amyloidosis 1.0:
User Acceptance Testing Report |
| 4 | Clinical data must be provided in support of the intended use and include the
following | |
| a | Output measure(s) must be compared to an
acceptable reference method to
demonstrate that the output measure(s)
represent(s) the predictive measure(s) that
the device provides in an accurate and
reproducible manner. | A.23.1 PTP EchoGo Amyloidosis 1.0:
Clinical Performance Testing Plan
A.23.2 PTR EchoGo Amyloidosis 1.0:
Clinical Performance Testing Report |
| b | The data set must be representative of the
intended use population for the device. Any
selection criteria or limitations of the
samples must be fully described and
justified. | |
| c | Agreement of the measure(s) with the
reference measure(s) must be assessed
across the full measurement range. | |
| Control | Description | Evidence |
| d | Data must be provided within the clinical
validation study or using equivalent
datasets to demonstrate the consistency of
the output and be representative of the
range of data sources and data quality
likely to be encountered in the intended
use population and relevant use conditions
in the intended use environment. | A.22.2 ADS EchoGo Amyloidosis 1.0:
Algorithm Design Specification
A.22.3 ADR EchoGo Amyloidosis 1.0:
Algorithmic Design Report
A.7.1 EchoGo Amyloidosis 1.0 IFU:
Clinician Manual
A.23.1 PTP EchoGo Amyloidosis 1.0:
Clinical Performance Testing Plan
A.23.2 PTR EchoGo Amyloidosis 1.0:
Clinical Performance Testing Report |
| 5 | Labeling must include the following: | |
| a | The type of input data used, including
specification of compatible hardware for
data acquisition | A.7.1 EchoGo Amyloidosis 1.0 IFU:
Clinician Manual
A.8.1 ADMAN EchoGo Amyloidosis 1.0:
Administration Manual - Overview |
| b | A description of what the device measures
and outputs to the user | A.8.2 ADMAN EchoGo Amyloidosis 1.0:
Administration Manual - InputAPI:
Specification and Usage
A.8.3 ADMAN EchoGo Amyloidosis 1.0:
Administration Manual - Configuration
Settings
A.8.4 ADMAN EchoGo Amyloidosis 1.0:
Administration Manual - Installation,
Management and Decommissioning
A.8.5 ADMAN EchoGo Amyloidosis 1.0:
Administration Manual - OutputAPI:
Specification and Usage
A.8.6 ADMAN EchoGo Amyloidosis 1.0:
Administration Manual - Security
A.8.7 ADMAN EchoGo Amyloidosis 1.0:
Administration Manual -
Troubleshooting
A.21.3 DICOM EchoGo Amyloidosis 1.0:
DICOM Conformance Statement |
| c | Warnings identifying acquisition or other
factors that may impact output measures | A.7.1 EchoGo Amyloidosis 1.0 IFU:
Clinician Manual |
| d | Guidance for interpretation of the output
measures, including warning(s) specifying
adjunctive use of the results | |
| Control | Description | |
| e | Key assumptions made in the calculation
and determination of results | |
| f | The measurement performance of the
device for all presented parameters, with
appropriate confidence intervals, and the
supporting evidence for this performance | |
| g | A detailed description of the patients
studied in the clinical validation (e.g., age,
gender, race/ethnicity, clinical stability) as
well as procedural details of the clinical
study. | |
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9 Consensus Standards
The following consensus standards will be used in the design and manufacture of EchoGo Amyloidosis 1.0 .
| Standard | Recognition Number |
|---|---|
| ISO 14971:2019 - Medical Devices - Application of Risk Management to | |
| Medical Devices | 5-125 |
| IEC 62304:2015 - Medical Device Software - Software Life Cycle | |
| Processes | 13-79 |
| IEC 62366-1:2020 - Medical Devices - Application of Usability | |
| Engineering to Medical Devices | 5-129 |
| NEMA PS 3.1 - 3.20 2022d - Digital Imaging and Communications in | |
| Medicine (DICOM) Set | 12-349 |
| IEC ISO 10918-1:1994 - Digital Compression and Coding of Continuous- | |
| tone Still Images | 12-261 |
| ISO 14155:2020 - Clinical investigation of medical devices for human | |
| subjects - Good clinical practice | 2-282 |
Ultromics Limited cites conformity to the voluntary standards above. In addition, EchoGo Amyloidosis 1.0 will be designed and manufactured under a QMS that fully conforms to US Quality System (QS) regulation (21 CFR Part 820) and ISO 13485:2016.
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10 Performance Data
10.1 Software Verification and Validation
EchoGo Amyloidosis 1.0 software was developed and tested in accordance with Ultromics' Design Control processes and has been subjected to extensive safety and performance testing. Nonclinical verification and validation test results established that the device meets its design requirements and intended use. A description for the patient characteristics within the training data is provided in the table below.
Software verification was conducted at unit, module, and system integration levels. Risk management analysis generated multiple risk mitigation measures and verification activities. Regression- and numerical stability testing were conducted to ensure the device meets algorithmic specifications. Formative and summative usability assessments were conducted to validate labelling and mitigate against the device outputs being misinterpreted by the clinical user. Cybersecurity and data security testing were conducted to verify that data and patient protected health information security measures are included in the design of the software.
Software verification and validation testing were conducted, and documentation was provided as recommended by FDA's Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices. The software device documentation was evaluated at the Enhanced Documentation level, as specified in the FDA's Content of Premarket Submissions for Device Software Functions guidance.
EchoGo Amyloidosis 1.0 passed all software verification and validation tests.
10.2 Essential Performance
Device performance was validated using bench- and clinical performance testing.
Algorithm training data was collected from collaborating centres. The clinical characteristics of the training data are provided in the table below. The training dataset included both controls and cases across a broad age range, consisting of males and females, with most participants aged between 61-80 years. Racial demographics included 79.7-80.6% White, 13.3% Black, 2.3-3.0% Asian, and 3.7-4.0% Other. An independent clinical validation study was conducted to complile a clinical test dataset (testing data). For testing and intended use, the data comprised male and female patients aged 65+ years with heart failure (HF). The testing dataset included both controls and cases, consisting of male and female participants, with most individuals aged between 71-80 years. Racial demographics included 58.4-66.0% White, 24.6-26.8% Black, 1.9-11.8% Asian, and 5.2-5.3% Other. The data was intended to be representative of the intended use population and containing a range of data sources and data quality likely to be encountered in the intended use population and relevant use conditions in the intended use environment (see table below). The clinical validation study was used to demonstrate consistency of the device output as well as to assess agreement with reference ground truth.
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| Variable | Training data | Testing Data (65+ years with HF) | ||
|---|---|---|---|---|
| Controls | Cases | Controls | Cases | |
| Age, years, median (IQR) | ||||
| 80 | 222 (17.6%) | 229 (17.6%) | 359 (47.9%) | 126 (30.4%) |
| Sex, n(%) | ||||
| Male | 921 (73%) | 948 (72.8%) | 355.0 (47.4%) | 333.0 (80.8%) |
| Female | 341 (27%) | 354 (27.2%) | 394.0 (52.6%) | 79.0 (19.2%) |
| Race, n(%) | ||||
| Black | 161 (13.3%) | 165 (13.3%) | 172.0 (26.8%) | 100.0 (24.6%) |
| White | 965 (79.7%) | 997 (80.6%) | 424.0 (66.0%) | 237.0 (58.4%) |
| Asian | 36 (3.0%) | 29 (2.3%) | 12.0 (1.9%) | 48.0 (11.8%) |
| Other | 49 (4.0%) | 46 (3.7%) | 34.0 (5.3%) | 21.0 (5.2%) |
| Patient Characteristics | ||||
| Hypertension, | ||||
| n(%) | 723 (57.3%) | 337 (25.9%) | 581.0 (91.1%) | 170.0 (74.6%) |
| Diabetes Mellitus, | ||||
| n(%) | 358 (28.4%) | 146 (11.2%) | 272.0 (42.4%) | 54.0 (23.7%) |
| Coronary Artery | ||||
| Disease, n(%) | 175 (13.9%) | 106 (8.1%) | 201.0 (40.7%) | 66.0 (41.5%) |
| Height, cm, | ||||
| median (IQR) | 173 (166, 180) | 173 (166, 179) | 167.5 (160.0, | |
| 175.0) | 172.72 (167.6, | |||
| 177.8) |
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| Variable | Training data | Testing Data (65+ years with HF) |
|---|---|---|
| Weight, Kg, median (IQR) | 88.9 (75.9, 103.3) 79.7 (68.0, 90.5) | 80.0 (68.5, 95.0) 77.11 (67.95, 89.2) |
| Body Mass Index, Kg/m2, median (IQR) | 29.4 (25.8, 33.8) 26.3 (23.6, 29.4) | 28.8 (24.0, 33.0) 26.1 (22.9, 29.1) |
| Left Ventricular Ejection Fraction, (%) median (IQR) | 63.0 (56.0, 68.0) 55.0 (44.0, 63.0) | 61.0 (55.0, 66.0) 55.0 (45.6, 62.8) |
The essential performance of EchoGo Amyloidosis 1.0 was not evaluated on patients with prior heart transplantation.
Device performance was determined according to a retrospective case:control study including multiple sites spanning nine states in the USA. The final testing data cohort for patients >65 years of age with evidence of heart failure amounted to 1,164 patients, comprising 749 controls and 415 cases. A summary of the device performance for this population is provided in the table below. The EchoGo Amyloidosis 1.0 device correctly identified 310 true positives, and 569 true negatives, alongside 65 false positives and 57 false negatives. This equates to a sensitivity of 84.5% (95% C1: 80.3%, 88.5%), a specificity of 89.7% (95% Cl: 87.0%, 92.4%), a PPV of 82.7% (95% Cl: 78.8%, 86.5%) and NPV of 90.9% (95% CI: 88.8%, 93.2%).
| Statistic | Outcome
(95% CI) |
|----------------------------------------------------|-------------------------|
| Sensitivity | 84.5%
(80.3%, 88.5%) |
| Specificity | 89.7%
(87.0%, 92.4%) |
| Positive Predictive Value at
2.2% prevalence* | 15.6%
(11.0%, 20.8%) |
| Negative Predictive Value at
2.2% prevalence* | 99.6%
(99.5%, 99.7%) |
| Positive Predictive Value at
36.7% prevalence** | 82.7%
(78.8%, 86.5%) |
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| Statistic | Outcome
(95% CI) |
|----------------------------------------------------|-------------------------|
| Negative Predictive Value at
36.7% prevalence** | 90.9%
(88.8%, 93.2%) |
| Repeatability | |
| Positive Agreement | 100% |
| Negative Agreement | 100% |
| Reproducibility | |
| Positive Agreement | 85.5%
(82.4%, 88.2%) |
| Negative Agreement | 79.9%
(76.5%, 83.2%) |
| No-classifications | |
| Proportion of data | 14.0% |
*Estimate based on inferred disease prevalence of 2.2% using data re-sampling.
**Estimate based on native disease proportion within the collected testing dataset (36.7% prevalence).
A breakdown of the device performance by relevant patient sub-groups in a population of patients >65 years of age with evidence of heart failure is provided in table below:
| Variable | Sensitivity | Specificity | PPV Inferred at 2.2%
Prevalence |
|---------------------------------------------------------------------------|-------------------------|-------------------------|------------------------------------|
| Data Origin | | | |
| USA | 83.3% (78.3%, 87.8%) | 89.7% (87.0%, 92.4%) | 15.4% (12.4%, 19.7%) |
| Non-USA | 90.3% (83.0%, 97.7%) | N/A | N/A |
| Age Category
(years) | | | |
| >80 | 80.6% (72.1%, 88.9%) | 89.2% (85.2%, 92.9%) | 14.4% (10.8%, 20.7%) |
| Variable | Sensitivity | Specificity | PPV Inferred at 2.2% Prevalence |
| 71-80 | 88.3% (83.2%, 92.9%) | 91.5% (87.6%, 95.1%) | 19.0% (13.7%, 29.4%) |
| 61-70 | 79.4% (66.7%, 89.8%) | 86.7% (78.3%, 94.5%) | 11.8% (7.6%, 25.2%) |
| BMI Category
(kg/m2) | | | |
| >35 | 68.8% (38.5%, 92.3%) | 93.3% (87.9%, 97.9%) | 18.8% (9.5%, 42.1%) |
| 30-35 | 69.4% (52.0%, 84.6%) | 92.7% (87.2%, 97.3%) | 17.6% (10.2%, 36.3%) |
| 25-30 | 84.7% (76.9%, 92.3%) | 91.9% (87.5%, 95.8%) | 19.0% (13.1%, 30.2%) |
| 18.5-25 | 88.7% (81.3%, 95.0%) | 83.3% (76.1%, 89.6%) | 10.7% (7.7%, 16.4%) |
| 65 | 82.5% (70.5%, 93.0%) | 91.6% (86.8%, 95.5%) | 18.1% (12.2%, 29.2%) |
| Variable | Sensitivity | Specificity | PPV Inferred at 2.2% Prevalence |
| 50-65 | 86.1% (79.6%, 91.5%) | 89.3% (85.3%, 92.9%) | 15.4% (11.5%, 21.5%) |
| 40-50 | 86.2% (76.0%, 94.5%) | 90.0% (78.6%, 100.0%) | 16.2% (8.1%, 100.0%) |
| 16 | 94.2% (89.0%, 98.0%) | 84.2% (75.0%, 92.9%) | 11.8% (7.7%, 22.6%) |
| 14-16 | 88.5% (80.8%, 95.1%) | 86.4% (79.3%, 93.4%) | 12.7% (8.7%, 23.0%) |
| 12-14 | 79.5% (68.5%, 89.5%) | 86.8% (80.5%, 92.8%) | 11.9% (8.2%, 19.9%) |
| 10-12 | 80.0% (66.7%, 92.1%) | 96.7% (93.2%, 99.3%) | 35.2% (20.9%, 71.7%) |
| 8-10 | 50.0% (27.8%, 73.7%) | 94.2% (86.0%, 100.0%) | 16.3% (6.1%, 100.0%) |
| 6-8 | 60.0% (0.0%, 100.0%) | 100.0% (0.0%, 100.0%) | 100.0% (0.0%, 100.0%) |
| Inter-
Ventricular
Septum
Thickness
Category
(mm) | | | |
| >16 | 93.7% (89.1%, 97.6%) | 87.1% (80.0%, 93.5%) | 14.0% (9.6%, 24.3%) |
| 14-16 | 81.6% (72.7%, 90.1%) | 88.4% (81.8%, 94.3%) | 13.7% (9.1%, 24.4%) |
| 12-14 | 85.9% (75.5%, 95.6%) | 90.2% (84.2%, 95.4%) | 16.4% (10.5%, 30.1%) |
| 10-12 | 52.2% (29.4%, 75.0%) | 92.9% (85.7%, 98.5%) | 14.3% (5.9%, 45.0%) |
| 8-10 | 60.0% (25.0%, 100.0%) | 95.0% (81.2%, 100.0%) | 21.3% (5.3%, 100.0%) |
| 6-8 | 60.0% (0.0%, 100.0%) | 100.0% (0.0%, 100.0%) | 100.0% (0.0%, 100.0%) |
| Variable | Sensitivity | Specificity | PPV Inferred at 2.2%
Prevalence |
| Left
Ventricular
Mass Indexed
(g/m2) | | | |
| >95 | 85.0% (79.8%, 90.4%) | 89.2% (85.3%, 92.9%) | 15.0% (11.4%, 21.2%) |
| 80 | 16.7% | 69.0% (61.0%, 77.1%) | 73.5% (69.0%, 78.1%) |
| 71-80 | 11.0% | 80.8% (75.6%, 86.1%) | 79.6% (75.0%, 84.3%) |
| 61-70 | 15.0% | 66.7% (56.0%, 77.3%) | 74.3% (65.9%, 82.6%) |
| BMI Category (kg/m2) | | | |
| >35 | 9.9% | 61.1% (38.6%, 83.6%) | 84.2% (78.0%, 90.4%) |
| 30-35 | 16.4% | 58.1% (43.4%, 72.9%) | 77.4% (71.0%, 83.8%) |
| 25-30 | 13.3% | 73.5% (65.3%, 81.6%) | 79.7% (74.6%, 84.7%) |
| 18.5-25 | 15.2% | 78.2% (70.5%, 85.9%) | 68.9% (61.8%, 75.9%) |
| 65 | 18.0% | 69.1% (58.1%, 80.1%) | 74.6% (69.2%, 80.0%) |
| 50-65 | 12.9% | 76.0% (70.0%, 82.0%) | 77.3% (73.2%, 81.4%) |
| 40-50 | 10.3% | 78.9% (69.4%, 88.4%) | 78.3% (66.3%, 90.2%) |
| 16 | 12.9% | 83.7% (77.5%, 89.9%) | 71.1% (61.7%, 80.5%) |
| 14-16 | 13.1% | 78.7% (71.0%, 86.4%) | 73.6% (66.0%, 81.2%) |
| 12-14 | 13.9% | 72.5% (62.7%, 82.3%) | 72.7% (66.0%, 79.3%) |
| 10-12 | 12.1% | 69.2% (56.7%, 81.8%) | 85.4% (80.5%, 90.2%) |
| 8-10 | 14.9% | 42.3% (23.3%, 61.3%) | 80.3% (70.4%, 90.3%) |
| 6-8 | 17.6% | 42.9% (6.2%, 79.5%) | 90.0% (71.4%, 108.6%) |
| Inter-Ventricular
Septum Thickness
Category (mm) | | | |
| Variable | Proportion of
Uncertain Reports | Sensitivity*
(including uncertain
portion) | Specificity*
(including uncertain
portion) |
| >16 | 12.2% | 83.6% (78.2%, 89.1%) | 74.8% (67.5%, 82.1%) |
| 14-16 | 20.2% | 71.4% (63.1%, 79.8%) | 65.6% (58.0%, 73.1%) |
| 12-14 | 10.1% | 78.6% (69.0%, 88.2%) | 80.4% (74.0%, 86.8%) |
| 10-12 | 15.0% | 46.2% (27.0%, 65.3%) | 78.2% (70.2%, 86.3%) |
| 8-10 | 16.7% | 50.0% (21.7%, 78.3%) | 79.2% (62.9%, 95.4%) |
| 6-8 | 18.2% | 50.0% (10.0%, 90.0%) | 80.0% (44.9%, 115.1%) |
| Left Ventricular Mass
Indexed (g/m2) | | | |
| >95 | 12.2% | 75.2% (69.7%, 80.7%) | 77.9% (73.6%, 82.3%) |
|