(205 days)
Not Found
Yes
The device description explicitly states that the Viz HCM ECG Analysis Algorithm is a "machine learning-based software algorithm". Additionally, the "Mentions AI, DNN, or ML" section confirms the use of "machine learning techniques".
No
The device is intended to analyze ECGs to detect signs associated with hypertrophic cardiomyopathy (HCM) and identify patients for further follow-up, but it is not intended to provide therapy or replace standard diagnostic methods.
Yes
The device analyzes ECG recordings to detect signs associated with hypertrophic cardiomyopathy (HCM) and identifies patients for further HCM follow-up, which are functions of a diagnostic device. While it states it should not be used in-lieu of full patient evaluation or relied upon to make or confirm a diagnosis, its role in detecting signs and identifying patients for follow-up aligns with a diagnostic purpose.
Yes
The device is explicitly described as "Software as a Medical Device (SaMD)" and a "combination of software modules that consists of an ECG analysis software algorithm and mobile application software module." It analyzes ECG data collected from compatible ECG devices but does not include the ECG hardware itself.
Based on the provided information, the Viz HCM device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs analyze samples taken from the human body. The Viz HCM analyzes recordings of 12-lead ECGs, which are electrical signals measured from the body's surface, not a biological sample taken from within the body (like blood, urine, or tissue).
- The intended use is to analyze ECG recordings. The description clearly states that the device analyzes "recordings of 12-lead ECG made on compatible ECG devices."
- There is no mention of analyzing biological samples. The entire description focuses on the analysis of electrical signals from the heart.
Therefore, while it is a medical device that uses machine learning to aid in the identification of potential medical conditions, it does not meet the definition of an In Vitro Diagnostic.
No
The provided text does not contain any explicit statement that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
Viz HCM is intended to be used in parallel to the standard of care to analyze recordings of 12-lead ECG made on compatible ECG devices. Viz HCM is capable of analyzing the ECG, detecting signs associated with hypertrophic cardiomyopathy (HCM), and allowing the user to view the ECG and analysis results. Viz HCM is indicated for use on 12-lead ECG recordings collected from patients 18 years of age or older. Viz HCM is not intended for use on patients with implanted pacemakers. Viz HCM is limited to analysis of ECG data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis. Viz HCM identifies patients for further HCM follow-up and does not replace the current standard of care methods for diagnosis of HCM. The results of the device are not intended to rule-out HCM follow-up.
Product codes
QXO
Device Description
The Viz HCM ECG Analysis Algorithm (HCM Algorithm) is a machine learning-based software algorithm that analyzes 12-lead electrocardiograms (ECGs) for characteristics suggestive of hypertrophic cardiomyopathy (HCM). The mobile software module enables the end user to receive and toggle notifications for ECGs determined by the Viz HCM ECG Analysis Algorithm to contain signs suggestive of HCM.
The Viz HCM is a Software as a Medical Device (SaMD) intended to analyze ECG signals collected as part of a routine clinical assessment, independently and in parallel to the standard of care. Viz HCM is a combination of software modules that consists of an ECG analysis software algorithm and mobile application software module.
Mentions image processing
Not Found
Mentions AI, DNN, or ML
Cardiovascular machine learning-based notification software. Cardiovascular machine learning-based notification software employs machine learning techniques to suggest the likelihood of a cardiovascular disease or condition for further referral or diagnostic follow-up.
The Viz HCM ECG Analysis Algorithm (HCM Algorithm) is a machine learning-based software algorithm that analyzes 12-lead electrocardiograms (ECGs) for characteristics suggestive of hypertrophic cardiomyopathy (HCM).
The software documentation included a detailed description of the machine learning model, the model inputs and outputs, and the supported patient population.
Input Imaging Modality
12-lead ECG recordings
Anatomical Site
Cardiovascular
Indicated Patient Age Range
18 years of age or older.
Intended User / Care Setting
Not Found
Description of the training set, sample size, data source, and annotation protocol
The data for algorithm development was collected from different US and Non-US (OUS) sources. The data contains both HCM Positive (obstructive and nonobstructive) and HCM Negative examples including random ECG samples (random control) and enrichment for conditions differential for and associated with HCM (negative controls), The data is diverse with respect to the age, sex, and health status of the patient, as well as the data source from which the ECG data was taken. Efforts were made to sample the data from data sources from ethnically diverse regions so as to promote ethnic diversity in the training and internal validation datasets.
The algorithm development set includes 301,106 patients and 831,329 ECG exams. The data is then split into a training set (80%) and an internal validation set (20%) for algorithm development and internal evaluation.
The development dataset was split into disjoint and secured training and internal validation partitions. The partitions were created using a hash function on a patientanonymized unique identifier (patient UID) to ensure consistency across all development phases and across different models and datasets and prevents different studies of the same patient being allocated to more than one partition.
Description of the test set, sample size, data source, and annotation protocol
Model testing was performed on a dataset which was acquired from a retrospective study to assess the performance of Viz HCM in the identification of suspected HCM findings in ECG as compared to the clinical finding of HCM as established by cardiologist chart and imaging review of historical patient data.
A total of 3.196 (291 HCM-Positive and 2905 HCM-Negative) ECG cases were included in the performance assessment for the pivotal study. Patient cases were selected from 3 hospitals representing a combination of academic and community hospitals between July 1. 2017, and June 30, 2022. Two (2) of the three (3) sites providing data were from the Boston. Massachusetts area which are racially and ethnically diverse in terms of the local African American. Asian, and Multi-Race (two or more) populations. The third site was in Salem, Massachusetts which was predominantly Caucasian or Latino. All three sites provided data from geographic areas with similar proportions of Hispanic or Latino individuals in the local population, which were also similar to the proportion of the Hispanic or Latino population in the USA overall. The proportions of obstructive and non-obstructive HCM were roughly equal.
For each HCM-Positive or HCM-Negative case, a single cardiologist performed a chart and imaging review (where available) for the patient to confirm the presence of HCM according to predefined guidelines using either the Cornell criteria or the Sokolow-Lyon criteria. In addition, ECGs were annotated for the presence of different features and pathologies. ECGs determined to contain a pacemaker or corrupt lead were excluded during ECG annotations.
For HCM-Positive cases, the cardiologist assessed the patient chart and imaging to confirm the presence of HCM and if confirmed, the degree of obstruction (either non-obstructive or obstructive). HCM-Negative cases were reviewed for the presence or mention of HCM in the patient chart, along with any available imaging to rule out HCM. If the patient chart and imaging confirmed the presence of HCM, the patient was moved from the HCM-Negative cohort to the HCM-Positive cohort. As part of a secondary assessment, a selection of 60 cases (30 HCM-Positive cases and 30 HCM-Negative) were truthed by a second cardiologist to perform an analysis of agreement/consistency in confirmation of HCM.
In the study, the ICD-10 Code was used to sample HCM-Positive and HCM-Negative patients prior to truthing. During the truthing process, HCM-Negative patient cases were confirmed if there were no notes related to HCM in the patient chart. These would be determined as HCM-Negative by the lack of ICD-9/10 code for HCM as was the case with algorithm development. For HCM-Negative patient cases with available imaging or HCM-Positive cases, the additional chart review and review of imaging provided more confidence into the label with imaging evidence as established by the initial sampling of the ICD-9/10 codes associated with the patient diagnosis (or lack of HCM diagnosis).
Summary of Performance Studies
Study type: Retrospective clinical study
Sample size: 3196 (291 HCM-Positive and 2905 HCM-Negative) ECG cases.
Key metrics:
Sensitivity: 68.4% (95% CI: 62.8% - 73.5%)
Specificity: 99.1% (95% CI: 98.7% - 99.4%)
PPV (prevalence of 0.002): 13.7% (95% CI: 10.1% - 19.9%)
Subgroup analysis by site:
BWH: AUC 0.982 (0.971,0.990), True positive 82, False negative 29, Sensitivity 73.9 (65.0,81.2), True negative 957, False positive 11, Specificity 98.9 (98.0,99.4)
MGH: AUC 0.986 (0.980,0.992), True positive 75, False negative 32, Sensitivity 70.1 (60.8,78.0), True negative 974, False positive 4, Specificity 99.6 (98.9,99.9)
SH: AUC 0.948 (0.915,0.970), True positive 42, False negative 31, Sensitivity 57.5 (46.1,68.2), True negative 949, False positive 10, Specificity 99.0 (98.1,99.5)
Subgroup analysis by HCM type:
Obstructive: True positive 97, False negative 45, Sensitivity 68.3 (60.2,75.4)
Non-obstructive: True positive 102, False negative 47, Sensitivity 68.5 (60.6,75.4)
Subgroup analysis by gender:
Female: AUC 0.979 (0.971,0.985), True positive 85, False negative 54, Sensitivity 61.2 (52.8,68.9), True negative 1494, False positive 7, Specificity 99.5 (99.0,99.8)
Male: AUC 0.971 (0.954,0.984), True positive 114, False negative 38, Sensitivity 75.0 (67.5,81.2), True negative 1386, False positive 18, Specificity 98.7 (98.0,99.2)
Subgroup analysis by age:
65 years: AUC 0.973 (0.959,0.983), True positive 83, False negative 39, Sensitivity 68.0 (59.3,75.7), True negative 1229, False positive 12, Specificity 99.0 (98.3,99.5)
Subgroup analysis by race:
American Indian or Alaska Native: AUC -, True positive 0, False negative 0, Sensitivity -, True negative 2, False positive 0, Specificity 100.0 (29.0,105.2)
Asian: AUC 0.987 (0.962,0.999), True positive 11, False negative 4, Sensitivity 73.3 (47.6,89.5), True negative 93, False positive 1, Specificity 98.9 (93.6,100.4)
Black: AUC 0.981 (0.961,0.994), True positive 20, False negative 11, Sensitivity 64.5 (46.9,79.0), True negative 262, False positive 2, Specificity 99.2 (97.1,100.0)
Native Hawaiian or Other Pacific Islander: AUC -, True positive 0, False negative 0, Sensitivity -, True negative 1, False positive 0, Specificity 100.0 (16.7,103.9)
White: AUC 0.972 (0.959,0.981), True positive 149, False negative 74, Sensitivity 66.8 (60.4,72.7), True negative 2222, False positive 21, Specificity 99.1 (98.6,99.4)
Other: AUC 1.000 (0.997,1.000), True positive 9, False negative 0, Sensitivity 100.0 (65.5,104.5), True negative 192, False positive 1, Specificity 99.5 (96.8,100.2)
Two or More: AUC 1.000 (1.000,1.000), True positive 3, False negative 1, Sensitivity 75.0 (28.9,96.6), True negative 21, False positive 0, Specificity 100.0 (81.8,102.8)
Declined: AUC 1.000 (0.956,1.000), True positive 3, False negative 1, Sensitivity 75.0 (28.9,96.6), True negative 34, False positive 0, Specificity 100.0 (87.9,101.9)
Subgroup analysis by ethnicity:
Hispanic: AUC 1.000 (0.995,1.000), True positive 7, False negative 0, Sensitivity 100.0 (59.6,105.0), True negative 167, False positive 3, Specificity 98.2 (94.7,99.6)
Non Hispanic: AUC 0.973 (0.962,0.982), True positive 166, False negative 80, Sensitivity 67.5 (61.4,73.0), True negative 2485, False positive 21, Specificity 99.2 (98.7,99.5)
Declined: AUC 0.989 (0.964,1.000), True positive 11, False negative 6, Sensitivity 64.7 (41.2,82.8), True negative 120, False positive 0, Specificity 100.0 (96.3,100.6)
Unknown: AUC 0.964 (0.923,0.990), True positive 15, False negative 6, Sensitivity 71.4 (49.8,86.4), True negative 108, False positive 1, Specificity 99.1 (94.5,100.3)
Key results: The study assessed the performance of the device in terms of sensitivity, specificity, and device positive value (PPV), and conducted additional analyses to assess the device performance for detection of suspected HCM in different sub-populations.
Key Metrics
Sensitivity: 68.4% (95% CI: 62.8% - 73.5%)
Specificity: 99.1% (95% CI: 98.7% - 99.4%)
PPV (prevalence of 0.002): 13.7% (95% CI: 10.1% - 19.9%)
Predicate Device(s)
Not Found
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
N/A
0
DE NOVO CLASSIFICATION REQUEST FOR VIZ HCM
REGULATORY INFORMATION
FDA identifies this generic type of device as:
Cardiovascular machine learning-based notification software. Cardiovascular machine learning-based notification software employs machine learning techniques to suggest the likelihood of a cardiovascular disease or condition for further referral or diagnostic follow-up. The software identifies a single condition based on one or more non-invasive physiological inputs as part of routine medical care. It is intended as the basis for further testing and is not intended to provide diagnostic quality output. It is not intended to identify or detect arrhythmias.
NEW REGULATION NUMBER: 21 CFR 870.2380
CLASSIFICATION: Class II
PRODUCT CODE: QXO
BACKGROUND
DEVICE NAME: Viz HCM
SUBMISSION NUMBER: DEN230003
DATE DE NOVO RECEIVED: January 10, 2023
SPONSOR INFORMATION:
Viz.ai, Inc. 201 Mission St., 12th Floor San Francisco, California 94105
INDICATIONS FOR USE
The Viz HCM is indicated as follows:
Viz HCM is intended to be used in parallel to the standard of care to analyze recordings of 12-lead ECG made on compatible ECG devices. Viz HCM is capable of analyzing the ECG, detecting signs associated with hypertrophic cardiomyopathy (HCM), and allowing the user to view the ECG and analysis results. Viz HCM is indicated for use on 12-lead ECG recordings collected from patients 18 years of age or older. Viz HCM is not intended for use on patients with implanted pacemakers. Viz HCM is limited to analysis of ECG data and should not be used in-lieu of full patient evaluation or relied upon to
1
make or confirm diagnosis. Viz HCM identifies patients for further HCM follow-up and does not replace the current standard of care methods for diagnosis of HCM. The results of the device are not intended to rule-out HCM follow-up.
LIMITATIONS
The sale, distribution, and use of the Viz HCM are restricted to prescription use in accordance with 21 CFR 801.109.
The device identifies patients for further HCM follow-up and does not replace the current standard of care methods for diagnosis of HCM. The results of the device are not intended to rule-out HCM follow-up.
The device is not intended for use on patients with implanted pacemakers.
The results of the device should not be used in lieu of full patient evaluation.
PLEASE REFER TO THE LABELING FOR A COMPLETE LIST OF WARNINGS, PRECAUTIONS AND CONTRAINDICATIONS.
DEVICE DESCRIPTION
The Viz HCM ECG Analysis Algorithm (HCM Algorithm) is a machine learning-based software algorithm that analyzes 12-lead electrocardiograms (ECGs) for characteristics suggestive of hypertrophic cardiomyopathy (HCM). The mobile software module enables the end user to receive and toggle notifications for ECGs determined by the Viz HCM ECG Analysis Algorithm to contain signs suggestive of HCM.
Image /page/1/Figure/9 description: This image is a flowchart outlining the steps for a Viz HCM analysis. The process begins with a review of the patient's ECG, which is then analyzed by Viz HCM. The results are available on a mobile interface. The flowchart continues with steps such as patient assessment, review of patient history, physical exams, and follow-up imaging, ultimately leading to a determination of whether HCM is confirmed and further patient assessment for risk factors and treatment.
Figure 1. Clinical Workflow Diagram
2
The Viz HCM is a Software as a Medical Device (SaMD) intended to analyze ECG signals collected as part of a routine clinical assessment, independently and in parallel to the standard of care. Viz HCM is a combination of software modules that consists of an ECG analysis software algorithm and mobile application software module.
SUMMARY OF NONCLINICAL/BENCH STUDIES
Nonclinical studies conducted for the Viz HCM system are summarized below.
SOFTWARE
The software was reviewed according to the "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" dated May 11, 2005. Appropriate software documentation consistent with a "Moderate" level of software concern were provided.
The software documentation included a detailed description of the machine learning model, the model inputs and outputs, and the supported patient population. Integration testing was conducted in the intended software system. Testing accounted for the impact of and variability between different ECG acquisition hardware. The ECG hardware controls included a description of input ECG signal control measures and mitigations for user error and system components on output accuracy.
Cybersecurity was reviewed in accordance with the FDA guidance document "Content of Premarket Submissions for Management of Cybersecurity in Medical Devices" dated October 2, 2014.
PERFORMANCE TESTING-MODEL DEVELOPMENT AND INTERNAL VALIDATION
The data for algorithm development was collected from different US and Non-US (OUS) sources. The data contains both HCM Positive (obstructive and nonobstructive) and HCM Negative examples including random ECG samples (random control) and enrichment for conditions differential for and associated with HCM (negative controls), The data is diverse with respect to the age, sex, and health status of the patient, as well as the data source from which the ECG data was taken. Efforts were made to sample the data from data sources from ethnically diverse regions so as to promote ethnic diversity in the training and internal validation datasets.
3
Image /page/3/Figure/0 description: The image shows a diagram of an algorithm development set. The algorithm development set includes 301,106 patients and 831,329 ECG exams. The set is divided into HCM positive patients (4,470) and HCM negative patients (298,394). The data is then split into a training set (80%) and an internal validation set (20%) for algorithm development and internal evaluation.
Figure 2. Distribution and separation of training and internal validation data
Refer to Table 9 in the labeling section of this document for full demographics of training and internal validation datasets.
The development dataset was split into disjoint and secured training and internal validation partitions. The partitions were created using a hash function on a patientanonymized unique identifier (patient UID) to ensure consistency across all development phases and across different models and datasets and prevents different studies of the same patient being allocated to more than one partition.
PERFORMANCE TESTING - USABILITY
The usability of the Viz HCM system was assessed per EN 62366-1 "Medical Devices-Part 1: Application of usability engineering to medical devices" dated 2015 + A1:2020 and the FDA guidance document "Applying Human Factors and Usability Engineering to Medical Devices". A use-related risk analysis (URRA) and system risk analysis (RA) were performed to identify use-related hazards and critical tasks. The study recruited 16 representative clinical users who were assessed within two (2) use scenarios for task completion according to correct use, user error, close calls, or use difficulty.
- . There were no use errors during the study
- All participants demonstrated full understanding of the intended use, device . output (i.e., the HCM flag), and that they would not rely on the device to make a diagnosis
- . 15/16 (93%) participants acknowledged the HCM flag during the session to review the ECG record
This testing demonstrated that the intended users of the product can perform the product's intended use in the expected use environment. It also demonstrated that the intended users could adequately comprehend the labeling.
SUMMARY OF CLINICAL INFORMATION
4
Overview
Model testing was performed on a dataset which was acquired from a retrospective study to assess the performance of Viz HCM in the identification of suspected HCM findings in ECG as compared to the clinical finding of HCM as established by cardiologist chart and imaging review of historical patient data. The objectives of the study were to assess the performance of the device in terms of sensitivity, specificity, and device positive value (PPV), and conduct additional analyses to assess the device performance for detection of suspected HCM in different sub-populations. This was a non-significant risk study using historical patient data and did not involve enrollment of any human beings. Due to the retrospective nature of the study, no adverse events were expected or observed. The Institutional Review Board (IRB) overseeing the study at participating hospitals waived the need for patient informed consent.
A total of 3.196 (291 HCM-Positive and 2905 HCM-Negative) ECG cases were included in the performance assessment for the pivotal study. Patient cases were selected from 3 hospitals representing a combination of academic and community hospitals between July 1. 2017, and June 30, 2022. Two (2) of the three (3) sites providing data were from the Boston. Massachusetts area which are racially and ethnically diverse in terms of the local African American. Asian, and Multi-Race (two or more) populations. The third site was in Salem, Massachusetts which was predominantly Caucasian or Latino. All three sites provided data from geographic areas with similar proportions of Hispanic or Latino individuals in the local population, which were also similar to the proportion of the Hispanic or Latino population in the USA overall. The proportions of obstructive and non-obstructive HCM were roughly equal. See Table 2 for full demographic information.
TRUTHING PROCESS
For each HCM-Positive or HCM-Negative case, a single cardiologist performed a chart and imaging review (where available) for the patient to confirm the presence of HCM according to predefined guidelines using either the Cornell criteria or the Sokolow-Lyon criteria. In addition, ECGs were annotated for the presence of different features and pathologies. ECGs determined to contain a pacemaker or corrupt lead were excluded during ECG annotations.
For HCM-Positive cases, the cardiologist assessed the patient chart and imaging to confirm the presence of HCM and if confirmed, the degree of obstruction (either non-obstructive or obstructive). HCM-Negative cases were reviewed for the presence or mention of HCM in the patient chart, along with any available imaging to rule out HCM. If the patient chart and imaging confirmed the presence of HCM, the patient was moved from the HCM-Negative cohort to the HCM-Positive cohort. As part of a secondary assessment, a selection of 60 cases (30 HCM-Positive cases and 30 HCM-Negative) were truthed by a second cardiologist to perform an analysis of agreement/consistency in confirmation of HCM.
In the study, the ICD-10 Code was used to sample HCM-Positive and HCM-Negative patients prior to truthing. During the truthing process, HCM-Negative patient cases were confirmed if there were no notes related to HCM in the patient chart. These would be determined as HCM-Negative by the lack of ICD-9/10 code for HCM as was the case with algorithm development. For HCM-Negative patient cases with available imaging or HCM-Positive cases, the additional chart review and review of imaging provided more confidence into the label with imaging
5
evidence as established by the initial sampling of the ICD-9/10 codes associated with the patient diagnosis (or lack of HCM diagnosis).
The study results are in Table 1 below.
| Performance Measure | Results |
|---|---|
| Sensitivity | 68.4% (95% CI: 62.8% - 73.5%) |
| Specificity | 99.1% (95% CI: 98.7% - 99.4%) |
| PPV (prevalence of 0.002) | 13.7% (95% CI: 10.1% - 19.9%) |
Table 1. Clinical Testing Results
Refer to Table 9 for full demographics of training and internal validation datasets.
In addition, subgroup analyses were conducted by:
- Hospital site .
- ECG device make/model .
- . Gender
- . Age
- Race .
- . Ethnicity
- HCM Characterization (i.e., obstructive vs. non-obstructive) .
| Subgroup | AUC
(95% CI) | True
positive | False
negative | Sensitivity
(95% CI) | True
negative | False
positive | Specificity
(95% CI) |
|----------|------------------------|------------------|-------------------|-------------------------|------------------|-------------------|-------------------------|
| BWH | 0.982
(0.971,0.990) | 82 | 29 | 73.9
(65.0,81.2) | 957 | 11 | 98.9
(98.0,99.4) |
| MGH | 0.986
(0.980,0.992) | 75 | 32 | 70.1
(60.8,78.0) | 974 | 4 | 99.6
(98.9,99.9) |
| SH | 0.948
(0.915,0.970) | 42 | 31 | 57.5
(46.1,68.2) | 949 | 10 | 99.0
(98.1,99.5) |
Table 2: Subgroup Analysis by Site
| Subgroup | True
positive | False
negative | Sensitivity
(95% CI) |
|-----------------|------------------|-------------------|-------------------------|
| Obstructive | 97 | 45 | 68.3
(60.2,75.4) |
| Non-obstructive | 102 | 47 | 68.5
(60.6,75.4) |
Table 3: Subgroup Analysis by HCM type
6
| Subgroup | AUC
(95% CI) | True
positive | False
negative | Sensitivity
(95% CI) | True
negative | False
positive | Specificity
(95% CI) |
|----------|------------------------|------------------|-------------------|-------------------------|------------------|-------------------|-------------------------|
| Female | 0.979
(0.971,0.985) | 85 | 54 | 61.2
(52.8,68.9) | 1494 | 7 | 99.5
(99.0,99.8) |
| Male | 0.971
(0.954,0.984) | 114 | 38 | 75.0
(67.5,81.2) | 1386 | 18 | 98.7
(98.0,99.2) |
Table 4: Subgroup Analysis by gender
| Subgroup | AUC
(95% CI) | True
positive | False
negative | Sensitivity
(95% CI) | True
negative | False
positive | Specificity
(95% CI) |
|----------------------------------------------|------------------------|------------------|-------------------|-------------------------|------------------|-------------------|-------------------------|
| 65 years | 0.973
(0.959,0.983) | 83 | 39 | 68.0
(59.3,75.7) | 1229 | 12 | 99.0
(98.3,99.5) |
| Table 5: Subgroup Analysis by age | | | | | | | |
| Subgroup | AUC
(95% CI) | True
positive | False
negative | Sensitivity
(95% CI) | True
negative | False
positive | Specificity
(95% CT) |
| American Indian or
Alaska Native | - | 0 | 0 | - | 2 | 0 | 100.0
(29.0.105.2) |
| Asian | 0.987
(0.962.0.999) | 11 | 4 | 73.3
(47.6,89.5) | 93 | 1 | 98.9
(93.6.100.4) |
| Black | 0.981
(0.961,0.994) | 20 | 11 | 64.5
(46.9,79.0) | 262 | 2 | 99.2
(97.1,100.0) |
| Native Hawaiian or
Other Pacific Islander | - | 0 | 0 | - | 1 | 0 | 100.0
(16.7.103.9) |
| White | 0.972
(0.959,0.981) | 149 | 74 | 66.8
(60.4,72.7) | 2222 | 21 | 99.1
(98.6,99.4) |
| Other | 1.000
(0.997,1.000) | 9 | 0 | 100.0
(65.5.104.5) | 192 | 1 | 99.5
(96.8.100.2) |
| Two or More | 1.000
(1.000,1.000) | 3 | 1 | 75.0
(28.9,96.6) | 21 | 0 | 100.0
(81.8,102.8) |
| Declined | 1.000
(0.956,1.000) | 3 | 1 | 75.0
(28.9,96.6) | 34 | 0 | 100.0
(87.9.101.9) |
| Table 6: Subgroup Analysis by race | |
|---|---|
| ------------------------------------ | -- |
l
80.0
(36.0,98.0)
53
0
4
0-996
(0.966,1.000)
Unknown
100.0
(91.9,101.3)
7
| Subgroup | AUC
(95% CI) | True
positive | False
negative | Sensitivity
(95% CI) | True
negative | False
positive | Specificity
(95% CI) |
|--------------|------------------------|------------------|-------------------|-------------------------|------------------|-------------------|-------------------------|
| Hispanic | 1.000
(0.995,1.000) | 7 | 0 | 100.0
(59.6,105.0) | 167 | 3 | 98.2
(94.7,99.6) |
| Non Hispanic | 0.973
(0.962,0.982) | 166 | 80 | 67.5
(61.4,73.0) | 2485 | 21 | 99.2
(98.7,99.5) |
| Declined | 0.989
(0.964,1.000) | 11 | 6 | 64.7
(41.2,82.8) | 120 | 0 | 100.0
(96.3,100.6) |
| Unknown | 0.964
(0.923,0.990) | 15 | 6 | 71.4
(49.8,86.4) | 108 | 1 | 99.1
(94.5,100.3) |
Table 7: Subgroup Analysis by ethnicity
| Subgroup | True
positive | False
negative | Sensitivity
(95% CI) |
|-----------------|------------------|-------------------|-------------------------|
| Obstructive | 97 | 45 | 68.3
(60.2,75.4) |
| Non-obstructive | 102 | 47 | 68.5
(60.6,75.4) |
Table 8: Subgroup Analysis by HCM type
Further stratification was conducted for:
- Concurrent ECG findings and anomalies, and .
- Comorbid conditions .
Pediatric Extrapolation
For medical devices, the FD&C Act defines patients before their 22nd birthday as pediatric patients. In this De Novo request, data from patients between 18-21 were used to support the use of the device in patients over the age of 18.
LABELING
The labeling supports the decision, including information on all required and/or compatible parts, to grant the De Novo request for this device. The labeling includes a detailed description of the device, description of the patient population for which the device is indicated for use, a description of the intended user population, and instructions for use.
The labeling reflects the following critical intended use and limitations of the Viz HCM device:
- The device identifies patients for further HCM follow-up and does not replace the current . standard of care methods for diagnosis of HCM. The results of the device are not intended to rule-out HCM follow-up.
- The device is not intended for use on patients with implanted pacemakers. .
- The results of the device should not be used in lieu of full patient evaluation. .
8
The following baseline demographic information is included in the labeling for age, race, ethnicity, sex, and HCM type.
| Training (N=301,106) | Testing (N=3196) | ||||
|---|---|---|---|---|---|
| Sex | Sex | ||||
| Female | 166,885 | 55.4% | Female | 1640 | 51.3% |
| Male | 134,221 | 44.6% | Male | 1556 | 48.7% |
| Age | Age | ||||
| 65 | 100,456 | 33.4% | > 65 | 1363 | 42.6% |
| Race | Race | ||||
| American Indian / | |||||
| Alaska Native | *Data collected from multiple regions | ||||
| (USA and OUS) representing different | |||||
| races: |
- United States
- Israel
- Germany
- Brazil | | American Indian /
Alaska Native | 2 | 0.1% |
| Asian | | | Asian | 109 | 3.4% |
| Black | | | Black | 295 | 9.2% |
| Native Hawaiian /
Pacific Islander | | | Native Hawaiian /
Pacific Islander | 1 |