K230842 · Implicity, Inc. · QNL · Oct 25, 2023 · Cardiovascular
Device Facts
Record ID
K230842
Device Name
SignalHF (IM008)
Applicant
Implicity, Inc.
Product Code
QNL · Cardiovascular
Decision Date
Oct 25, 2023
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 870.2210
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K230842 · Oct 25, 2023
SignalHF (IM008)
Implicity, Inc.
SNDS (Système National des Données de Santé) French national health database; Implicity proprietary databases (routine clinical remote monitoring data)
The FORESEE-HF retrospective study was used to evaluate the performance of the SignalHF algorithm in detecting signs of worsening heart failure using real-world data from patients with various CIEDs.
Patients over 18 years old implanted with ICD, CRT-D, PM, or CRT-P devices (Medtronic, Boston Scientific, Biotronik) with at least one remote monitoring data transmission.; Sample Size: 17,974
Not applicable for this study
Sensitivity for detecting HF hospitalizations, Unexplained alert rate per patient-year, Lower quartile on alerting time (days).
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Heart Failure hospitalization risk
—
Sensitivity > 40% for ICD/CRT-D; Sensitivity > 30% for Pacemaker/CRT-P; Unexplained alert rate < 2.0 PPY; Lower quartile on alerting time > 15 days
Sensitivity 59.8% [54.0%; 65.4%] for ICD/CRT-D; Sensitivity 45.9% [38.1%; 53.8%] for Pacemaker/CRT-P; Unexplained alert rate 0.654 PPY for ICD/CRT-D; Unexplained alert rate 0.470 PPY for Pacemaker/CRT-P; Lower quartile alerting time 35.0 days for ICD/CRT-D; Lower quartile alerting time 37 days for Pacemaker/CRT-P
FORESEE-HF Study (Clinical cohort): 6,740 patients
—
Indications for Use
The SignalHF System is intended for use by qualified healthcare professionals (HCP) managing patients over 18 years old who are receiving physiological monitoring for Heart Failure surveillance and implanted with a compatible Cardiac Implantable Electronic Devices (CIED) (i.e., compatible pacemakers, ICDs, and CRTs). The SignalHF System provides additive information to use in conjunction with standard clinical evaluation. The SignalHF HF Score is intended to calculate the risk of HF for a patient in the next 30 days. This System is intended for adjunctive use with other physiological vital signs and patient symptoms and is not intended to independently direct therapy.
Device Story
SignalHF is a cloud-based SaMD for Heart Failure (HF) surveillance. It ingests physiologic data from remote CIED transmissions (activity, atrial burden, heart rate variability, heart rate, thoracic impedance, PVCs) and patient demographics. A proprietary algorithm calculates a 'SignalHF HF Score' representing the risk of HF-related hospitalization within the next 30 days. The system provides alerts based on fixed alert and recovery thresholds. Used by healthcare professionals (HCPs) in clinical settings to monitor chronic HF worsening. Output is displayed on compatible platforms (e.g., Implicity). HCPs use the score as adjunctive information alongside standard clinical evaluation to guide patient management. The device aims to provide early warning of decompensation, potentially reducing hospitalizations.
Clinical Evidence
Non-interventional retrospective study (FORESEE-HF) using 17,974 patients (2017-2021) from the French national health database (SNDS) and proprietary data. Evaluated sensitivity for HF hospitalization, unexplained alert rate (UAR), and alerting time. Results: ICD/CRT-D sensitivity 59.8%, UAR 0.654 PPY, alerting time 35 days. Pacemaker/CRT-P sensitivity 45.9%, UAR 0.470 PPY, alerting time 37 days. All primary endpoints met for most device configurations.
Technological Characteristics
SaMD cloud-based service. Inputs: CIED physiologic data (impedance, HR, activity, etc.) and demographics. Processing: Machine learning algorithm for risk scoring. Connectivity: Networked/Cloud. Software level of concern: Moderate.
Indications for Use
Indicated for patients >18 years old with compatible CIEDs (pacemakers, ICDs, CRTs) receiving physiological monitoring for Heart Failure surveillance. Adjunctive use only; not for independent therapy direction.
Regulatory Classification
Identification
The adjunctive predictive cardiovascular indicator is a prescription device that uses software algorithms to analyze cardiovascular vital signs and predict future cardiovascular status or events. This device is intended for adjunctive use with other physical vital sign parameters and patient information and is not intended to independently direct therapy.
Special Controls
*Classification.* Class II (special controls). The special controls for this device are:(1) A software description and the results of verification and validation testing based on a comprehensive hazard analysis and risk assessment must be provided, including:
(i) A full characterization of the software technical parameters, including algorithms;
(ii) A description of the expected impact of all applicable sensor acquisition hardware characteristics and associated hardware specifications;
(iii) A description of sensor data quality control measures;
(iv) A description of all mitigations for user error or failure of any subsystem components (including signal detection, signal analysis, data display, and storage) on output accuracy;
(v) A description of the expected time to patient status or clinical event for all expected outputs, accounting for differences in patient condition and environment; and
(vi) The sensitivity, specificity, positive predictive value, and negative predictive value in both percentage and number form.
(2) A scientific justification for the validity of the predictive cardiovascular indicator algorithm(s) must be provided. This justification must include verification of the algorithm calculations and validation using an independent data set.
(3) A human factors and usability engineering assessment must be provided that evaluates the risk of misinterpretation of device output.
(4) A clinical data assessment must be provided. This assessment must fulfill the following:
(i) The assessment must include a summary of the clinical data used, including source, patient demographics, and any techniques used for annotating and separating the data.
(ii) The clinical data must be representative of the intended use population for the device. Any selection criteria or sample limitations must be fully described and justified.
(iii) The assessment must demonstrate output consistency using the expected range of data sources and data quality encountered in the intended use population and environment.
(iv) The assessment must evaluate how the device output correlates with the predicted event or status.
(5) Labeling must include:
(i) A description of what the device measures and outputs to the user;
(ii) Warnings identifying sensor acquisition factors that may impact measurement results;
(iii) Guidance for interpretation of the measurements, including a statement that the output is adjunctive to other physical vital sign parameters and patient information;
(iv) A specific time or a range of times before the predicted patient status or clinical event occurs, accounting for differences in patient condition and environment;
(v) Key assumptions made during calculation of the output;
(vi) The type(s) of sensor data used, including specification of compatible sensors for data acquisition;
(vii) The expected performance of the device for all intended use populations and environments; and
(viii) Relevant characteristics of the patients studied in the clinical validation (including age, gender, race or ethnicity, and patient condition) and a summary of validation results.
Predicate Devices
Acumen™ Hypotension Prediction Index (HPI) (K203224)
Submission Summary (Full Text)
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Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
October 25, 2023
Implicity Inc. Caroline Florequin - Head of Quality and Reguatory Affairs 185 Alewife Brook Parkway: Suite 210 Cambridge, Massachusetts 02138
Re: K230842
Trade/Device Name: SignalHF Regulation Number: 21 CFR 870.2210 Regulation Name: Adjunctive Predictive Cardiovascular Indicator Regulatory Class: Class II Product Code: QNL Dated: September 25, 2023 Received: September 25, 2023
Dear Caroline Florequin:
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.
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).
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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.
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-regulatoryassistance/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
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# Indications for Use
510(k) Number (if known) K230842
Device Name SignalHF
### Indications for Use (Describe)
The SignalHF System is intended for use by qualified healthcare professionals (HCP) managing patients over 18 years old who are receiving physiological monitoring for Heart Failure surveillance and implanted with a compatible Cardiac Implantable Electronic Devices (CIED) (i.e., compatible pacemakers, ICDs, and CRTs).
The SignalHF System provides additive information to use in conjunction with standard clinical evaluation.
The SignalHF HF Score is intended to calculate the risk of HF for a patient in the next 30 days.
This System is intended for adjunctive use with other physiological vital signs and patient symptoms and is not intended to independently direct therapy.
Type of Use (Select one or both, as applicable)
X Prescription Use (Part 21 CFR 801 Subpart D)
| Over-The-Counter Use (21 CFR 801 Subpart C)
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# 510(k) Summary
This summary of 510(k) safety and effectiveness information is submitted in accordance with the requirements of 21 CFR 807.92
#### Submitter 1
| Applicant | IMPLICITY INC<br>185 ALEWIFE BROOK PARKWAY - SUITE 210<br>CAMBRIDGE, MA 02138<br>USA<br>Phone: +33 6 76 731 731 |
|----------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Contact Person | Caroline Florequin<br>Head of QARA<br>IMPLICITY INC<br>185 ALEWIFE BROOK PARKWAY -<br>SUITE 210 CAMBRIDGE, MA 02138<br>USA<br>caroline.florequin@implicity.com<br>Phone: +33 6 66 21 35 91 |
| Assisted by | Mark Johnson<br>Regulatory consultant to Implicity Inc<br>MJ Medtech Consulting Services LLC<br>4587 Canvasback rd<br>Blaine, Washington<br>98230<br>+1 (503) 575-5886 |
| Date prepared:<br>510(k) number: | October, 25th 2023<br>K230842 |
| 2 Device Information | |
| Trade Name | SignalHF |
| Common Name | IM008 |
| Classification | 21CFR- 870.2210 - Adjunctive predictive cardiovascula |
indicator, Class II – Medium
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Product Code QNL
# 3 Predicate Device
The predicate and reference device for SignalHF are:
Acumen™ Hypotension Prediction Index (HPI), Edwards Lifesciences, LLC K203224 Predicate Device
| | Subject Device | Predicate Device | Comparison<br>to<br>Predicate Device |
|-----------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Device Name | SignalHF | Acumen™ Hypotension<br>Prediction Index (HPI) | |
| Manufacturer | IMPLICITY INC | Edwards Lifesciences, LLC | |
| 510(k) # | | K203224 | |
| Regulation<br>Number | 21 CFR 870.2210 | 21 CFR 870.2210 | Same |
| Class | II | II | Same |
| Device Class /<br>Name | Medium term adjunctive<br>predictive cardiovascular<br>indicator. | Adjunctive predictive<br>cardiovascular indicator. | Same |
| Product Code | QNL | QAQ | Similar, Product<br>codes are not the<br>same, but similar.<br>Both devices are<br>SaMD. Incoming<br>data is similar but<br>not the same.<br>Product Code QAQ<br>specifically identifies<br>hypotension as the<br>target area. |
| Product<br>Code<br>Device<br>Definition | Medium-Term Adjunctive<br>Predictive Cardiovascular<br>Indicator<br>The adjunctive predictive<br>cardiovascular indicator is a<br>prescription device that uses<br>software algorithms to<br>analyze cardiovascular vital | Adjunctive Predictive<br>Cardiovascular Indicator<br>The adjunctive predictive<br>cardiovascular indicator is<br>a prescription device that<br>uses software algorithms<br>to analyze cardiovascular<br>vital signs and predict | Same |
| | signs and predict future<br>cardiovascular status or<br>events within a defined<br>medium-term period. This<br>device is intended for<br>adjunctive use with other<br>physical vital sign parameters<br>and patient information and is<br>not intended to<br>independently direct therapy. | future cardiovascular<br>status or events. This<br>device is intended for<br>adjunctive use with other<br>physical vital sign<br>parameters and patient<br>information and is not<br>intended to independently direct<br>therapy. | |
| Software<br>Level of<br>Concern | Moderate | Moderate | Same |
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#### Device Description 4
SignalHF is a software as medical device (SaMD) that uses a proprietary and validated algorithm, the SignalHF HF Score, to calculate the risk of a future worsening condition related to Heart Failure (HF). The algorithm computes this HF score using data obtained from (i) a diverse set of physiologic measures generated in the patient's remotely accessible pre-existing cardiac implant (activity, atrial burden, heart rate variability, heart rate, heart rate at rest, thoracic impedance (for fluid retention), and premature ventricular contractions per hour), and (ii) his/her available Personal Health Records (demographics). SignalHF provides information regarding the patient's health status (like a patient's stable HF condition) and also provides alerts based on the SignalHF HF evaluation. Based on an alert and a recovery threshold on the SignalHF score established during the learning phase of the algorithm and fixed for all patients, our monitoring system is expected to raise an alert 30 days (on median) before a predicted HF hospitalization event (see Figure 1).
Image /page/5/Figure/5 description: The image shows a graph titled "HF Watch Risk score of hospitalization for Heart Failure within the next 30 Days". The graph plots the HF score over time, with the x-axis representing dates from June to December and the y-axis representing the HF score from 0 to 100. The HF score is shown as a blue line, and there are also dashed lines representing the alert threshold at 50 and the recovery threshold at 30. The text indicates that the HFWatch score has crossed the alert threshold and is still above the recovery threshold since November 3, 2022, indicating a high risk of hospitalization within the next 30 days.
Figure 1. An example of a graph displaying the SignalHF score over time, crossing the alert threshold.
SignalHF does not provide a real-time alert. Rather, it is designed to detect chronic worsening of HF status. SignalHF is designed to provide a score linked to the probability of a future decompensated heart failure event specific to each patient. Using this adjunctive information, healthcare professionals can make adjustments for the patient based on their clinical judgement and expertise.
The score and score-based alerts provided through SignalHF can be displayed on any compatible HF monitoring platform, including the Implicity platform. The healthcare professional (HCP) can utilize the
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SignalHF HF score as adjunct information when monitoring CIED patients with remote monitoring capabilities.
The HCP's decision is not based solely on the device data which serves as adjunct information, but rather on the full clinical and medical picture and record of the patient.
#### Indication for Use 5
### Indication for use :
The SignalHF System is intended for use by qualified healthcare professionals (HCP) managing patients over 18 years old who are receiving physiological monitoring for Heart Failure surveillance and implanted with a compatible Cardiac Implantable Electronic Devices (CIED) (i.e., compatible pacemakers, ICDs, and CRTs). The SignalHF System provides additive information to use in conjunction with standard clinical evaluation. The SignalHF HF Score is intended to calculate the risk of HF for a patient in the next 30 days.
This System is intended for adjunctive use with other physiological vital signs and patient symptoms and information and is not intended to independently direct therapy.
### Indication for use of predicate:
The Edwards Lifesciences Acumen Hypotension Prediction Index feature provides the clinician with physiological insight into a patient's likelihood of future hypotensive events (defined as mean arterial pressure < 65 mmHg for at least one minute in duration) and the associated hemodynamics.
The Acumen HPI feature is intended for use in surgical patients receiving advanced hemodynamic monitoring.
The Acumen HPI feature is considered to be additional quantitative information regarding the patient's physiological condition for reference only and no therapeutic decisions should be made based solely on the Hypotension Prediction Index (HPI) parameter.
Comparison of the two indication for use statements :
- Same target users (HCP).
- Both provided adjunctive information
- Same type of input data (physiological data)
- Both devices produce an output future risk score
- Different input data: CIEDs data for the subject device and blood pressure and associated ● hemodynamics for the predicate device (HPI).
#### 6 Technological Characteristics
SignalHF (the subject device) and the Predicate Device have substantially equivalent fundamental scientific technology. Both consist of:
- physiologic data analysis performed using machine learning
- software being deployed as a cloud service `
- input data comes from cardiac vital signs monitors
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Based on the assessment and comparison of the technical characteristics of the subject and predicate device, it is determined that both systems have been developed using similar methodologies, offer a future risk score as part of the intended use, and provide adjunct information to the healthcare professional in management of the patient population. The technical characteristics of both devices have been determined to be substantially equivalent.
#### 7 Non-clinical Performance
Non-clinical testing was conducted to assess algorithm performance and to verify that SignalHF performs as intended.
Software verification and validation testing was conducted, and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices. The software for this device was considered as moderate level of concern. Testing was conducted to ensure that the SignalHF algorithm works as designed.
Algorithm performance testing was assessed using SNDS (the SYSTÈME NATIONAL DES DONNÉES DE SANTÉ) databases as well as Implicity proprietary databases.
The results of the testing demonstrate that SignalHF performs to its specifications and meets its intended use, which is substantially equivalent to that of the predicate device. A traditional clinical study was not required.
A traditional clinical study was not required.
FORESEE-HF Study is a non-interventional clinical retrospective study designed to evaluate a SignalHF score resulting from the combination of multiple sensor measurements collected from multi-brands CIEDs and the data stored in the French national health database "SNDS" in order to detect signs of worsening HF. The follow-up period is defined as 2017-2021.
### Inclusion and exclusion criteria
All patients that meet the following criteria were included in the study:
1. Implanted with a ICD / CRT-D / PM / CRT-P, manufactured by Medtronic, Boston Scientific and Biotronik, compatible with thoracic impedance recording
2. With at least one remote monitoring data transmission during the follow-up period of 2018-2021
Patients were excluded from the trial if they presented data quality issues:
- 1. Patients with unspecified device type in the retrospective database
- 2. Patients with multiple devices between 2010 and 2021
## Coprimary endpoints
The coprimary endpoints of the study for ICD/CRT-D devices are:
- -Sensitivity for detecting hospitalizations with HF as primary diagnosis > 40%
- Unexplained alert rate per patient-year < 2.0 -
- -75% of true positive alerts are raised at least 15 days before HF hospitalization event
The coprimary endpoints of the study for pacemaker/CRT-P devices are:
- Sensitivity for detecting hospitalizations with HF as primary diagnosis > 30% ।
- -Unexplained alert rate per patient-year < 2.0
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- 75% of true positive alerts are raised at least 15 days before hospitalization event -
The population size of patients who match the inclusion criteria and are not excluded is 17,974 (PM 7,360, ICD 5,642, CRT-D 4,116 and CRT-P 856; age 70.0 ± 13.3, male 70.20%).
| Demographic Variable | Train cohort | Validation cohort | Clinical cohort |
|---------------------------------|--------------|-------------------|-----------------|
| Nb of patients | 7556 | 3678 | 6,740 |
| Age (years) | 69.2 ± 13.9 | 70.9 ± 12.3 | 70.6 ± 13.0 |
| Sex | | | |
| Female (%) | 28.43 | 29.50 | 30.03 |
| Male (%) | 71.57 | 70.50 | 69.97 |
| Device model | | | |
| ICD (%) | 33.72 | 28.17 | 30.54 |
| CRT-D (%) | 23.13 | 23.33 | 22.40 |
| Pacemaker (%) | 38.49 | 42.90 | 42.64 |
| CRT-P (%) | 4.66 | 5.60 | 4.42 |
| Manufacturer<br>device<br>model | | | |
| Biotronik (%) | 44.64 | 43.83 | 49.75 |
| Boston Scientific (%) | 18.99 | 17.64 | 10.01 |
| Medtronic (%) | 36.37 | 38.53 | 40.24 |
| Comorbidities | | | |
| Renal failure(%) | 10.26 | 10.47 | 11.56 |
| Hypertension(%) | 37.43 | 40.33 | 40.54 |
| Diabetes(%) | 15.62 | 17.84 | 16.25 |
| Obesity/High BMI(%) | 6.93 | 8.48 | 11.51 |
# Coprimary Objective Results
SignalHF sensitivity, defined as sensitivity for detecting hospitalizations with HF as primary diagnosis, is above 50% for Medtronic and Boston Scientific ICD and CRT-D devices. For Biotronik ICD and CRT-D products, the
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sensitivity reaches 70 %. The UAR PPY is always < 1.5 and the lower quartile on alerting time is always > 15 days for ICD and CRT-D from all the manufacturers analyzed, except for Boston Scientific ICD/CRT-D devices, where it is > 15 days on average only. Concerning IPG and CRT-P devices, the sensitivity is 50.4 % for Medtronic devices and 45.3% for Biotronik and UAR PPY is always < 1 for both manufacturers. Lower quartile on alerting time is > 15 days for Biotronik IPG/CRT-P devices, and on average due to low sample size for Medtronic IPG/CRT-Ps. Therefore, the three co-primary endpoints are reached.
## Global Performance
The following presents the performance of SignalHF by device type as it relates to:
- Sensitivity for detecting hospitalizations with HF as primary diagnosis, and ●
- Unexplained alert rate per patient-year (PPY) ●
- Lower quartile on alerting time (in days)
All SignalHF performance metrics are available in the Clinical Evaluation Report (CER-IM008)
| Endpoints | ICD/CRT-D population objective | SignalHF performance for ICD/CRT-D devices |
|-------------------------------------------|--------------------------------|--------------------------------------------|
| Sensitivity (%) | > 40% | 59.8% [54.0%; 65.4%] |
| Unexplained Alert Rate PPY | < 2.0 | 0.654 [0.614; 0.692] |
| Lower quartile on alerting time (in days) | > 15 days | 35.0 [27.0; 52.0] |
| Endpoints | Pacemaker/CRT-P<br>population<br>objective | SignalHF performance<br>for<br>pacemaker/CRT-P devices |
|----------------------------------------------|--------------------------------------------|--------------------------------------------------------|
| Sensitivity (%) | > 30% | 45.9% [38.1%; 53.8%] |
| Unexplained Alert Rate PPY | < 2.0 | 0.470 [0.441; 0.502] |
| Lower quartile on alerting<br>time (in days) | > 15 days | 37 [24.5; 53.0] |
# Performance per manufacturer
MEDTRONIC
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| Endpoints | ICD/CRT-D population<br>objective | SignalHF performance for<br>Medtronic ICD/CRT-D |
|----------------------------------------------|-----------------------------------|-------------------------------------------------|
| Sensitivity (%) | > 40% | 52.0% [43.6%; 60.2%] |
| Unexplained Alert Rate<br>PPY | < 2.0 | 0.30 [0.28; 0.32] |
| Lower quartile on alerting<br>time (in days) | > 15 days | 39.3 [21.0; 59.0] |
| Endpoints | Pacemaker/CRT-P<br>population objective | SignalHF performance for<br>Medtronic pacemaker/CRT-P |
|----------------------------------------------|-----------------------------------------|-------------------------------------------------------|
| Sensitivity (%) | > 30% | 50.4% [30.6%; 70.2%] |
| Unexplained Alert<br>Rate PPY | < 2.0 | 0.71 [0.65; 0.78] |
| Lower quartile on<br>alerting time (in days) | > 15 days | 64.5 [8.0; 119.0] |
# BOSTON SCIENTIFIC
| Endpoints | ICD/CRT-D population<br>objective | SignalHF performance for<br>Boston Scientific ICD/CRT-D |
|----------------------------------------------|-----------------------------------|---------------------------------------------------------|
| Sensitivity (%) | > 40% | 62.5% [46.9%; 75.8%] |
| Unexplained Alert Rate<br>PPY | < 2.0 | 0.89 [0.81; 0.97] |
| Lower quartile on alerting<br>time (in days) | > 15 days | 28.0 [13.0; 55.5] |
## BIOTRONIK
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| Endpoints | ICD/CRT-D population<br>objective | SignalHF performance for<br>Biotronik ICD/CRT-D |
|----------------------------------------------|-----------------------------------|-------------------------------------------------|
| Sensitivity (%) | > 40% | 70.0% [60.6%; 77.9%] |
| Unexplained Alert Rate<br>PPY | < 2.0 | 1.09 [1.00; 1.18] |
| Lower quartile on alerting<br>time (in days) | > 15 days | 39.0 [25.8 ; 60.0] |
| Endpoints | Pacemaker/CRT-P<br>population objective | SignalHF performance for<br>Biotronik pacemaker/CRT-P |
|----------------------------------------------|-----------------------------------------|-------------------------------------------------------|
| Sensitivity (%) | > 30% | 45.3% [36.9%; 53.9%] |
| Unexplained Alert Rate<br>PPY | < 2.0 | 0.42 [0.39; 0.46] |
| Lower quartile on alerting<br>time (in days) | > 15 days | 37.0 [25.5; 53.0] |
### SUMMARY
SignalHF performances are above target performances for all pre-defined patient groups except for Boston Scientific pacemaker/CRT-P devices, for which we did not have enough data for training and evaluation of a HF prediction algorithm.
# Performance analysis per manufacturer, device model type and number of
# leads
Medtronic ICD/CRT-D devices
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| Endpoints | ICD/CRT-D<br>population<br>objective | One lead | Two leads | Three leads |
|-------------------------------------------------|--------------------------------------|-------------------------|-------------------------|-------------------------|
| Sensitivity (%) | > 40% | 43.5%<br>[29.6%; 58.5%] | 49.3%<br>[35.8%; 63.0%] | 62.7%<br>[48.0%; 75.4%] |
| Unexplained<br>alert rate PPY | < 2.0 | 0.24<br>[0.20; 0.28] | 0.31<br>[0.27; 0.35] | 0.34<br>[0.30; 0.38] |
| Lower quartile<br>on alerting time<br>(in days) | > 15 days | 59.0<br>[19.0; 163.0] | 31.0<br>[10.0; 55.0] | 57.0<br>[16.0; 96.0] |
Medtronic PM/CRT-P devices
| Endpoints | Pacemaker<br>/CRT-P<br>population<br>objective | One lead | Two leads | Three leads |
|-------------------------------------------------|------------------------------------------------|-----------------------------|-------------------------|------------------------|
| Sensitivity (%) | > 30% | 16.7%<br>[2.3%; 63.1%] | 64.6%<br>[34.5%; 86.3%] | Not available |
| Unexplained<br>alert rate PPY | < 2.0 | 0.61<br>[0.42; 0.84] | 0.62<br>[0.56; 0.68] | 0.76<br>[0.60; 0.93] |
| Lower quartile<br>on alerting<br>time (in days) | > 15 days | 10.0<br>(CI not computable) | 91.8<br>[20.0; 129.0] | 106.3<br>[98.0; 131.0] |
Boston Scientific ICD/CRT-D devices
{13}------------------------------------------------
| Endpoints | ICD/CRT-D<br>population<br>objective | One lead | Two leads | Three leads |
|-------------------------------------------------|--------------------------------------|-------------------------|-----------------------|-------------------------|
| Sensitivity (%) | > 40% | 71.2%<br>[40.3%; 90.0%] | Not available | 60.5%<br>[39.4%; 78.4%] |
| Unexplained<br>alert rate PPY | < 2.0 | 0.69<br>[0.56; 0.84] | 0.49<br>[0.38; 0.60] | 0.56<br>[0.49; 0.64] |
| Lower quartile<br>on alerting time<br>(in days) | > 15 days | 52.5<br>[15.0; 140.3] | 53.8<br>[29.0; 179.0] | 43.0<br>[11.3; 94.0] |
Biotronik ICD/CRT-D devices
| Endpoints | ICD/CRT-D<br>population<br>objective | One lead | Two leads | Three leads |
|-------------------------------------------------|--------------------------------------|-------------------------|-------------------------|-------------------------|
| Sensitivity (%) | > 40% | 73.1%<br>[55.4%; 85.6%] | 67.5%<br>[50.3%; 81.1%] | 63.1%<br>[48.4%; 75.8%] |
| Unexplained<br>alert rate PPY | < 2.0 | 0.88<br>[0.76; 1.01] | 0.76<br>[0.64; 0.88] | 0.63<br>[0.54; 0.71] |
| Lower quartile<br>on alerting time<br>(in days) | > 15 days | 41.0<br>[22.0; 64.8] | 62.75<br>[30.0; 157.0] | 54.0<br>[18.5; 88.0] |
Biotronik PM/CRT-P devices
{14}------------------------------------------------
| Endpoints | Pacemaker/<br>CRT-P<br>population<br>objective | One lead | Two leads | Three leads |
|----------------------------------------------------|------------------------------------------------|-------------------------|-------------------------|-------------------------|
| Sensitivity<br>(%) | > 30% | 29.4%<br>[11.2%; 58.0%] | 40.2%<br>[30.9%; 50.2%] | 48.0%<br>[26.3%; 70.4%] |
| Unexplained<br>alert rate PPY | < 2.0 | 0.46<br>[0.36; 0.58] | 0.31<br>[0.28; 0.34] | 0.45<br>[0.35; 0.57] |
| Lower<br>quartile on<br>alerting time<br>(in days) | > 15 days | 108.0<br>[3.0; 395.0] | 64.0<br>[34.0; 105.5] | 10.0<br>[9.0; 34.5] |
### SUMMARY
Defibrillators with 1, 2 or 3 leads from all manufacturers, as well as pacemakers with 2 and 3 leads from Medtronic and pacemakers with 1, 2 or 3 leads from Biotronik passed the required performances on every endpoint (sensitivity, unexplained alert rate, median alerting time). The results show in particular no specific group overperforming and causing performance overestimation.
Medtronic Pacemaker 1 lead is the only group not meeting endpoint requirements as defined in the CEP IM008. However, this population does not have any alternative to assess a potential risk of Heart Failure based on their device data. Implicity believes bringing IM008 to this population could avoid some hospitalization in a population that is for the moment not addressed by any other solution.
Warning: In patient populations unlikely to have acute decompensation of HF, the use of the algorithm may have less and possibly poor PPV and NPV.
#### 8 Conclusion
The results of non-clinical testing demonstrate that SignalHF meets its intended use which is equivalent to that of the predicate device. Testing also ensured that SignalHF performed as intended and does not raise different questions of safety or effectiveness to the predicate device.
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Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.