(211 days)
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.
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.
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 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.
Here's a summary of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for SignalHF:
Acceptance Criteria and Device Performance for SignalHF
The SignalHF device was evaluated through the FORESEE-HF Study, a non-interventional clinical retrospective study.
1. Table of Acceptance Criteria and Reported Device Performance
For ICD/CRT-D Devices:
| Endpoints | Acceptance Criteria (Objective) | SignalHF Performance (ICD/CRT-D Devices) |
|---|---|---|
| Sensitivity for detecting HF hospitalization (%) | > 40% | 59.8% [54.0%; 65.4%] |
| Unexplained Alert Rate PPY | 15 days | 35.0 [27.0; 52.0] |
For Pacemaker/CRT-P Devices:
| Endpoints | Acceptance Criteria (Objective) | SignalHF Performance (Pacemaker/CRT-P Devices) |
|---|---|---|
| Sensitivity for detecting HF hospitalization (%) | > 30% | 45.9% [38.1%; 53.8%] |
| Unexplained Alert Rate PPY | 15 days | 37 [24.5; 53.0] |
2. Sample Size and Data Provenance for the Test Set
- Test Set (Clinical Cohort) Sample Size: 6,740 patients (comprising PM 7,360, ICD 5,642, CRT-D 4,116 and CRT-P 856 - Note: there appears to be a discrepancy in the total sum provided, however, "6,740" is explicitly stated as the 'Clinical cohort' which is the test set).
- Data Provenance: Retrospective study using data from the French national health database "SNDS" (SYSTÈME NATIONAL DES DONNÉES DE SANTÉ) and Implicity proprietary databases. The follow-up period was 2017-2021.
3. Number of Experts and Qualifications for Ground Truth
The document does not explicitly state the number of experts used to establish ground truth or their specific qualifications (e.g., radiologist with 10 years of experience). However, the ground truth was "hospitalizations with HF as primary diagnosis" as recorded in the national health database, implying that these diagnoses were made by qualified healthcare professionals as part of routine clinical care documented within the SNDS.
4. Adjudication Method for the Test Set
The document does not specify an adjudication method like 2+1 or 3+1 for establishing the ground truth diagnoses. The study relies on “hospitalizations with HF as primary diagnosis” from the national health database, suggesting that these are established clinical diagnoses within the healthcare system.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
There is no indication that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to evaluate human reader improvement with AI assistance. The study focuses solely on the standalone performance of the SignalHF algorithm.
6. Standalone Performance
Yes, a standalone (algorithm only without human-in-the-loop performance) study was done. The FORESEE-HF study evaluated the SignalHF algorithm's performance in predicting heart failure hospitalizations based on CIED data and personal health records.
7. Type of Ground Truth Used
The ground truth used was outcomes data, specifically "hospitalizations with HF as primary diagnosis" recorded in the French national health database (SNDS).
8. Sample Size for the Training Set
- Training Cohort Sample Size: 7,556 patients
9. How the Ground Truth for the Training Set Was Established
The document states that the algorithm computes the HF score using physiological measures from compatible CIEDs and available Personal Health Records (demographics). It also mentions that the "recovery threshold on the SignalHF score established during the learning phase of the algorithm and fixed for all patients". This implies that the ground truth for the training set, similar to the test set, was derived from the same data sources: "hospitalizations with HF as primary diagnosis" documented within the SNDS database. The training process would have used these documented HF hospitalizations as the target outcome for the algorithm to learn from.
§ 870.2210 Adjunctive predictive cardiovascular indicator.
(a)
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.(b)
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.