(265 days)
Indications for Use: This device is intended for use in patients who are morbidly obese and have failed to lose weight with diet and exercise.
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Here's a breakdown of the acceptance criteria and study details for the Hypertension Prediction Index (HePI) Algorithm, based on the provided FDA 510(k) letter:
1. Table of Acceptance Criteria and Reported Device Performance
| Performance Metric | Acceptance Criteria | Reported Device Performance (Minimally Invasive) | Reported Device Performance (Non-Invasive) |
|---|---|---|---|
| Sensitivity (%) | > 80% | 99.7% [99.6, 99.9] | 99.6% [99.1, 100.0] |
| Specificity (%) | > 80% | 93.7% [92.9, 94.3] | 91.6% [90.7, 92.5] |
Note: The reported performance is for the overall datasets (N=1813 for Minimally Invasive, N=1351 for Non-invasive). All sub-categories (surgical/non-surgical) also met the acceptance criteria.
2. Sample Size Used for the Test Set and Data Provenance
- Minimally Invasive Sensor:
- US Patients: 1615 subjects
- OUS Patients: 198 subjects
- Total N: 1813 subjects
- Non-Invasive Finger Cuff:
- US Patients: 464 subjects
- OUS Patients: 887 subjects
- Total N: 1351 subjects
Data Provenance: The study used retrospective clinical data from multiple independent datasets. Data was collected from both US and OUS (Outside United States) patients.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The provided document does not specify the number of experts used or their qualifications for establishing the ground truth.
4. Adjudication Method for the Test Set
The provided document does not specify an adjudication method. The ground truth definition of a "hypertensive event" is clearly stated (MAP > 115 mmHg for at least 1 minute or MAP increase of > 20% when current MAP > 95 mmHg), suggesting an objective, pre-defined criterion rather than expert consensus on individual cases that would require adjudication.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
The provided document does not indicate that an MRMC comparative effectiveness study was done. The focus is on the standalone performance of the algorithm.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done
Yes, a standalone performance study was done. The results presented in Table 2 (AUC, Sensitivity, Specificity, PPV, NPV) are direct measures of the algorithm's performance in predicting hypertensive events based on retrospective clinical data, without human interaction.
7. The Type of Ground Truth Used
The ground truth used is defined by objective physiological measurements and thresholds:
A "hypertensive event" is defined as:
- Mean Arterial Pressure (MAP) greater than 115 mmHg for at least 1 minute OR
- A MAP increase of more than 20% when current MAP is greater than 95 mmHg.
8. The Sample Size for the Training Set
The provided document does not explicitly state the sample size for the training set. It mentions that "Algorithm performance was tested using retrospective clinical data" and "Prospective analyses of retrospective clinical data from multiple independent datasets...were analyzed to verify the safety and performance of the subject device," referring to the test sets.
9. How the Ground Truth for the Training Set Was Established
The provided document does not explicitly state how the ground truth for the training set was established. However, given the nature of the device and the ground truth definition for the test set, it is highly likely that the same objective physiological measurements and thresholds (MAP > 115 mmHg for at least 1 minute or MAP increase of > 20% when current MAP > 95 mmHg) were used to establish ground truth labels for the training data.
§ 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.