(536 days)
The Edwards Lifesciences Acumen Hypotension Prediction Index (HPI) feature provides the clinician with physiological insight into a patient's likelihood of future hypotensive events (defined as mean arterial pressure
The Acumen Hypotension Prediction Index Feature ("the device") consists of software running on the Edwards Lifesciences EV1000 Platform (previously cleared under K100709, K110597, K131892. K140312, and K160552) paired with the FloTrac IQ extravascular blood pressure transducer (K152980) and a radial arterial catheter. The device includes the Hypotension Prediction Index (HPI), the Dynamic Arterial Elastance Parameter (Eagyn), the Left Ventricular Contractility Parameter (dP/dt), and additional graphical user interface features.
HPI is an index related to the likelihood of a patient experiencing a hypotensive event (defined as mean arterial pressure (MAP)
1. Table of Acceptance Criteria and Reported Device Performance
| Metric | Acceptance Criteria (Implicit from Clinical Validation) | Reported Device Performance (N=52 Study) | Reported Device Performance (N=204 Study) |
|---|---|---|---|
| Sensitivity | High enough to be clinically useful | 83.7% [81.5, 86.0]% | 65.8% [63.7, 67.9]% |
| Specificity | High enough to avoid excessive false positives | 99.8% [99.4, 100.0]% | 99.4% [99.2, 99.7]% |
| AUC | High enough to indicate good discrimination | 0.95 | 0.88 |
| Positive Predictive Value (PPV) | (Not explicitly stated as AC, but evaluated) | 99.9% [99.7, 100.0]% | 98.3% [97.6, 99.0]% |
| Negative Predictive Value (NPV) | (Not explicitly stated as AC, but evaluated) | 75.1% [71.9, 78.4]% | 84.9% [83.9, 86.0]% |
Note: The document does not explicitly state numerical acceptance criteria for sensitivity, specificity, and AUC. However, the reported performance metrics from the clinical validation studies demonstrate a level of accuracy deemed acceptable by the FDA for de novo classification. The high specificities and AUC values, along with the detailed performance table for different HPI ranges, suggest that the device's ability to predict hypotension within the 15-minute timeframe was considered sufficient. The acceptance criteria for usability testing (at least 80% of participants agree or strongly agree) are explicitly stated in the Usability Testing section.
2. Sample Size Used for the Test Set and Data Provenance
The "test set" for the HPI algorithm's performance evaluation was derived from two retrospective patient databases:
- First Database (Edwards Lifesciences):
- Sample Size: 52 subjects (OR patients)
- Data Provenance: Global clinical sites, collected via prospective, IRB/EC approved clinical protocols with informed consent for each patient. (Retrospective analysis of prospectively collected data).
- Second Database (University Hospital):
- Sample Size: 204 subjects (OR patients)
- Data Provenance: From a university hospital, includes OR patients. (Retrospective analysis of an arterial waveform database).
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document does not specify the "number of experts" or their "qualifications" used to establish the ground truth for the test set.
Instead, the ground truth for hypotensive events was defined objectively: "mean arterial pressure (MAP)
§ 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.