(150 days)
The Edwards Acumen Hypotension Prediction Index software feature provides the clinician with physiological insight into a patient's likelihood of future hypotensive events and the associated hemodynamics. The Acumen is intended for use in surgical or non-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 is not intended to make therapeutic decisions solely on the Acumen Hypotension Prediction Index (HPI) parameter.
The Acumen Hypotension Prediction Index parameter (HPI) provides the clinician with the likelihood that the patient may be trending toward a hypotensive event. The Acumen HPI feature is intended for use in surgical or non-surgical patients. By default, the software defines a hypotensive event as mean arterial pressure (MAP)
The Edwards Acumen Hypotension Prediction Index (HPI) software feature was evaluated through algorithm verification and a usability study. The key aspects of the evaluation are as follows:
1. Acceptance Criteria and Reported Device Performance:
The document primarily focuses on verifying that the changes to the HPI algorithm (adjustable MAP targets) and the expansion to non-surgical patients did not negatively impact its safety and effectiveness. Specific numerical acceptance criteria for performance metrics (e.g., sensitivity, specificity, accuracy) are not explicitly stated in the provided text. Instead, the document states:
- "The results establish that the usage of the HPI algorithm with the adjustable Mean Arterial Pressure (MAP) targets for hypotension (55, 60, 70, 75, 80, 85 mmHg) did not adversely affect the safety and effectiveness of the subject device."
- "The usability study demonstrated that the intended users can perform primary operating functions and critical tasks of the system without any usability issues that may lead to patient or user harm."
Without specific performance metrics and their corresponding acceptance thresholds, a table of acceptance criteria versus reported performance cannot be fully constructed from the provided text. The overall reported performance is that the device meets the implicit acceptance criterion of not adversely affecting safety and effectiveness with the modifications.
2. Sample Size and Data Provenance for Test Set:
- Algorithm Verification: The algorithm verification was performed using "clinical data." For the expanded non-surgical indication, "non-surgical clinical data collected retrospectively" was used.
- Sample Size: The document does not specify the sample size for the clinical data used in the algorithm verification test set.
- Data Provenance: The data used for the non-surgical indication was "retrospectively" collected. The country of origin is not specified.
3. Number of Experts and Qualifications for Ground Truth:
The document does not provide information on the number of experts used or their qualifications for establishing the ground truth for the test set.
4. Adjudication Method:
The document does not specify any adjudication method used for the test set.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study, nor does it provide any effect size of human readers improving with AI vs. without AI assistance. The HPI is an "Adjunctive Predictive Cardiovascular Indicator" providing "additional quantitative information" and is "not intended to make therapeutic decisions solely on the Acumen Hypotension Prediction Index (HPI) parameter," suggesting it is an aid rather than a replacement for human decision-making. However, the exact nature of human-in-the-loop studies, if any, is not detailed.
6. Standalone Performance:
Yes, a standalone (algorithm only) performance evaluation was done through the "Algorithm Verification" section. The verification confirmed that the modified algorithm, with adjustable MAP targets, did not adversely affect safety and effectiveness. This indicates an evaluation of the algorithm's performance independent of real-time human interaction.
7. Type of Ground Truth:
The ground truth used for algorithm verification appears to be based on observed "hypotensive events" defined by Mean Arterial Pressure (MAP) falling below user-defined thresholds (e.g.,
§ 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.