(228 days)
The Edwards Lifesciences Acumen Assisted Fluid Management (AFM) Software Feature provides the clinician with physiological insight into a patient's estimated response to fluid therapy and the associated hemodynamics. The Acumen AFM Software Feature is intended for use in surgical patients ≥18 years of age, that require advanced hemodynamic monitoring. The Acumen AFM Software Feature offers suggestions regarding the patient's physiological condition and estimated response to fluid therapy. Acumen AFM fluid administration suggestions are offered to the clinician; the decision to administer a fluid bolus is made by the clinician, based upon review of the patient's hemodynamics. No therapeutic decisions should be made based solely on the Assisted Fluid Management suggestions.
The Acumen AFM Software Feature (core AFM algorithm + AFM Graphical User Interface) was originally granted in De Novo, DEN190029, on November 13, 2020, to inform clinicians about a patient's fluid responsiveness. The performance of the AFM Software Feature in predicting a patient's fluid responsiveness is measured using response rate and is calculated by reporting the percentage of followed AFM recommendations ("Fluid Bolus Suggested" and "Test Bolus Suggested" prompts) that have the desired change in stroke volume (SV), divided by the total number of AFM recommendations.
With this submission, Edwards is seeking clearance for the AFM Prompt Reclassifier algorithm (AFM PR algorithm) to the Acumen AFM Software Feature. The AFM Prompt Reclassifier algorithm is intended to be used in conjunction with the core AFM algorithm to re-assess the fluid bolus recommendations provided by the core alqorithm. It analyzes the patient's current hemodynamics for either confirming (corroborating) the original prompt or reclassifying the prompts (i.e., reclassify a "Test Bolus Suggested" prompt to a "Fluid Bolus Suggested" prompt or vice versa). In doing so, it acts as a secondary check for the fluid bolus prompts such that a greater number of the "Fluid Bolus Suggested" prompts lead to the desired change in stroke volume. Through refined prompt adjustments informed by real-time hemodynamic data, the AFM PR algorithm aims to improve patient responsiveness, thereby optimizing the impact of the AFM Software Feature on patient hemodynamics.
The FDA 510(k) summary for the Acumen Assisted Fluid Management (AFM) Software Feature describes the acceptance criteria and the study conducted to demonstrate the device meets these criteria.
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not explicitly stated as a numerical target, but the overall goal was to demonstrate an improvement in the response rate for "Fluid Bolus Suggested" prompts due to the addition of the AFM Prompt Reclassifier (AFM PR) algorithm. This improvement should confirm that a greater number of these prompts lead to desired changes in the patient's stroke volume. | The study "demonstrated an improvement in response rate for 'Fluid Bolus Suggested' prompts, thus demonstrating that the AFM PR algorithm met the predefined acceptance criteria." The results showed that the differences in fluid bolus suggestions introduced by the AFM PR algorithm do not raise any safety and effectiveness concerns. |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: The algorithm performance was analyzed on an archived dataset consisting of 1229 data points from 307 patients.
- Data Provenance: The data came from the US IDE Study, G170204. The study involved 9 independent U.S. sites. The data is retrospective as it was an "archived dataset."
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
- This information is not provided in the document. The study uses physiological response (change in stroke volume) as the outcome measure, implying a physiological ground truth rather than expert interpretation of images or other data.
4. Adjudication Method for the Test Set
- This information is not provided in the document. Given the nature of the ground truth (physiological response), a traditional adjudication method for subjective assessments might not be directly applicable.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance
- A MRMC comparative effectiveness study was not explicitly mentioned or described.
- The study focuses on the algorithm's performance in improving the response rate of its suggestions, rather than comparing human reader performance with and without AI assistance. The device offers "suggestions" and the "decision to administer a fluid bolus is made by the clinician."
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
- Yes, a standalone algorithm performance study was done. The document states, "Algorithm performance was analyzed on the archived dataset... The validation was to demonstrate the impact on the response rate of the AFM Software Feature's fluid bolus prompts due to the addition of AFM Prompt Reclassifier algorithm." This implies evaluating the algorithm's predictions against the measured physiological outcomes.
7. The Type of Ground Truth Used
- The ground truth used is physiological response/outcomes data. Specifically, the "desired change in stroke volume (SV)" following AFM recommendations was used to measure the "response rate."
8. The Sample Size for the Training Set
- The document does not explicitly state the sample size for the training set. It only mentions the "archived dataset from the US IDE Study, G170204" used for algorithm performance analysis/validation.
9. How the Ground Truth for the Training Set Was Established
- The document does not explicitly describe how the ground truth for the training set was established. It focuses on the validation of the AFM Prompt Reclassifier algorithm using an existing dataset. Given that the core AFM algorithm was granted in De Novo DEN190029, the ground truth for its original training would likely have involved similar physiological outcome data from clinical studies where fluid administration decisions were made and subsequent stroke volume changes were observed.
§ 870.5600 Adjunctive open loop fluid therapy recommender.
(a)
Identification. The adjunctive open loop fluid therapy recommender is a prescription device that uses software algorithms to analyze cardiovascular vital signs and predict a patient's estimated response to fluid therapy. The 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) Clinical performance testing under anticipated conditions of use must fulfill the following:
(i) A summary of the clinical performance testing must include the relevant patient demographics, and any statistical techniques used for analyzing the data;
(ii) Subjects must be representative of the intended use population for the device. Any selection criteria or sample limitations must be fully described and justified;
(iii) Testing must demonstrate the recommendation consistency using the expected range of data sources and data quality encountered in the intended patients, users, and environments; and
(iv) Testing must evaluate the relationship between algorithm recommendations, therapeutic actions, and predicted physiological event or status.
(2) 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 recommendation, accounting for differences in patient condition and environment;
(iii) A description of all mitigations for user error or failure of any subsystem components (including signal detection, signal analysis, data display, and storage) that affect the device's recommendations;
(iv) A characterization of algorithm sensitivity to variations in user inputs;
(v) A characterization of sensor accuracy and performance;
(vi) A description of sensor data quality control measures; and
(vii) Safeguards to reduce the possibility of fluid overload.
(3) A scientific justification for the validity of the algorithm(s) must be provided. This justification must include non-clinical verification and validation of the algorithm calculations and clinical validation using an independent data set.
(4) A human factors and usability engineering assessment must be provided.
(5) Labeling must include:
(i) A description of what the device measures, how the device decides to issue recommendations, and the expected range of frequency of recommendations, while accounting for differences in patient condition and environment;
(ii) Detailed information regarding limitations of the device's algorithm, and key assumptions made when the device issues a recommendation;
(iii) Warnings identifying sensor acquisition factors that may impact measurement results;
(iv) Warnings identifying user errors that affect the device's recommendations;
(v) Detailed information regarding the expected impact of user input errors on the device recommendations;
(vi) Guidance for interpretation of the device's recommendations, including a description that the recommendation is adjunctive to other physical vital sign parameters and patient information;
(vii) Description of the impact of the compatible sensor(s) on the device's performance;
(viii) The expected performance of the device for all intended patients, users, and environments;
(ix) Relevant characteristics of the patients studied in the clinical validation (such as age, gender, race or ethnicity, and patient condition) and a summary of validation results; and
(x) Description of the software safeguards that are in place to prevent fluid overload, and description of any limitation of the software safeguards.