(529 days)
Not Found
Yes
The device description explicitly states that the "AFM algorithm learns from the stroke volume response to each fluid bolus to determine if a patient is in a fluid responsive or pre-load dependent state" and "Throughout the case. the algorithm tracks and records bolus and patient response information to adapt its suggestions based off of the individual patient." This learning and adaptation based on patient data is a key characteristic of machine learning.
Yes
The device aids clinicians in fluid therapy decisions by providing physiological insight and suggestions, with the goal of improving a patient's hemodynamic state, thus having a therapeutic effect.
Yes
The device provides "physiological insight into a patient's estimated response to fluid therapy and the associated hemodynamics" and "offers suggestions regarding the patient's physiological condition and estimated response to fluid therapy," which are all diagnostic functions.
No
The device description explicitly states that the software runs on the Edwards Lifesciences EV1000 Clinical Platform and is coupled with an Acumen 10 sensor connected to a radial arterial catheter. This indicates the device includes hardware components (the platform and the sensor) in addition to the software.
Based on the provided information, the Edwards Lifesciences Acumen Assisted Fluid Management (AFM) software feature is not an In Vitro Diagnostic (IVD) device.
Here's why:
- IVD Definition: An IVD device is a medical device intended for use in vitro for the examination of specimens, including blood, tissue, and urine, from the human body to provide information for diagnostic, monitoring, or compatibility purposes.
- Acumen AFM Function: The Acumen AFM software feature analyzes physiological data (hemodynamics) obtained from a sensor connected to a radial arterial catheter. It provides suggestions and insights into a patient's estimated response to fluid therapy. It does not examine specimens from the human body in vitro.
- Intended Use: The intended use is to provide physiological insight and suggestions for fluid management in surgical patients based on real-time hemodynamic data. This is a monitoring and decision support tool, not a diagnostic test performed on a specimen.
Therefore, the Acumen AFM software feature falls under the category of a medical device that monitors physiological parameters and provides clinical decision support, rather than an In Vitro Diagnostic device.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device. The 'Control Plan Authorized (PCCP) and relevant text' section is marked as "Not Found."
Intended Use / Indications for Use
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.
Product codes (comma separated list FDA assigned to the subject device)
QMS
Device Description
The Acumen™ Assisted Fluid Management (AFM) Software Feature ("the device") consists of software running on the Edwards Lifesciences EV1000 Clinical Platform (K160552 cleared on June 1, 2016) coupled with an Acumen 10 sensor (which was called FloTrac IO sensor in K152980 cleared on January 19, 2016) connected to a radial arterial catheter. The goal of AFM is to reduce the barriers slowing the utilization of perioperative goal directed therapy (PGDT) during surgical procedures by easing the implementation of PGDT, recognizing patterns of fluid responsiveness (i.e. hemodynamic data and past responses to fluid), and suggesting when fluid administration may improve the patient's hemodynamic state. The clinician is responsible for reviewing the AFM software suggestion in addition to a patient's current hemodynamic state and, if the clinician agrees, the clinician can deliver fluid in the standard-of-care fashion. Alternatively, if the clinician disagrees with the fluid suggestion, it can be rejected as the clinician chooses to not deliver any fluid.
The AFM algorithm can be used on the EV1000 Clinical Platform to help maintain patient fluid balance throughout a surgery. The AFM algorithm continuously estimates patient fluid responsiveness (percent increase in Stroke Volume, A SV%) using current hemodynamic parameters and past responses to fluid boluses. The Acumen AFM software feature is intended to simplify the implementation of fluid management protocols/perioperative goal directed therapy (PGDT).
When an Acumen IO sensor is connected and the AFM algorithm is initialized. the EV1000 Clinical Platform will provide notifications to the user when fluid is recommended by the AFM algorithm. The AFM algorithm learns from the stroke volume response to each fluid bolus to determine if a patient is in a fluid responsive or pre-load dependent state. The patient's tidal volume must be ≥ 8 mL/kg while using the AFM software feature. Throughout the case. the algorithm tracks and records bolus and patient response information to adapt its suggestions based off of the individual patient. In order for the algorithm to analyze each fluid bolus, the start and stop time of each infusion must be entered in the system, as well as the volume of the fluid bolus. The algorithm uses data from the current patient in order to predict their fluid responsiveness; this data is not used by the algorithm to determine fluid responsiveness in future patients.
Each bolus can be administered with the fluid, rate, and volume at the discretion of the clinician. Additionally, any fluid bolus can be declined or discarded as deemed appropriate by the clinician. The AFM algorithm will analyze fluid boluses within the following range: Volume: 100 - 500 mL: Rate: 1 - 10 L / hr.
Mentions image processing
Not Found
Mentions AI, DNN, or ML
Not Found
Input Imaging Modality
Not Found
Anatomical Site
Not Found
Indicated Patient Age Range
≥18 years of age
Intended User / Care Setting
Clinician; use in surgical patients.
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
The AFM IDE study was a prospective, multi-center, single-arm clinical study. A total of b Subjects were enrolled and b study sites in the United States (US).
The primary objective of the AFM IDE study was to evaluate the performance of the device in its ability to predict a patient's fluid responsiveness compared to the historical performance criterion of 30% fluid responsiveness.
Enrollment in the AFM IDE study was limited to patients who met the following inclusion criteria: > 18 years of age, Non-cardiac/Non-thoracic surgery expected to last >2 hours post anesthesia induction, Mechanical ventilation, American Society of Anesthesiology (ASA) Score 3 or 4, Expected arterial line placement for surgical procedure and general anesthesia, Projected to receive hemodynamic monitoring during surgical procedure, Participate or have authorized representative participate in the Informed Consent process and sign/date the IRB approved informed consent form.
Exclusion criteria included: 35 kg/m2, Known acute congestive heart failure, Known aortic stenosis with valve area
§ 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.
0
DE NOVO CLASSIFICATION REQUEST FOR ACUMENTM ASSISTED FLUID MANAGEMENT SOFTWARE FEATURE
REGULATORY INFORMATION
FDA identifies this generic type of device as:
Adjunctive open loop fluid therapy recommender. 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.
NEW REGULATION NUMBER: 21 CFR 870.5600
CLASSIFICATION: Class II
PRODUCT CODE: QMS
BACKGROUND
DEVICE NAME: Acumen™ Assisted Fluid Management (AFM) Software Feature
SUBMISSION NUMBER: DEN190029
DATE OF DE NOVO: June 4, 2019
CONTACT: Edwards Lifesciences LLC One Edwards Way Irvine, CA 92614
INDICATIONS FOR USE
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.
LIMITATIONS
The sale, distribution, and use of the Acumen Assisted Fluid Management Software Feature are restricted to prescription use in accordance with 21 CFR § 801.109.
1
The Assisted Fluid Management feature should not be used exclusively to treat the patient. A review of the patient's hemodynamics is recommended throughout the monitoring session to assess fluid responsiveness.
PLEASE REFER TO THE LABELING FOR A MORE COMPLETE LIST OF WARNINGS, PRECAUTIONS AND CONTRAINDICATIONS.
DEVICE DESCRIPTION
The Acumen™ Assisted Fluid Management (AFM) Software Feature ("the device") consists of software running on the Edwards Lifesciences EV1000 Clinical Platform (K160552 cleared on June 1, 2016) coupled with an Acumen 10 sensor (which was called FloTrac IO sensor in K152980 cleared on January 19, 2016) connected to a radial arterial catheter. The goal of AFM is to reduce the barriers slowing the utilization of perioperative goal directed therapy (PGDT) during surgical procedures by easing the implementation of PGDT, recognizing patterns of fluid responsiveness (i.e. hemodynamic data and past responses to fluid), and suggesting when fluid administration may improve the patient's hemodynamic state. The clinician is responsible for reviewing the AFM software suggestion in addition to a patient's current hemodynamic state and, if the clinician agrees, the clinician can deliver fluid in the standard-of-care fashion. Alternatively, if the clinician disagrees with the fluid suggestion, it can be rejected as the clinician chooses to not deliver any fluid.
The AFM algorithm can be used on the EV1000 Clinical Platform to help maintain patient fluid balance throughout a surgery. The AFM algorithm continuously estimates patient fluid responsiveness (percent increase in Stroke Volume, A SV%) using current hemodynamic parameters and past responses to fluid boluses. The Acumen AFM software feature is intended to simplify the implementation of fluid management protocols/perioperative goal directed therapy (PGDT).
When an Acumen IO sensor is connected and the AFM algorithm is initialized. the EV1000 Clinical Platform will provide notifications to the user when fluid is recommended by the AFM algorithm. The AFM algorithm learns from the stroke volume response to each fluid bolus to determine if a patient is in a fluid responsive or pre-load dependent state. The patient's tidal volume must be ≥ 8 mL/kg while using the AFM software feature. Throughout the case. the algorithm tracks and records bolus and patient response information to adapt its suggestions based off of the individual patient. In order for the algorithm to analyze each fluid bolus, the start and stop time of each infusion must be entered in the system, as well as the volume of the fluid bolus. The algorithm uses data from the current patient in order to predict their fluid responsiveness; this data is not used by the algorithm to determine fluid responsiveness in future patients.
Each bolus can be administered with the fluid, rate, and volume at the discretion of the clinician. Additionally, any fluid bolus can be declined or discarded as deemed appropriate by the clinician. The AFM algorithm will analyze fluid boluses within the following range: Volume: 100 - 500 mL: Rate: 1 - 10 L / hr.
2
Image /page/2/Picture/0 description: The image shows a medical device screen with several panels displaying different information. One panel shows the total volume as 0 ml, while another shows it as 400 ml. A notification panel indicates that the total volume delivered through AFM is 2100 ml, exceeding the maximum case volume of 2000 ml. The screen also displays fluid strategy settings, surgery mode, and maximum case volume, along with options to start or pause AFM.
Figure 1: The AFM dashboard before AFM is started (top, left); an example of the AFM Settings Screen (bottom, left); the fluid delivery recommendation pop up (top, center); an example of information on fluid bolus delivered by the user (bottom, center); an example of the AFM dashboard while AFM is running (top, right); an example of notification prompting the user to either change the Maximum Case Volume or End AFM Session (bottom, right).
SUMMARY OF NONCLINICAL/BENCH STUDIES
ELECTROMAGNETIC COMPATIBILITY AND ELECTRICAL SAFETY
The device is a software feature installed on the EV1000 Clinical Platform (K160552 cleared on June 1, 2016). The EV1000 Clinical Platform has undergone some modifications (K193179 cleared on December 17, 2019) related to the recent Class I recall for the EV1000 Clinical Platform, Z-1193-2019.
SOFTWARE
Complete software documentation was provided in accordance with the FDA Guidance Document. "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices," (issued May 11, 2005) for a Moderate Level of Concern (LOC). A Moderate LOC is deemed appropriate as malfunction of the device software or a latent design flaw in the device software may lead to an erroneous diagnosis or a delay in the delivery of appropriate medical care, which would likely result in minor injury but would likely not result in serious injury or death due to the availability of other patient vital signs.
Cybersecurity information was provided in accordance with the FDA Guidance Document, "Content of Premarket Submissions for Management of Cybersecurity in Medical Devices - Guidance for Industry and Food and Drug Administration Staff" (issued October 02, 2014).
Regression testing was performed to demonstrate that the device software does not adversely impact the performance of the cleared EV1000 Clinical Platform.
3
Translation testing was performed to demonstrate that movement of the device software from the development environment to the EV1000 Clinical Platform does not adversely impact the intended performance of the EV1000 Clinical Platform.
Algorithm unit testing was performed to demonstrate that the device software meets its software requirements. This testing was performed using privately collected patient data. Additional details are provided in the "Summary of Clinical Information" section.
The models in the AFM algorithm (i.e., the fixed population model and the patientspecific bolus log model) were fully described. This included full description of the input and output parameters of the models, and how the model outputs are combined using. techniques that are designed to mitigate user error or failure of subsystem components. The transfer functions for the models of the AFM algorithm were fully described: in particular, the relationship between the inputs (i.e., stroke volume variation, pulse rate. ... ) and outputs (i.e., estimated percent increase in stroke volume) of the models, and the expected final recommendations (i.e., fluid is recommended, a test bolus is suggested, ... ) were also fully described.
A Monte Carlo simulation characterized the effect of the expected uncertainty in fluid delivery volume on the device's recommendations. The simulation repeatedly applied the AFM algorithm on the data from the AFM IDE study while injecting errors into the user input (i.e., fluid delivery volume) aspect of the clinical data. The statistical distribution of the injected errors was derived using a follow up usability study, which focused on hof the participants of the original usability study: these participants were selected based on their initial bolus estimation error, and they included those participants that had the highest over-estimation and highest under-estimation of fluid delivery volume. The participants had an even distribution of Certified Registered Nurse Anesthetists (CRNAs) and Anesthesiologists. The follow up usability study was used to derive the fluid delivery volume error distributions for a | [V bag as a function of the amount of fluid that remains in the IV bag; the | IV bag was chosen for the follow up usability study because participants had larger errors in volume estimation with the | [1]4] [IV bag compared to the 500 mL IV bag. For each iteration of the Monte Carlo simulation, the initial volume in the IV bag was chosen from a uniform distribution between 500 mL and | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | simulate the delivery of fluid boluses from a bolds TV bag.
The results of the Monte Carlo simulation indicated that the expected fluid volume delivery error would not result in grossly unreasonable AFM recommendations, which would result in patient harm. In particular, the expected fluid bolus volume error rarely resulted in new AFM recommendations (i.e., 16(4) of all simulations showed hew AFM recommendations). On the other hand, there was a higher likelihood that an AFM recommendation would be missed (i.e. 10(3) of and (0)(9) of all simulations showed and missed AFM recommendations respectively).
In addition to user input, the AFM algorithm relies on data from the Acumen IO sensor,
4
whose accuracy was characterized. An animal study was used to characterize the sensor accuracy and performance. In particular, percent increases in stroke volume measurements using the Acumen IO sensor and a reference flow probe sensor were compared in porcine model. The radial arterial pressure was used for the computation of the Acumen IQ sensor stroke volume measurement, and the flow probe was placed in the ascending aorta for the reference stroke volume measurement.
To evaluate the performance of Acumen IQ sensor to measure stroke volume changes, percent changes in stroke volume from the Acumen IO sensor measurements (i.e., & SVa%) were compared with the percent change in stroke volume from the reference sensor (i.e., A SVr%). For A SVa% ~ 1%, data points where △ SVr% 18 years of age
-
Non-cardiac/Non-thoracic surgery (e.g., abdominal surgery, combined abdominal/pelvic surgery, major peripheral vascular surgery) expected to last >2 hours post anesthesia induction
-
Procedure will require Mechanical ventilation
-
- American Society of Anesthesiology (ASA) Score 3 or 4
-
- Expected arterial line placement for surgical procedure and general anesthesia
-
- Projected to receive hemodynamic monitoring during surgical procedure
- Participate or have authorized representative participate in the Informed
Consent process and sign/date the IRB approved informed consent form.
Potential subjects were excluded from AFM IDE study participation if during the screening and enrollment process it was determined that they (have):
-
- Are 35 kg/m2
-
- Known acute congestive heart failure
-
- Known aortic stenosis with valve area 100-200 | 59.92% | (54.61, 65.13) | 152 | 76 |
| >200-250 | 57.73% | (50.63, 64.94) | 79 | 49 |
| >250-300 | 65.27% | (59.18, 69.39) | 49 | 39 |
| All Boluses | 66.04% | (61.56, 71.13) | 424 | 207 |
- Known aortic stenosis with valve area 100-200 | 59.92% | (54.61, 65.13) | 152 | 76 |
Table 5: The device performance stratified by delivered bolus volume (in mL).
As a clinical decision support system, AFM suggestions can be declined or discarded by the user. The following table provides complete accounting of the fluid boluses (e.g., declined, discarded, ... ) for the 307 subjects in the per protocol cohort (effectiveness cohort). Although post-hoc analysis revealed no difference in performance based on compliance to AFM suggestions, the AFM IDE study was not designed to directly address this question. Therefore, the device performance may be affected by the compliance to AFM suggestions.
| Bolus Originator | Prompted | Declined | Accepted | Discarded | Completed | Analyzed |
|---|---|---|---|---|---|---|
| AFM | 2550 | 1209 | 1341 | 168 | 1173 | 1165 |
| Recommended | 803 | 324 | 479 | 52 | 427 | 424 |
| Test | 1747 | 885 | 862 | 116 | 746 | 741 |
| User | 606 | 14 | 592 | 81 | 511 | 508 |
| Total | 3156 | 1223 | 1933 | 249 | 1684 | 1673 |
Table 6: Complete accounting of the fluid boluses in the AFM IDE study.
To summarize, the AFM IDE study was a clinical validation using an independent data set. which provided scientific justification for the validity of the device's algorithms. The AFM IDE study subjects were representative of the intended use population for the device. One of
14
the limitations of the study was that about half of the recommendations were declined or discarded. The performance of the device during the AFM IDE study was reported using statistical metrics and confidence intervals for the primary endpoint. In addition, subgroup analyses were provided as discussed above.
Pediatric Extrapolation
In this De Novo request, existing clinical data were not leveraged to support the use of the device in a pediatric patient population.
LABELING
The device labeling includes the following key items below.
- a. The device labeling describes what the device measures. In particular, the inputs to the device include hemodynamic data from an arterial pressure-based analysis (e.g., pulse rate, mean arterial pressure, stroke volume variation, systemic vascular resistance, and the rate of stroke volume change over the past two minutes) and user inputs (e.g., fluid strategy, surgery mode, fluid delivery data). The device labeling describes how the device uses a rule-based algorithm to issue fluid management recommendations, while accounting for the patient's hemodynamic data, the surgery mode, and the user's fluid strategy, Finally, the device labeling provides the following summary of the expected range of frequency of recommendations, and the device labeling provides a statement indicating the possibility of a recommendation immediately after an incorrect recommendation.
| AFM Recommendations
Per Hour | Frequency of
Occurrence" |
|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------|
| 0 | 73.8% (784/1062) |
| - | 10.9% (116/1062) |
| 2 | 6.7% (71/1062) |
| 6.2 | 5.3% (55/1062) |
| 4 | 24% (26/1062) |
| 5 | 0.6% (6/1062) |
| ర్ | 0.3% (3 1062) |
| *The frequency of occurrence is based upon the number of hours
with a given number of AFM recommendations divided by the
total number of hours
"The frequency of AFM recommendations per nour is presented
as general guidance and may not be representative of Individual
aroerlence | |
Table 17-7 Frequency of AFM Recommendations Per Hour*
"It is also possible for an AFM suggestion to immediately follow the completion of a nonresponsive fluid bolus if current hemodynamic state has changed since the prior non-responsive bolus"
- b. The device labeling provides the following detailed information regarding limitations of the device's algorithm, and key assumptions made when the device issues a recommendation.
"The fluid suggestions generated by the AFM software feature are focused on SV and CO and independent of MAP. Therefore, AFM may suggest fluid when a patient is normotensive."
15
"It is also possible for an AFM suggestion to immediately follow the completion of a nonresponsive fluid bolus if current hemodynamic state has changed since the prior nonresponsive bolus"
-
c. The device labeling contains the following cautions identifying sensor acquisition factors that may impact measurement results.
Image /page/15/Figure/2 description: The image contains two sections of text, each headed by a caution symbol and the word "CAUTION". The first section lists factors that can cause inaccurate FT-CO measurements, including improperly zeroed sensors, over- or under-damped pressure lines, and conditions causing blood pressure variations. It also mentions clinical situations where arterial pressure is inaccurate, such as extreme peripheral vasoconstriction and hyperdynamic conditions post-liver transplant. The second section discusses how fluid management suggestions provided by the AFM feature can be compromised, citing inaccurate FT-CO measurements, acute changes due to vasoactive medication, bleeding rates, and arterial line interference. -
d. The device labeling contains the following statements to identify user errors which affect the device's recommendations.
16
Image /page/16/Picture/0 description: The image shows a caution message regarding confounding factors during bolus delivery. It states that the presence of confounding factors may lead to an incorrect fluid recommendation by AFM, and boluses delivered in the presence of such factors should be discarded. The message lists potential confounding factors, including vasoactive agent administration, additional fluid given after primary bolus, subject repositioning, ventilatory changes, surgical manipulation, arterial line interference, vascular clamping, additional line of fluid simultaneously opened during bolus administration, and known acute hemorrhage during fluid administration.
"Precaution. When estimating the amount of fluid delivered and entering the information into the system for analysis, it is important to ensure that the fluid bolus volume entered into the system is as accurate as possible."
- e. The device labeling describes the consequences of user input errors, which may include: selecting the wrong Surgery Mode, selecting Fluid Strategy that is not aligned with the clinician's fluid management strategy, underestimating or overestimating the bolus volume that was given.
- f. The device labeling provides the following guidance for interpretation of the device's recommendations.
Image /page/16/Picture/4 description: This image is a table titled "Table 17-2 AFM Fluid Status Icons". The table has three columns: "AFM Fluid Status Icon in Navigation Bar Display", "AFM Fluid Status Icon in AFM Dashboard", and "Meaning". The first two columns show images of a water droplet with a check mark inside, and the third column describes the meaning of the icon, which is that fluid is recommended. The estimated percentage change in stroke volume exceeds the threshold defined by the Fluid Strategy setting (10%, 15%, 20%).
Table 17-2 AFM Fluid Status Icons
17
| AFM Fluid
Status Icon in
Navigation
Bar Display | AFM Fluid
Status Icon
in AFM
Dashboard | Meaning |
|-----------------------------------------------------------------------------|--------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Image: AFM Fluid Status Icon in Navigation Bar Display with a question mark | Image: AFM Fluid Status Icon in AFM Dashboard with a question mark | A test bolus is suggested.
To learn about the patient's
fluid responsiveness, a test
bolus is suggested. When
AFM suggests a test bolus
the final prediction
contains little to no input
from the individual patient
bolus history and relies
primarily on the patient
population model and will
trigger a test bolus
suggestion if SVV > 9% in
Open Surgery Mode or
SVV > 12% in
Laparoscopic / Prone
Surgery Mode |
| Image: AFM Fluid Status Icon in Navigation Bar Display with an X | Image: AFM Fluid Status Icon in AFM Dashboard with an X | Fluid is not recommended
The AFM software feature
will not suggest fluid
(neither AFM
recommendation nor test
bolus) when specific
physiology indicates that
fluid is not recommended.
This status display will
appear when the AFM
software feature has
learned that the patient
has not responded to fluid
in this hemodynamic state
in the past through the
individual patient bolus
history. If it does not have
information in the
Individual patient bolus
history, it relies on SVV
and will not suggest fluid if
SVV > 9% in Open Surgery
Mode or SVV ≤ 12% in
Laparoscopic / Prone
Surgery Mode |
| Image: AFM Fluid Status Icon in Navigation Bar Display with pause symbol | Image: AFM Fluid Status Icon in AFM Dashboard with pause symbol | AFM Mode is paused /
suspended
The AFM software feature
will not suggest fluid
in this state |
"A full review of the patient's hemodynamic status is recommended prior to accepting an AFM recommendation or AFM test suggestion."
"The Assisted Fluid Management feature should not be used exclusively to treat the patient. A review of the patient's hemodynamics is recommended throughout the monitoring session to assess fluid responsiveness."
- g. The device labeling shows that the performance and limitations of the Acumen IQ sensor will affect the device's fluid recommendation performance. Because: i) the Acumen IQ sensor measures stroke volume changes; ii) stroke volume changes are used to derive fluid responsiveness; iii) fluid responsiveness is used to compute predicted increase in stroke volume; and iv) predicted increase in stroke volume is used to derive the device's fluid recommendations.
- h. The device labeling shows the response rates reported in the AFM IDE study as follows.
18
| Type | |
|---|---|
| Type of Bolus Event | Mean Response Rate (%) |
| [Confidence Interval] | |
| AFM Recommendation | 66.1% [62.1, 69.7] |
| AFM Test | 60.5% [57.8, 63.2] |
Table 17-4 AFM Response Rates by Bolus
"An analysis of the response rate at the subject level demonstrates that the mean response rate was 65.62% and the median [interquartile range] per-subject response is 75% [50%, 100%] with a range from 0% to 100%."
In addition, the labeling describes the following limitations of the AFM IDE study so that users have adequate information to understand the expected performance of the device, despite the reported results of the AFM IDE study.
"The primary objective is based upon the performance of the AFM feature and the clinical decision making that occurred during the clinical study."
"Out of the 330 subjects enrolled in the study, 307 subjects were assigned to the per-protocol pivotal cohort and included in the effectiveness evaluation for the primary endpoint. In the perprotocol pivotal cohort, 94% (289/307) and 54% (165/307) of the subjects received AFM Test suggestions and AFM Recommended suggestions, respectively, and 6% of the subjects (18/307) did not receive any AFM suggestions. Therefore, it should be noted that the primary effectiveness endpoint is based on the 54% that received AFM Recommended boluses."
"User boluses during the study were recorded whenever fluid was given outside of an AFM test or recommendation while the AFM feature was in use. When the clinician administered a user bolus, there was an increase in stroke volume 40.9% [37.4, 44.1] of the time. The user boluses were not given exclusively as part of a manually administered fluid management protocol."
"In the clinical validation study, 66% of the AFM Recommended boluses produced the desired change in SV that met the Fluid Strategy as reported in Table 17-4. However, a study limitation was that fluid was not delivered when the user declined an AFM Recommendation and, as such, the SV responses of the declined AFM suggestions are unknown. If each declined AFM Recommendation was categorized as a negative response rate could be as low as 37%. Reasons for these declines included normotension, fluid contraindicated by the procedure at the present time, and clinician preference to use a vasopressor."
- i. The device labeling provides following relevant characteristics of the subjects in the AFM IDE study along with a summary of the validation results.
| Type | AFM IDE Study |
|---|---|
| # of Patients | 330 |
| Age | $64.2 \pm 12.9$ |
| BMI | $26.3 \pm 4.5$ |
| ASA 3 | 91.8% |
| ASA 4 | 8.2% |
| Table 17-3 Subject Demographics | ||
|---|---|---|
| --------------------------------- | -- | -- |
19
| Type of Bolus Event | Mean Response Rate (%)
[Confidence Interval] |
|---------------------|-------------------------------------------------|
| AFM Recommendation | 66.1% [62.1, 69.7] |
| AFM Test | 60.5% [57.8, 63.2] |
Table 17-4 AFM Response Rates by Bolus Type
- j. The Maximum Case Volume, which is a safeguard to prevent fluid overload, is described as follows in the device labeling.
wThe Maximum Case Volume provides the user with a target fluid volume delivery and is set by the clinician at the start of the case based upon available clinical data at that point. A parient's fluid needs may change over the course of the case and therefore this value should be considered as a guide and not the absolute threshold between optimal and excessive fluid delivery. During an active AFM session a visual notification pop-up is provided when the total fluid delivered through the AFM feature approaches (within 500 mL) or exceeds the pre-set Maximum Case Volume to guard against potential fluid overload."
In addition, the device labeling illustrates the following examples of user prompts associated with the Maximum Case Volume parameter. The second prompt below illustrates that the total volume delivered through the device could slightly exceed the pre-set Maximum Case Volume.
| Notification |
|---|
| -------------- |
| Total Volume Delivered delivered through AFM is | 1500 mL |
|---|---|
| This is approaching the Maximum Case Volume of | 2000 mL |
| Change Maximum Case Volume |
|---|
| Acknowledge and Continue |
| Notification |
|---|
| -------------- |
| Total Volume Delivered delivered through AFM is | 2100 mL |
|---|---|
| This has exceeded the Maximum Case Volume of | 2000 mL |
| Change the Maximum Case Volume to continue using AFM |
| Change Maximum Case Volume |
|---|
| End AFM Session |
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RISKS TO HEALTH
The table below identifies the risks to health that may be associated with use of the adjunctive open loop fluid therapy recommender and the measures necessary to mitigate these risks.
| Identified Risks to Health | Mitigation Measures |
|---|---|
| Delay in monitoring or treatment. | Software verification, validation, and hazard analysis; |
| Usability assessment; and | |
| Labeling | |
| Inappropriate or missed treatment due | |
| to over-reliance on software | |
| recommendation which is affected by: | |
| algorithm or software error, or | |
| inaccurate input from sensors or users. | Software verification, validation, and hazard analysis; |
| Non-clinical performance testing; | |
| Usability assessment; | |
| Clinical performance testing; and | |
| Labeling | |
| Fluid overload due to over-reliance on | |
| software recommendations which are | |
| affected by: algorithm or software | |
| error, or inaccurate input from sensors | |
| or users. | Software verification, validation, and hazard analysis; |
| Non-clinical performance testing; | |
| Usability assessment; | |
| Clinical performance testing; and | |
| Labeling |
SPECIAL CONTROLS
In combination with the general controls of the FD&C Act, the adjunctive open loop fluid therapy recommender is subject to the following special controls:
-
- Clinical performance testing under anticipated conditions of use must fulfill the following:
- a. A summary of the clinical performance testing must include the relevant patient demographics, and any statistical techniques used for analyzing the data;
- b. Subjects must be representative of the intended use population for the device. Any selection criteria or sample limitations must be fully described and justified;
- c. 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
- d. Testing must evaluate the relationship between algorithm recommendations, therapeutic actions, and predicted physiological event or status.
-
- A software description and the results of verification and validation testing based on a comprehensive hazard analysis and risk assessment must be provided, including:
- a. A full characterization of the software technical parameters, including algorithms;
- b. A description of the expected recommendation, accounting for differences in patient condition and environment;
- c. 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;
- d. A characterization of algorithm sensitivity to variations in user inputs;
- e. A characterization of sensor accuracy and performance:
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- f. A description of sensor data quality control measures: and
- g. Safeguards to reduce the possibility of fluid overload.
-
- 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.
-
- A human factors and usability engineering assessment must be provided.
-
- Labeling must include:
- a. 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;
- b. Detailed information regarding limitations of the device's algorithm, and key assumptions made when the device issues a recommendation:
- Warnings identifying sensor acquisition factors that may impact measurement results; ن
- d. Warnings identifying user errors that affect the device's recommendations;
- e. Detailed information regarding the expected impact of user input errors on the device recommendations:
- Guidance for interpretation of the device's recommendations, including a description f. that the recommendation is adjunctive to other physical vital sign parameters and patient information;
- g. Description of the impact of the compatible sensor(s) on the device's performance;
- The expected performance of the device for all intended patients, users, and h. environments:
- Relevant characteristics of the patients studied in the clinical validation (such as age, i. gender, race or ethnicity, and patient condition) and a summary of validation results; and
- Description of the software safeguards that are in place to prevent fluid overload, and j. description of any limitation of the software safeguards.
BENEFIT/RISK DETERMINATION
The probable benefits of the device are based on the AFM IDE study. The certainly demonstrated benefit of the device is automating a tedious and time-consuming process of manually recording hemodynamic values, fluid boluses and the associated fluid responses to allow for easier and more standardized performance of goal-directed fluid therapy. The device provides benefit by electronically recording entered boluses, along with the associated hemodynamics. On the other hand, the main proposed benefit of the device is to determine fluid responsiveness in the gray zone. The AFM IDE study results cause a high extent of uncertainty related to this benefit. An inappropriate historical control group followed by an equally inappropriate internal control group limits conclusions made from the AFM IDE study. Due to the high extent of uncertainty of this proposed benefit. it is impossible to conclude that the device provides this benefit regarding prediction of fluid responsiveness as compared to manual management.
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The probable risks of the device are based on the AFM IDE study summarized above, as well as a clinical study result from the closed-loop version of the AFM algorithm (Joosten et al., Anesthesiology. 2018 Jan;128(1):55-66). The primary risk associated with the device is fluid overload caused by acceptance of all fluid bolus recommendations by the user. Although no patients in the AFM IDE study were determined to have fluid overload, more than 50% of 2655 AFM boluses were declined or discarded, making it impossible to determine the risk of fluid overload if the user followed all bolus recommendations. Additionally, a clinical study using a closed-loop version of the AFM algorithm demonstrated that 20% of patients reached the maximum allowable fluid dose during automated bolusing (Joosten et al., Anesthesiology. 2018 Jan:128(1):55-66). Therefore, there is a moderate degree of uncertainty related to this risk of fluid overload.
To summarize, the probable benefit of improved prediction of fluid responsiveness has a high extent of uncertainty due to limitation of the AFM IDE study (e.g., due to inappropriate historical control group and inappropriate comparison with manual fluid management). Although the probable risk of fluid overload has a moderate degree of uncertainty (e.g., because about half of the recommended boluses were not accepted during the AFM IDE study), the probable risk of fluid overload in the event that the users accept all recommended fluid boluses can be mitigated by: i) requiring user entry of a Maximum Case Volume to be entered before AFM recommendations will appear, and requiring issuing of notifications for nearing and exceeding pre-set Maximum Case Volume, ii) labeling the device to thoroughly explain the limitations of the clinical study in assessing benefit and risk of the device, iii) labeling the device to thoroughly explain the conditions leading to bolus recommendations and the potential frequency of bolus recommendations.
Patient Perspectives
This submission did not include specific information on patient perspectives for this device.
Benefit/Risk Conclusion
In conclusion, given the available clinical and pre-clinical information summarized above, the data support that for the indications for use specified above, the probable benefits outweigh the probable risks for the Acumen Assisted Fluid Management Software Feature. The device provides benefits and the risks can be mitigated by the use of general controls and the identified special controls.
CONCLUSION
The De Novo request for the Acumen Assisted Fluid Management Software Feature is granted and the device is classified under the following:
Product Code: OMS Device Type: Adjunctive open loop fluid therapy recommender Class: II Regulation: 21 CFR 870.5600