Hypertension Prediction Index (HePI) Algorithm

K242518 · Edwards Lifesciences, LLC · QAQ · May 15, 2025 · Cardiovascular

Device Facts

• Record ID: K242518
• Device Name: Hypertension Prediction Index (HePI) Algorithm
• Applicant: Edwards Lifesciences, LLC
• Product Code: QAQ · Cardiovascular
• Decision Date: May 15, 2025
• Decision: SESE
• Submission Type: Traditional
• Regulation: 21 CFR 870.2210
• Device Class: Class 2
• Attributes: AI/ML, Software as a Medical Device

AI Performance

Output	Acceptance	Observed	Dev DS	Test DS
Hypertensive Event Prediction	Sensitivity and specificity above 80%	Minimally Invasive: AUC 0.989, Sensitivity 99.7%, Specificity 93.7%; Non-invasive: AUC 0.990, Sensitivity 99.6%, Specificity 91.6%	Not specified	Retrospective clinical data from multiple independent datasets: Minimally Invasive (N=1813) and Non-invasive (N=1351) patients, including surgical and non-surgical cohorts.

Indications for Use

The Hypertension Prediction Index (HePI) software provides the clinician with physiological insight into an adult (18 years and older) patient's likelihood of future hypertensive events. The HePI software is intended for use in surgical or non-surgical patients receiving advanced hemodynamic monitoring. The HePI software is considered to be additional quantitative information regarding the patient's physiological condition for reference only and no therapeutic decisions should be made solely on the HePI parameter.

Device Story

HePI software processes hemodynamic data from minimally invasive sensors or non-invasive finger cuffs to predict hypertensive events (MAP >115 mmHg for 1 min or >20% increase when MAP >95 mmHg). Algorithm updates every 20 seconds, outputting a 0-100 likelihood score. Used in hospital critical care settings by clinicians. When score exceeds 85 for two consecutive readings, the system triggers a visual alarm (red trend graph). Provides adjunctive quantitative information to assist clinicians in early intervention; does not replace clinical judgment or support autonomous therapeutic decisions.

Clinical Evidence

Retrospective analysis of clinical data from 3,164 subjects (minimally invasive and non-invasive monitoring). Performance metrics for a 15-minute search window: AUC 0.986–0.992; Sensitivity 99.5%–99.9%; Specificity 90.6%–94.3%. Results demonstrate high predictive accuracy across surgical and non-surgical cohorts.

Technological Characteristics

Software-based algorithm; utilizes machine learning model. Inputs: hemodynamic data from minimally invasive sensors or non-invasive finger cuffs. Outputs: 0-100 likelihood score. Updates every 20 seconds. Complies with ANSI/AAMI/IEC 62304:2006/A1:2016 software life cycle processes.

Indications for Use

Indicated for adult patients (18+ years) undergoing surgical or non-surgical procedures requiring advanced hemodynamic monitoring to provide physiological insight into the likelihood of future hypertensive events.

Regulatory Classification

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.

Special Controls

*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.

Predicate Devices

• HemoSphere Advanced Monitoring Platform (K213682)

Submission Summary (Full Text)

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FDA U.S. FOOD & DRUG ADMINISTRATION

May 15, 2025

Edwards Lifesciences LLC
Chirag Shah
Director, Regulatory Affairs Program Management
17200 Laguna Canyon Rd
Irvine, California 92602

Re: K242518
Trade/Device Name: Hypertension Prediction Index (HePI) Algorithm
Regulation Number: 21 CFR 870.2210
Regulation Name: Adjunctive Predictive Cardiovascular Indicator
Regulatory Class: Class II
Product Code: QAQ
Dated: April 11, 2025
Received: April 11, 2025

Dear Chirag Shah:

We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov

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K242518 - Chirag Shah
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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.

For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-

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K242518 - Chirag Shah
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assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email ([e-mail]) or phone [phone] or 301-796-7100).

Sincerely,

Robert T. Kazmierski -S

for

LCDR Stephen Browning

Assistant Director

Division of Cardiac Electrophysiology,

Diagnostics, and Monitoring Devices

Office of Cardiovascular Devices

Office of Product Evaluation and Quality

Center for Devices and Radiological Health

Enclosure

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DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration
Indications for Use
Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.

Submission Number (if known)
K242518

Device Name
Hypertension Prediction Index (HePI) Algorithm

Indications for Use (Describe)
The Hypertension Prediction Index (HePI) software provides the clinician with physiological insight into an adult (18 years and older) patient's likelihood of future hypertensive events. The HePI software is intended for use in surgical or non-surgical patients receiving advanced hemodynamic monitoring.

The HePI software is considered to be additional quantitative information regarding the patient's physiological condition for reference only and no therapeutic decisions should be made solely on the HePI parameter.

Type of Use (Select one or both, as applicable)
☑ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)

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Edwards Lifesciences, LLC
K242518
Page 1 of 4

| Hypertension Prediction Index (HePI) Algorithm | | |
| --- | --- | --- |
| **510(k)**
Submitter | Edwards Lifesciences LLC
One Edwards Way, Irvine, CA 92614
[phone] | |
| **Contact Person** | **Primary Contact**
Chirag Shah
Director, RA Program Management
Edwards Lifesciences LLC
17200 Laguna Canyon Rd,
Irvine, CA 92602
Email: [e-mail] | **Secondary Contact**
Karen Clement
Senior Director, Regulatory Affairs
Edwards Lifesciences LLC
17200 Laguna Canyon Rd,
Irvine, CA 92602
Email: [e-mail] |
| **Date Prepared** | May 15, 2025 | |
| **Trade Name** | Hypertension Prediction Index (HePI) | |
| **Regulation Number / Name** | 21 CFR 870.2210 / Adjunctive Predictive Cardiovascular Indicator | |
| **Product Code** | QAQ | |
| **Regulation Class** | Class II | |
| **Primary Predicate Device** | HemoSphere Advanced Monitoring Platform, manufactured by Edwards Lifesciences, K213682 cleared June 22, 2022, utilized for the Acumen™ Hypotension Prediction Index (HPI) software. | |

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Edwards Lifesciences, LLC
K242518
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# Hypertension Prediction Index (HePI) Algorithm

| Device Description | The subject HePI is an algorithm providing the clinician with the likelihood that the patient may be trending towards a hypertensive event, which allows clinicians to take appropriate action sooner than if the algorithm was not available. For the subject algorithm, 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. The software triggers an alert screen whenever the HePI parameter value is greater than 85 for two consecutive readings. The HePI value is updated every 20 seconds and displayed as a value equating to the likelihood that a hypertensive event may occur on a scale from 0 to 100. The higher the value, the higher the likelihood of a hypertensive event.

When the HePI value reaches the default alarm threshold of 85, which cannot be changed by the user, the HePI value and trended graph turn red. After 2 continuous samples above 85, an alarm will also trigger. The acceptability criteria for the validation of the HePI algorithm is defined as a sensitivity and specificity above 80%. |
| --- | --- |
| Indications for Use | The Hypertension Prediction Index (HePI) software provides the clinician with physiological insight into an adult (18 years or older) patient’s likelihood of future hypertensive events. The HePI software is intended for use in surgical or non-surgical patients receiving advanced hemodynamic monitoring.

The HePI software is considered to be additional quantitative information regarding the patient’s physiological condition for reference only and no therapeutic decisions should be made based solely on the HePI parameter. |
| Intended Use | The Hypertension Prediction Index (HePI) software is intended to be used by qualified personnel or trained clinicians in a critical care environment in a hospital setting. |
| Parameter | Description | Patient Population | Hospital Environment |
| --- | --- | --- | --- |
| HePI | Hypertension Predication Index | Adult Only | Surgical and non-surgical |

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# Hypertension Prediction Index (HePI) Algorithm

| Comparative Analysis | The subject HePI Algorithm is substantially equivalent to the predicate HemoSphere Advanced Monitoring Platform, with Acumen™ Hypotension Prediction Index (HPI). The subject and predicate devices share similar intended use and indications for use. The subject and predicate maintain the same overall technological characteristics, maintain the same architecture, incorporate the same input, and utilize the same processing and output steps as the primary predicate. |
| --- | --- |
| | The subject differs from the predicate device since the subject device is a new software algorithm that predicts the likelihood that the patient may be trending toward a hypertensive event instead of hypotensive event and uses a slightly different machine learning model to output the prediction index. |
| | Performance testing executed shows that there are no new concerns of safety and effectiveness. |
| Performance Data (Bench and/or Clinical) | The following verification activities were performed in support of a substantial equivalence determination for the subject Hypertension Prediction Index (HePI) Algorithm. | | | | |
| --- | --- | --- | --- | --- | --- |
| | Algorithm Verification Algorithm performance was tested using retrospective clinical data. Algorithm verification was performed per FDA's Guidance for Industry and FDA Staff, Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices (issued May 11, 2005) and ANSI/AAMI/IEC 62304:2006/A1:2016, Medical Device Software - Software Life Cycle Processes. The algorithm was tested at the algorithm level to ensure the safety of the device. All tests passed. | | | | |
| | The following clinical performance data was submitted to support the indications for use of the subject device. | | | | |
| | Patient Demographics: Table 1: Test Dataset Demographics for US and OUS patients | | | | |
| | | Minimally Invasive Sensor | | Non-Invasive Finger Cuff | |
| | | US | OUS | US | OUS |
| | Number of subjects | 1615 | 198 | 464 | 887 |
| | Data Per Patient (min) | 1483.5 ± 11640.9 | 1248.0 ± 305.2 | 215.4 ± 89.9 | 288.7 ± 201.3 |
| | Gender | 915 Male, 700 Female | 146 Male, 52 Female | 262 Male, 202 Female | 493 Male, 394 Female |
| | Age (year) | 58.9 ± 17.1 | 64.5 ± 11.7 | 61.3 ± 11.1 | 58.0 ± 15.3 |
| | Height (cm) | 170.1 ± 11.1 | 172.3 ± 10.5 | 171.1 ± 9.6 | 173.4 ± 12.4 |
| | Weight (kg) | 83.2 ± 25.0 | 83.3 ± 20.3 | 94.4 ± 25.9 | 80.9 ± 18.9 |
| BSA (m2) | 1.9 ± 0.3 | 2.0 ± 0.3 | 2.0 ± 0.3 | 1.9 ± 0.2 | |

Edwards Lifesciences, LLC

K242518

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Traditional 510(k) Premarket Notification for Hypertension Prediction Index (HePI) Algorithm

# Hypertension Prediction Index (HePI) Algorithm

| | Patient Baseline Characteristics: The validation dataset had no inclusion/exclusion criteria related to patient baseline characteristics. In general, adult surgical and non-surgical patients who were monitored via an A-line and/or a continuous non-invasive blood pressure monitoring cuff were randomly selected for retrospective analysis. Results of Clinical Performance Testing Prospective analyses of retrospective clinical data from multiple independent datasets, comprised of data from patients over the age of 18 years undergoing surgical and non-surgical procedures with minimally invasive and non-invasive monitoring, were analyzed to verify the safety and performance of the subject device. The results are as shown in Table 2. Table 2: ROC results of the subject HePI device for minimally invasive and non-invasive, surgical and non-surgical patients, for 15-minute search window and 5-minute step size | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | Dataset | AUC | Sensitivity (%) | Specificity (%) | PPV (%) | NPV (%) | Cutoff |
| | Minimally Invasive (N=1813) | 0.989 [0.987, 0.991] | 99.7 [99.6, 99.9] | 93.7 [92.9, 94.3] | 73.5 [71.1, 75.8] | 100.0 [99.9, 100.0] | 85 |
| | Minimally Invasive (N=672, non-surgical) | 0.989 [0.986, 0.992] | 99.6 [99.3, 99.9] | 94.3 [93.4, 95.2] | 68.0 [64.4, 71.8] | 100.0 [99.9, 100.0] | 85 |
| | Minimally Invasive (N=1141, surgical) | 0.990 [0.989, 0.992] | 99.9 [99.7, 100.0] | 91.5 [90.6, 92.4] | 81.1 [79.4, 82.6] | 100.0 [99.9, 100.0] | 85 |
| | Non-invasive (N=1351) | 0.990 [0.987, 0.992] | 99.6 [99.1, 100.0] | 91.6 [90.7, 92.5] | 71.3 [68.8, 73.8] | 99.9 [99.8, 100.0] | 85 |
| | Noninvasive (N=424, non-surgical) | 0.986 [0.981, 0.990] | 99.9 [99.5, 100.0] | 90.6 [88.8, 92.4] | 64.2 [58.4, 69.8] | 100.0 [99.9, 100.0] | 85 |
| | Noninvasive (N=927, surgical) | 0.992 [0.989, 0.995] | 99.5 [98.7, 100.0] | 92.3 [91.3, 93.3] | 75.4 [72.8, 77.9] | 99.9 [99.7, 100.0] | 85 |
| | | | | | | | |
| | Conclusion | The subject HePI algorithm has successfully passed functional and performance testing, including software and algorithm verification and validation and bench studies. Completion of all performance verification and validation activities demonstrated that the subject device meets the predetermined design and performance specifications. Verification activities performed confirmed that the differences in the features did not adversely affect the safety and effectiveness of the subject device. The testing performed demonstrates that the HePI software is substantially equivalent to its legally marketed predicate. | | | | | |

Edwards Lifesciences, LLC

K242518