K212219 · Fifth Eye, Inc. · QNV · Dec 3, 2021 · Cardiovascular
Device Facts
Record ID
K212219
Device Name
AHI System
Applicant
Fifth Eye, Inc.
Product Code
QNV · Cardiovascular
Decision Date
Dec 3, 2021
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 870.2220
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K212219 · Dec 3, 2021
AHI System
Fifth Eye, Inc.
Hospital-based continuous physiological monitoring data (ECG, arterial line, vital signs)
The sponsor used a prospective clinical dataset of hospitalized patients to validate the AHI-PI predictive indicator's ability to distinguish risk levels for future hemodynamic instability episodes.
Hospitalized patients 18 years or older receiving continuous physiological monitoring with ECG and invasive arterial line monitoring; Sample Size: 65,969 windows (from consecutive patients); Number of Sites: 1 (Michigan Medicine)
Not applicable for this study
Prediction of hemodynamic instability (defined as 10 continuous minutes of hypotension/tachycardia) within 1 hour
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Hemodynamic Instability (HI) Prediction
Pattern analysis of lead-II ECG waveform and AHI outputs
—
Low Risk (Green): 0.7% probability; Moderate Risk (Yellow): 6.5% probability; High Risk (Red): 35.9% probability
—
—
Prospective clinical study at Michigan Medicine: 65,969 windows from patients invasively monitored with an arterial line.
—
Indications for Use
The AHI System is intended for use by healthcare professionals managing patients 18 years or older who are receiving continuous physiological monitoring with electrocardiography (ECG) in hospitals. AHI provides a frequently updated binary output over time based on pattern analysis of a lead-II ECG waveform intended to describe a patient's hemodynamic status and indicate if a patient is showing signs of hemodynamic stability or instability. Signs of hemodynamic instability (HI) are defined as hypotension (systolic blood pressure <90 mmHg or mean arterial pressure (MAP) <70 mmHg) combined with tachycardia (heart rate ≥ 100 bpm). AHI-PI provides the clinician with physiological insight into a patient's likelihood of a future episode of HI. An episode of HI is defined as 10 continuous minutes or more where HI is present. The goal of this adjunctive monitoring method is to enable identification of patients who are likely to experience a future episode of HI, and to allow clinicians an opportunity to increase vigilance. This device is intended for adjunctive use with other physical vital sign parameters and patient information and is not intended to independently direct therapy.
Device Story
The AHI System is a software-only clinical decision support tool for hospital use. It processes continuous lead-II ECG waveform data to provide two outputs: AHI (current hemodynamic status) and AHI-PI (predictive indicator for future hemodynamic instability). AHI analyzes heart rate variability and ECG morphology to categorize status as stable or unstable. AHI-PI uses the most recent 30 minutes of AHI outputs to estimate the likelihood of a future hemodynamic instability episode (defined as 10+ minutes of hypotension and tachycardia). The system displays color-coded indicators (red, yellow, green) to clinicians, enabling increased vigilance for patient deterioration. It serves as an adjunctive tool alongside standard vital signs and patient information; it does not independently direct therapy. The device benefits patients by providing early warning of potential hemodynamic instability, potentially reducing missed diagnoses of patient deterioration.
Clinical Evidence
Clinical validation involved a prospective study of 65,969 data windows from patients with arterial lines. AHI-PI performance was compared against a reference standard of hemodynamic instability (hypotension <90 mmHg systolic or <70 mmHg MAP + tachycardia ≥100 bpm). Results showed significant risk discrimination: High-Risk (red) indicators were 51x more likely and Moderate-Risk (yellow) indicators 9x more likely to experience an instability episode within 1 hour compared to Low-Risk (green) indicators. AHI-PI predicted 89% of deterioration cases with a median lead time of 48 minutes.
Technological Characteristics
Software-as-a-medical-device (SaMD). Inputs: lead-II ECG waveform and prior AHI outputs. Processing: pattern analysis of ECG morphology and heart rate variability; mathematical model for risk prediction. Outputs: binary hemodynamic status and color-coded (red/yellow/green) risk indicators. Connectivity: receives data from FDA-cleared patient monitors. No electrical components or patient-contacting materials.
Indications for Use
Indicated for patients 18 years or older receiving continuous ECG monitoring in hospitals. Used to identify hemodynamic stability/instability and predict future episodes of hemodynamic instability (defined as ≥10 minutes of hypotension <90 mmHg systolic or <70 mmHg MAP combined with tachycardia ≥100 bpm). Adjunctive use only; not for independent therapy direction.
Regulatory Classification
Identification
An adjunctive hemodynamic indicator with decision point is a device that identifies and monitors hemodynamic condition(s) of interest and provides notifications at a clinically meaningful decision point. This device is intended to be used adjunctively along with other monitoring and patient information.
Special Controls
In combination with the general controls of the FD&C Act, the adjunctive hemodynamic indicator with decision point is subject to the following special controls:
*Classification.* Class II (special controls). The special controls for this device are:(1) Software description, verification, and validation based on comprehensive hazard analysis and risk assessment must be provided, including:
(i) Full characterization of technical parameters of the software, including algorithm(s);
(ii) Description of the expected impact of all applicable sensor acquisition hardware characteristics on performance and any associated hardware specifications;
(iii) Specification of acceptable incoming sensor data quality control measures;
(iv) Mitigation of impact of user error or failure of any subsystem components (signal detection and analysis, data display, and storage) on output accuracy; and
(v) The sensitivity, specificity, positive predictive value, and negative predictive value in both percentage and number form for clinically meaningful pre-specified time windows consistent with the device output.
(2) Scientific justification for the validity of the hemodynamic indicator algorithm(s) must be provided. Verification of algorithm calculations and validation testing of the algorithm must use an independent data set.
(3) Usability assessment must be provided to demonstrate that risk of misinterpretation of the status indicator is appropriately mitigated.
(4) Clinical data must support the intended use and include 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) Output measure(s) must be compared to an acceptable reference method to demonstrate that the output represents the measure(s) that the device provides in an accurate and reproducible manner;
(iii) The data set must be representative of the intended use population for the device. Any selection criteria or limitations of the samples must be fully described and justified;
(iv) Where continuous measurement variables are displayed, agreement of the output with the reference measure(s) must be assessed across the full measurement range; and
(v) Data must be provided within the clinical validation study or using equivalent datasets to demonstrate the consistency of the output and be representative of the range of data sources and data quality likely to be encountered in the intended use population and relevant use conditions in the intended use environment.
(5) Labeling must include the following:
(i) The type of sensor data used, including specification of compatible sensors for data acquisition, and a clear description of what the device measures and outputs to the user;
(ii) Warnings identifying factors that may impact output results;
(iii) Guidance for interpretation of the outputs, including warning(s) specifying adjunctive use of the measurements;
(iv) Key assumptions made in the calculation and determination of measurements; and
(v) A summary of the clinical validation data, including details of the patient population studied (
*e.g.,* age, gender, race/ethnicity), clinical study protocols, and device performance with confidence intervals for all intended use populations.
Predicate Devices
Analytic for Hemodynamic Instability (AHI) (DEN200022)
CLEWICU System (ClewICUServer And ClewICUnitor) (K200717)
Submission Summary (Full Text)
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December 3, 2021
Fifth Eye Inc. % Donna-Bea Tillman Senior Consultant Biologics Consulting Group 1555 King Street. Suite 300 Alexandria, Virginia 22314
Re: K212219
Trade/Device Name: AHI System Regulation Number: 21 CFR 870.2220 Regulation Name: Adjunctive hemodynamic indicator with decision point Regulatory Class: Class II Product Code: QNV, QNL Dated: November 3, 2021 Received: November 4, 2021
Dear Donna-Bea Tillman:
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 (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 located 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.
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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 of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-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 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 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-device-safety/medical-device-reportingmdr-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/medicaldevices/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-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely.
for
LCDR Stephen Browning Assistant Director Division of Cardiac Electrophysiology, Diagnostics, and Monitoring Devices Office of Cardiovascular Devices Office of Product Evaluation and Ouality Center for Devices and Radiological Health
Enclosure
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### Indications for Use
510(k) Number (if known) K212219
Device Name AHI System
### Indications for Use (Describe)
The AHI System is intended for use by healthcare professionals managing patients 18 years or older who are receiving continuous physiological monitoring with electrocardiography (ECG) in hospitals.
AHI provides a frequently updated binary output over time based on pattern analysis of a lead-II ECG waveform intended to describe a patient's hemodynamic status and indicate if a patient is showing signs of hemodynamic stability or instability. Signs of hemodynamic instability (HI) are defined as hypotension (systolic blood pressure <90 mmHg or mean arterial pressure (MAP) <70 mmHg) combined with tachycardia (heart rate ≥ 100 bpm).
AHI-PI provides the clinician with physiological insight into a patient's likelihood of a future episode of HI. An episode of HI is defined as 10 continuous minutes or more where HI is present.
The goal of this adjunctive monitoring method is to enable identification of patients who are likely to experience a future episode of HI, and to allow clinicians an opportunity to increase vigilance. This device is intended for adjunctive use with other physical vital sign parameters and patient information and is not intended to independently direct therapy.
Type of Use *(Select one or both, as applicable)*
| | <span> ☒ Prescription Use (Part 21 CFR 801 Subpart D) </span> |
|--|-------------------------------------------------------------------------------------------|
| | <span> ☐ Over-The-Counter Use (21 CFR 801 Subpart C) </span> |
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In accordance with 21 CFR 807.87(h) and 21 CFR 807.92 the 510(k) Summary for the AHI System is provided below.
#### 1. SUBMITTER
| Applicant: | Fifth Eye Inc.<br>110 Miller Avenue, Suite 300<br>Ann Arbor, MI 48104 |
|---------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Contact: | Jen Baird<br>Chief Executive Officer<br>Fifth Eye Inc.<br>734.260.4800<br>jbaird@fiftheye.com |
| Submission Correspondent: | Donna-Bea Tillman, Ph.D.<br>Senior Consultant<br>Biologics Consulting<br>1555 King St, Suite 300<br>Alexandria, VA 22314<br>410.531.6542<br>dtillman@biologicsconsulting.com |
| Date Prepared: | November 2, 2021 |
#### 2. DEVICE
| Device Trade Name: | AHI System |
|--------------------------|-------------------------------------------------------------------------|
| Device Common Name: | Hemodynamic Indicator |
| Classification Name | 21 CFR 870.2220 Adjunctive Hemodynamic<br>Indicator with Decision Point |
| Regulatory Class: | II |
| Primary Product Code: | QNV |
| Subsequent Product Code: | QNL |
#### 3. PREDICATE DEVICE
Predicate Device: Analytic for Hemodynamic Instability (AHI) - DEN200022 Secondary Predicate Device: CLEWICU System (ClewICUServer And ClewICUnitor) -K200717
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#### DEVICE DESCRIPTION 4.
The AHI System is a multiparameter system designed to meet clinicians' need to identify patient hemodynamic status and predict patient hemodynamic instability episodes using two analytics:
1. Analytic for Hemodynamic Instability (AHI) (as granted in DEN200022): Utilizing data from a single existing lead of a non-invasive electrocardiograph (ECG), AHI analyzes heart rate variability (HRV) and ECG morphology features to rapidly detect signs of hemodynamic stability or instability and categorize each window of data as either "AHI Stable" or "AHI Unstable." Time trending of AHI outputs is also provided.
2. Analytic for Hemodynamic Instability Predictive Indicator (AHI-PI): Utilizing AHI outputs from up to the most recent 30 minutes of ECG data, AHI-PI indicates the likelihood of a future episode of hemodynamic instability, defined as ten continuous minutes or more where signs of hemodynamic instability are present.
### INTENDED USE/INDICATIONS FOR USE 5.
The AHI System is intended for use by healthcare professionals managing patients 18 years or older who are receiving continuous physiological monitoring with electrocardiography (ECG) in hospitals.
AHI provides a frequently updated binary output over time based on pattern analysis of a lead-II ECG waveform intended to describe a patient's hemodynamic status and indicate if a patient is showing signs of hemodynamic stability or instability. Signs of hemodynamic instability (HI) are defined as hypotension (systolic blood pressure <90 mmHg or mean arterial pressure (MAP) <70 mmHg) combined with tachycardia (heart rate ≥ 100 bpm).
AHI-PI provides the clinician with physiological insight into a patient's likelihood of a future episode of HI. An episode of HI is defined as 10 continuous minutes or more where HI is present.
The goal of this adjunctive monitoring method is to enable identification of patients who are showing HI or are likely to experience a future episode of HI, and to allow clinicians an opportunity to increase vigilance. This device is intended for adjunctive use with other physical vital sign parameters and patient information and is not intended to independently direct therapy.
### 6. SUBSTANTIAL EQUIVALENCE
# Comparison of Indications
The subject device and both of the predicate devices are all software decision support systems that receive patient data, process it, and display calculated insights into patient hemodynamic and cardiovascular status to support meaningful clinical decisions.
The subject and both predicate devices are all prescription devices intended for use by healthcare professionals. They are intended for use in adult patients 18 years and older and are intended for adjunctive use with other monitoring and patient information and are not intended to
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independently direct therapy. The subject device and primary predicate device are both intended for use on patients in hospitalized settings who are receiving continuous physiological monitoring with electrocardiography (ECG). The subject device and secondary predicate device both are intended to provide physiological insight into a patient's likelihood of future hemodynamic instability.
### Technological Comparisons
The table below compares the key technological feature of the subject devices to the predicate devices.
| | AHI System | Primary Predicate | Secondary Predicate |
|---------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 510(k)<br>Number | AHI System | DEN200022 | K200717 |
| Applicant | Fifth Eye Inc. | Fifth Eye Inc. | CLEW Medical Ltd. |
| Device Name | AHI System | Analytic for<br>Hemodynamic Instability<br>(AHI) | CLEWICU System<br>(ClewICUServer And<br>ClewICUnitor) |
| Classification<br>Regulation | 860.2220. Adjunctive<br>Hemodynamic Indicator<br>With Decision Point<br>870.2210. Medium-Term<br>Adjunctive Predictive<br>Cardiovascular Indicator | 860.2220. Adjunctive<br>Hemodynamic Indicator<br>With Decision Point | 870.2210. Medium-Term<br>Adjunctive Predictive<br>Cardiovascular Indicator |
| Product<br>Code | QNV, QNL | QNV | QNL |
| Type of<br>Device | Software-as-a-medical-<br>device | Software-as-a-medical-<br>device | Software-as-a-medical-<br>device |
| Timing of<br>Analytic<br>Inputs | Near-real-time patient<br>health data | Near-real-time patient<br>health data | Near-real-time patient<br>health data |
| Analytic<br>Inputs | Inputs are based on signals<br>received from FDA-<br>cleared patient monitoring<br>systems, specifically:<br>For AHI: ECG-II<br>For AHI-PI: AHI Outputs | Inputs are based on signals<br>received from FDA-<br>cleared patient monitoring<br>systems, specifically:<br>ECG-II | Inputs are based on signals<br>received from FDA-<br>cleared patient monitoring<br>systems and other values<br>stored in the electronic<br>health record (EHR)<br>system, specifically:<br>Up to 80 EHR inputs<br>including vital signs,<br>nursing assessments, |
| | AHI System | Primary Predicate | Secondary Predicate |
| | | | flowsheet data,<br>medications, and lab data |
| Technical<br>Method | Uses analysis of patient<br>data (ECG-II) to detect<br>current signs of a<br>hemodynamic condition<br>(AHI) and combines<br>patient data from patient<br>monitors to produce a<br>single value (AHI-PI) that<br>estimates the likelihood of<br>a future adverse<br>cardiovascular event or<br>condition from a single<br>model mathematically<br>derived from AHI inputs. | Uses pattern analysis of<br>patient data (ECG-II) to<br>detect current signs of a<br>hemodynamic condition | Combines patient data<br>collected from patient<br>monitors and/or electronic<br>health records to produce a<br>single value that estimates<br>the likelihood of a future<br>adverse cardiovascular<br>event or condition using<br>two models, a high risk<br>model and a low risk<br>model. |
| Analytic<br>Outputs | AHI: A frequently updated<br>binary output over time<br><br>AHI-PI: A single indicator<br>that estimates the<br>likelihood of a future<br>adverse cardiovascular<br>event (hemodynamic<br>instability episode) | AHI: A frequently updated<br>binary output over time | Single CLEWICU<br>indicator that estimates the<br>likelihood of a future<br>adverse cardiovascular<br>event (hemodynamic<br>instability) |
| Visual Alerts | AHI: Signs of<br>hemodynamic status<br>provided by red and green<br>color-coded indicators<br><br>AHI-PI: Risk of future<br>hemodynamic episode<br>provided by red, yellow,<br>and green color-coded<br>indicators | AHI: Signs of<br>hemodynamic status<br>provided by red and green<br>color-coded indicators | CLEWICU: Risk of<br>hemodynamic instability<br>provided by red, yellow,<br>and green color-coded<br>indicators |
Table 1: Technological Comparison
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In conclusion, the differences in the intended use and technological characteristics do not raise new questions of safety and effectiveness. Based on the detailed comparison between the predicate devices and the subject device, the performance testing, and conformance with applicable standards, the AHI System can be found substantially equivalent to the predicate devices.
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#### 7. PERFORMANCE DATA
### Biocompatibility Testing
The AHI System is a software only device. There are no direct or indirect patient-contacting components of the subject device; therefore, patient contact information is not needed for this device.
### Electrical safety and electromagnetic compatibility (EMC)
Not applicable. The AHI System is a software-only device. It contains no electric components, generates no electrical emissions, and uses no electrical energy of any type.
## Software Verification and Validation Testing
Software verification and validation testing were conducted and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." The software for this device was considered as a moderate level of concern. Testing was conducted to ensure the AHI System works as designed.
# Bench Testing
Fifth Eye conducted a summative usability study according to the FDA 's guidance on Applying Human Factors and Usability Engineering to Medical Devices. Fifth Eve recruited 30 users to participate in the study. The results of the human factors validation study were positive and demonstrated that mitigations addressing use-related risk made during device development were effective.
# Animal Testing
Not applicable. Animal studies are not necessary to establish the substantial equivalence of this device.
## Clinical Data
The clinical data to support AHI was provided in DEN200022. There have been no changes to AHI since that submission.
To validate that AHI-PI predicts a patient's likelihood of a future hemodynamic instability episode, defined as 10 continuous minutes or more where signs of hemodynamic instability are present, we compared AHI-PI outputs to episodes of hemodynamic instability as observed using a hemodynamic vital signs reference standard - Hypotension (systolic blood pressure <90mmHg or mean arterial pressure (MAP) <70 mmHg) combined with tachycardia (heart rate ≥100 bpm).
The targeted patient population for the AHI System is hospitalized patients 18 years or older who are receiving continuous physiological monitoring with electrocardiography (ECG) and are not contraindicated. While ideally the clinical study would reflect the full spectrum of levels of care for ECG-monitored patients, study design considerations required limiting the primary study population to only patients who were invasively monitored with an arterial line. Therefore,
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beginning at the study start date, data were prospectively collected from consecutive patients in equipped beds (those that could store continuous measures of ECG, arterial line numeric data, and other vital signs) at Michigan Medicine.
As requested by FDA, and using bootstrapping to account for multiple measurements per subject, Fifth Eye has demonstrated that AHI-PI can distinguish the risk of developing hemodynamic instability for patients in different risk groups, i.e., those that are at low risk (green), moderate risk (yellow), and high risk (red).
| AHI-PI Indicator | Windows in<br>Primary Analysis<br>Set (total 65,969<br>windows) n (%)<br>[2] | Probability of an Episode of<br>Hemodynamic Instability in<br>the next 1 hour measured<br>using continuous vital signs [1]<br>(Observed [95% confidence<br>interval]) | Likelihood Ratio |
|--------------------------------------------|------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| AHI-PI Low Risk<br>Indicator<br>(Green) | 43,443<br>(65.8%) | 0.7%<br>[0.4%, 1.3%] | N/A |
| AHI-PI Moderate Risk<br>Indicator (Yellow) | 4,999<br>(7.6%) | 6.5%<br>[3.7%, 10.3%] | An AHI-PI Moderate Risk<br>indication is 9x more likely to<br>have an episode of<br>hemodynamic instability in the<br>next 1 hour than an AHI-PI<br>Low Risk Indicator. |
| AHI-PI High Risk<br>Indicator (Red) | 17,527<br>(26.6%) | 35.9%<br>[28.1%, 44.0%] | An AHI-PI High Risk<br>indication is 51x more likely<br>to have an episode of<br>hemodynamic instability in the<br>next 1 hour than an AHI-PI<br>Low Risk Indicator. |
### Probability estimates of the occurrence of an episode of hemodynamic instability in Table 2: the prediction window for each of the AHI-PI indicators (Study Population).
[1] The unit of analysis here is at the windows level and not the patient level.
[2] Windows included in the analysis total 65,969 to ensure that a full one hour is available for each AHI-Pl in order to determine whether an episode of hemodynamic instability is present or not.
As seen in Table 2, with the prevalence of 10.5% seen in the Primary Analysis Set population using the continuous vital signs reference standard, the probability of an episode of hemodynamic instability in the next 1 hour measured using continuous vital signs was 0.7%, 6.5%, and 35.9% for green, yellow, and red AHI-PI indicators respectively. An AHI-PI High Risk (red) indication is 51 times more likely and an AHI-PI Moderate Risk (yellow) indication is 9 times more likely to have an episode of hemodynamic instability in the next hour as compared to an AHI-PI Low Risk (green) indication. Thus, the risk predication and likelihood performance data show significant discrimination between the risk of future hemodynamic instability events between the different AHI-PI indications, demonstrating a clinical benefit by providing
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adjunctive information to the clinician to facilitate fewer missed diagnoses of emerging hemodynamic instability/patient deterioration.
The other relevant metric is lead time. Of those patients who had an episode of hemodynamic instability, AHI-PI High Risk predicted patient deterioration in 89% of cases. The median lead time was calculated as 48 minutes. Although this lead time is less that the 3 hours reported for CLEWICU, it still provides an advance warning to clinicians that the patient is at risk of future hemodynamic instability. The differences in the performance do not impact the safety or effectiveness, as neither device is intended to be the sole factor upon which a patient's clinical care is based.
#### CONCLUSION 8.
The subject AHI System and the predicate devices have equivalent intended uses. The differences in technological characteristics do not raise different questions of safety and effectiveness, and the results of software verification, human factors and clinical validation demonstrate that the subject device performs in accordance with specifications and meets user needs and intended uses. Therefore, the AHI System has been demonstrated to be substantially equivalent to the predicate devices.
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Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.
