Healthy volunteer study (standard activities of daily living and altitude change perturbation).
—
Indications for Use
The Personalized Physiology Engine (PPA Engine) is intended to be used with data from already cleared sensors measuring physiological parameters, including heart rate, respiratory rate, and activity in ambulatory patients being monitored in a healthcare facility or at home. The device provides a time series Multivariate Change Index (MCI) which indicates whether the relationships among the patient's monitored vital signs change from those measured at baseline, which has been derived from measurements previously obtained during routine activities of daily living. The MCI is based on an integrated computation evaluating changes in the parameters and their relationships to each other. The PPA Engine is an adjunct to and is not intended to replace vital signs monitoring. The MCI is intended for daily intermittent, retrospective review by a qualified practitioner. The PPA Engine is intended to provide additional information for use during routine patient monitoring. The MCI is not intended for making clinical decisions regarding patient treatment or for diagnostic purposes.
Device Story
PPA Engine is a software-only device that processes heart rate, respiratory rate, and activity data from cleared sensors. It establishes a personalized baseline for each patient based on their routine activities of daily living. The device continuously analyzes incoming vital sign data to calculate a Multivariate Change Index (MCI), a scalar value from 0 to 1 representing the likelihood that current vital sign relationships deviate from the established baseline. The MCI is presented as a time series trend graph for retrospective review by clinicians. Used in healthcare facilities or home settings, the device serves as an adjunct to standard monitoring. It does not provide real-time alerts or diagnostic outputs; instead, it provides supplemental information to assist clinicians in monitoring ambulatory patients. By identifying shifts in physiological relationships, it aims to provide additional context for patient status during routine monitoring.
Clinical Evidence
Clinical validation conducted via IRB-approved study with healthy volunteers. Data collected during activities of daily living and during altitude-induced physiological perturbation. Results demonstrated that the Multivariate Change Index (MCI) correlates with changes in monitored vital sign relationships compared to the subject's personalized baseline, supporting the device's intended use.
Technological Characteristics
Software-only device. Computes a non-linear Multivariate Change Index (MCI) based on integrated evaluation of heart rate, respiratory rate, and activity data. Operates by comparing real-time vital sign relationships against a learned, personalized baseline. Designed for ambulatory monitoring; integrates with clinical information systems. No hardware components; no alert system.
Indications for Use
Indicated for ambulatory non-pediatric patients monitored in healthcare facilities or at home to provide a Multivariate Change Index (MCI) as an adjunct to routine vital signs monitoring. Not for diagnostic use or clinical decision-making.
Regulatory Classification
Identification
A cardiac monitor (including cardiotachometer and rate alarm) is a device used to measure the heart rate from an analog signal produced by an electrocardiograph, vectorcardiograph, or blood pressure monitor. This device may sound an alarm when the heart rate falls outside preset upper and lower limits.
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Public Health Service
Food and Drug Administration 10903 New Hampshire Avenue Document Control Center - WO66-G609 Silver Spring, MD 20993-0002
June 11, 2015
VGBio, Inc. (DBA PhysIO) % Michael Billig Regulatory Consultant Experien Group 755 N Mathilda Avenue Suite 100 Sunnyvale, California 94085
Re: K142512
Trade/Device Name: Personalized Physiology Analytics Engine Regulation Number: 21 CFR 870.2300 Regulation Name: Cardiac Monitor (Including Cardiotachometer and Rate Alarm) Regulatory Class: Class II Product Code: PLB (Multivariate vital signs index) Dated: May 4, 2015 Received: May 5, 2015
Dear Michael Billig,
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. 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.
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
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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 devicerelated adverse events) (21 CFR 803); good manufacturing practice requirements as set forth in the quality systems (OS) regulation (21 CFR Part 820); and if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
If you desire specific advice for your device on our labeling regulation (21 CFR Part 801), please contact the Division of Industry and Consumer Education at its toll-free number (800) 638-2041 or (301) 796-7100 or at its Internet address
http://www.fda.gov/MedicalDevices/ResourcesforYou/Industry/default.htm. 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
http://www.fda.gov/MedicalDevices/Safety/ReportaProblem/default.htm for the CDRH's Office of Surveillance and Biometrics/Division of Postmarket Surveillance.
You may obtain other general information on your responsibilities under the Act from the Division of Industry and Consumer Education at its toll-free number (800) 638-2041 or (301) 796-7100 or at its Internet address
http://www.fda.gov/MedicalDevices/ResourcesforYou/Industry/default.htm.
Sincerely vours.
Mitchell Stein
for Bram D. Zuckerman, M.D. Director Division of Cardiovascular Devices Office of Device Evaluation Center for Devices and Radiological Health
Enclosure
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#### DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration.
### Indications for Use
510(k) Number (if known) K142512
Device Name Personalized Physiology Analytics Engine
#### Indications for Use (Describe)
The Personalized Physiology Engine (PPA Engine) is intended to be used with data from already cleared sensors measuring physiological parameters, including heart rate, respiratory rate, and activity in ambulatory patients being monitored in a healthcare facility or at home. The device provides a time series Multivariate Change Index (MCI) which indicates whether the relationships among the patient's monitored vital signs change from those measured at baseline, which has been derived from measurements previously obtained during routine activities of daily living. The MCI is based on an integrated computation evaluating changes in the parameters and their relationships to each other.
The PPA Engine is an adjunct to and is not intended to replace vital signs monitoring. The MCI is intended for daily intermittent, retrospective review by a qualified practitioner. The PPA Engine is intended to provide additional information for use during routine patient monitoring. The MCI is not intended for making clinical decisions regarding patient treatment or for diagnostic purposes.
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)
Form Approved: OMB No. 0910-0120.
Expiration Date: January 31, 2017
See PRA Statement below.
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#### FOR FDA USE ONLY
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## SECTION 5 510(k) SUMMARY
This summary of the 510(k) premarket notification for the Personalized Physiology Analytics Engine is being submitted in accordance with the requirements of SMDA 1990 and 21 CFR§807.92.
{4}------------------------------------------------
### 510(k) Notification K142512
### GENERAL INFORMATION
#### Applicant:
PhysIQ 1415 West Diehl Road Suite 150 Naperville, IL 60563 U.S.A. Phone: 630-251-5214
#### Contact Person:
Michael J. Billig Regulatory Consultant for PhysIQ Experien Group, LLC 755 N. Mathilda Ave, Suite 100 Sunnyvale, CA 94085 U.S.A. Phone: 408-400-0856 FAX: 408-400-0865 Email: mjb@experiengroup.com
Date Prepared: June 10, 2015
### DEVICE INFORMATION
#### Trade Name:
Personalized Physiology Analytics Engine
#### Generic/Common Name:
Monitor, Physiological, Patient (Without Arrhythmia Detection Or Alarms)
#### Classification:
Class II, 21 CFR\$870.2300, Cardiac monitor (including cardiotachometer and rate alarm)
### Product Code:
PLB
### PREDICATE DEVICES
- Oxford BioSignals Ltd, BioSign™ (K053112) ●
- OBS Medical, Visensia® with Alert (K081140) ●
{5}------------------------------------------------
### INDICATIONS FOR USE
The Personalized Physiology Engine (PPA Engine) is intended to be used with data from already cleared sensors measuring physiological parameters, including heart rate, respiratory rate, and activity in ambulatory patients being monitored in a healthcare facility or at home. The device provides a time series Multivariate Change Index (MCI) which indicates whether the relationships among the patient's monitored vital signs change from those measured at baseline, which has been derived from measurements previously obtained during routine activities of daily living. The MCI is based on an integrated computation evaluating changes in the parameters and their relationships to each other.
The PPA Engine is an adjunct to and is not intended to replace vital signs monitoring. The MCI is intended for daily intermittent, retrospective review by a qualified practitioner. The PPA Engine is intended to provide additional information for use during routine patient monitoring. The MCI is not intended for making clinical decisions regarding patient treatment or for diagnostic purposes.
## PRODUCT DESCRIPTION
The PhysIQ Personalized Physiology Analytics Engine ("PPA Engine") is a computerized analysis software program that is designed for detecting change in the relationships among the patient's vital signs throughout dynamic physical activity, based on data input from multi-parameter vital sign monitoring devices. The PPA Engine first "learns" a patient's personalized baseline, defined by the relationship among the vital signs derived from measurements obtained during routine activities of daily living. Once the baseline vital sign relationships are established, it analyzes the subsequent data to assess how the relationships among the vital signs incoming during the monitoring period compare to the established baseline. The PPA Engine can analyze data collected wherever the patient is monitored, reflecting a patient's activities of daily living. The device is intended for monitoring ambulatory patients.
The PPA Engine requires vital sign inputs of Heart Rate (HR), Respiration Rate (RR) and Activity (ACT) (body motion). The PPA Engine can accept input from commercial vital sign monitors or combinations of monitors that can provide multivariate observations of these vital signs.
The PPA Engine calculates the Multivariate Change Index (MCI), a scalar index between 0 and 1, which represents the likelihood that the relationships among the patient's vital signs are different from those at baseline, which was established during routine activities of daily living. An MCI value closer to zero (0) indicates that the monitored relationships among the vital signs are similar to the learned baseline. An MCI value closer to one (1) indicates that the patient's monitored relationships among the vital signs are likely to be different from the learned baseline.
The MCI is also presented as a time series (MCI over time) and it is intended to for retrospective review by the clinician The MCI is not intended to replace standard patient monitoring. Rather, it was designed to supplement standard monitoring of ambulatory patients.
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## SUBSTANTIAL EQUIVALENCE
The indications for use for the predicate devices are substantially equivalent to the proposed indications for use for the PPA Engine. Any differences in the technological characteristics between the devices do not raise any new issues of safety or effectiveness. Thus, the PPA Engine is substantially equivalent to the predicate devices. A comparison of the PPA Engine to the predicate devices is provided in Table 1.
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| Feature | PhysIQ<br>Personalized Physiology<br>Analytics Engine<br>(K142512) | Oxford Biosignals Ltd.<br>BioSignTM<br>(K053112) | OBS Medical<br>Visensia® with Alert<br>(K081140) |
|---------------------------------|-----------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------|
| General Characteristics | | | |
| Classification | Class II,<br>21CFR§870.2300 | Class II,<br>21CFR§870.2300 | Class II,<br>21CFR§870.1025 |
| Product Code | MWI | MWI | MHX |
| Patient<br>Environment | Ambulatory | Bedside or ambulatory | Bedside or ambulatory |
| Patient<br>Population | Monitored non-pediatric<br>patients | Monitored non-pediatric<br>high dependency patients | Monitored non-pediatric<br>high dependency patients |
| Technological Characteristics | | | |
| Components | Software only | Software only | Software only |
| Index<br>Produced | Non-linear combination of<br>vital parameters | Unknown combination of<br>vital parameters | Non-linear combination of<br>vital parameters |
| Index<br>Meaning | Index represents how<br>different the relationships<br>among the patient's vital<br>signs are with respect to<br>normality. | Index represents how<br>different the relationships<br>among the patient's vital<br>signs are with respect to<br>normality. | Index represents how<br>different the relationships<br>among the patient's vital<br>signs are with respect to<br>normality. |
| Index<br>Algorithm<br>Normality | Normality is defined as the<br>patient's own baseline. | Normality is defined as<br>population normality. | Normality is defined as<br>population normality. |
| Index Display | • Single numeric value of<br>latest index<br>• Trend graph<br>• Table | • Trend graph | • Single numeric value<br>of latest index<br>• Trend graph<br>• Table |
| Vital Signs<br>Data Source | Clinical Information Systems | Physiological Patient<br>Monitors / Clinical<br>Information Systems | Physiological Patient<br>Monitors / Clinical<br>Information Systems |
| Alert System | No | No | Audible and Visual |
## Table 1: Summary Substantial Equivalence Table
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### TESTING IN SUPPORT OF SUBSTANTIAL EQUIVALENCE DETERMINATION
All necessary bench and clinical testing was conducted on the PPA Engine to support a determination of substantial equivalence to the predicate devices.
## Bench Testing Summary:
The PPA Engine was tested to ensure that it performs as intended per its specifications. and to verify that technological differences between the PPA Engine and the predicate devices do not raise new issues of safety or effectiveness for providing a change index. The bench testing included:
- . Verification testing for the PPA Engine (to verify that the device meets its specifications)
- . Validation testing of the PPA Engine's MCI output (to validate correlation of MCI with changes in the relationships among vital signs compared to baseline in order to meet its intended use), including analysis of vital sign changes in human physiological data collected:
- o Perturbed clinical data study
- o Simulator data study
The collective results of the bench testing demonstrate that the software design of the PPA Engine meets the established specifications necessary for consistent performance during its intended use. In addition, the collective bench testing demonstrates that the PPA Engine index output correlates with changes in the relationships among vital signs compared with baseline, as do the indices of the predicate devices. Thus, the PPA Engine does not raise new questions of safety or effectiveness for vital sign monitoring when compared to the predicate devices.
## Clinical Testing Summary:
To validate that the PPA Engine's MCI output correlates with changes in the relationships among vital signs compared with baseline, healthy volunteer studies were conducted under an IRB-approved non-significant risk protocol, where volunteers collected vital sign data during standard activities of daily living and during a trip with a substantial altitude change (causing natural perturbation in relationships among vital signs). Study results demonstrate the MCI correlates with change in the monitored relationships among the vital signs compared to the subject's baseline; thus, the PPA Engine meets its intended use.
### CONCLUSION
The PPA Engine has a subset of the elements of intended use, patient population, as well as highly similar technological characteristics as those of the predicate devices, the BioSign and Visensia. The differences in technological characteristics have been analyzed and addressed through performance testing that demonstrates that the PPA Engine meets its intended use. Any differences between the PPA Engine and the predicate devices do not raise any new issues of safety or effectiveness. As such, the PPA Engine is substantially equivalent to the predicate devices.
{9}------------------------------------------------
# SUMMARY
The PPA Engine is substantially equivalent to the predicate devices.
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Part 1 — Search, results, and everyday workflows 16 min
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1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
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Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
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
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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.
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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.
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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
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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.
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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.