Software as a Medical Device, Therapeutic, PCCP, Pediatric
Indications for Use
Glucommander is a glycemic management tool intended to evaluate current as well as cumulative patient blood glucose values coupled with patient information including age, weight and height, and, based on the aggregate of these measurement parameters, whether one or many, recommend an IV dosage of insulin, glucose or saline or a subcutaneous basal and bolus insulin dosing recommendation to adjust and maintain the blood glucose level towards a configurable physician-determined target range. Glucommander is indicated for use in adult and pediatric (ages 2 – 17 years) patients. Glucommander logic is not a substitute for, but rather an adjunct to clinical reasoning. The measurements and calculations generated are intended to be used by qualified and trained medical personnel in evaluating patient conditions in conjunction with clinical history, symptoms, and other diagnostic measurements, as well as the medical professional’s clinical judgment. No medical decision should be based solely on the recommended guidance provided by this software program.
Device Story
Glucommander is a SaMD glycemic management tool; inputs include current/cumulative blood glucose values, age, weight, and height. Device uses deterministic, rule-based algorithms to calculate and recommend IV insulin, glucose, or saline dosages, or subcutaneous basal-bolus insulin regimens. Used in hospital settings by qualified medical personnel; output is displayed via web-based client-server interface. Clinicians review recommendations to adjust patient blood glucose toward a configurable target range. Device acts as an adjunct to clinical reasoning; does not replace professional judgment. Benefits include standardized, automated titration guidance to maintain glycemic control. System includes safety guardrails, alerts, and timers; integrates with EHRs.
Clinical Evidence
No clinical data. Substantial equivalence supported by non-clinical software verification and validation, including algorithm testing, regression testing, and cybersecurity verification per FDA guidance.
Technological Characteristics
SaMD; deterministic, rule-based dosing algorithm. Web-based client-server deployment. Integrates with EHR. Complies with IEC 62304 (software lifecycle), ISO 14971 (risk management), IEC 62366-1 (usability), and ISO 13485 (quality management).
Indications for Use
Indicated for adult and pediatric (ages 2–17) patients requiring glycemic management. Contraindicated for patients with life expectancy <48 hours, donor-after-brain-death (DBD) organ preservation, known insulin allergy, or pediatric patients <2 years old. Requires clinical oversight for patients with severe insulin resistance (>500 units/hr).
Regulatory Classification
Identification
A predictive pulmonary-function value calculator is a device used to calculate normal pulmonary-function values based on empirical equations.
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FDA U.S. FOOD & DRUG ADMINISTRATION
# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY
## I Background Information:
A 510(k) Number
K254102
B Applicant
Glytec, LLC
C Proprietary and Established Names
Glucommander
D Regulatory Information
| Product Code(s) | Classification | Regulation Section | Panel |
| --- | --- | --- | --- |
| NDC | II | 21 CFR 868.1890 - Predictive Pulmonary-Function Value Calculator | CH - Clinical Chemistry |
E Purpose for Submission:
Modification to the Glucommander cleared under K152300 to
- Update cybersecurity controls.
- Establish a Pre-Determined Change Control Plan (PCCP).
## II Intended Use/Indications for Use:
A Intended Use(s):
See Indications for Use below.
B Indication(s) for Use:
Glucommander is a glycemic management tool intended to evaluate current as well as cumulative patient blood glucose values coupled with patient information including age, weight and height, and, based on the aggregate of these measurement parameters, whether one or many, recommend an IV dosage of insulin, glucose or saline or a subcutaneous basal and bolus insulin dosing recommendation to adjust and maintain the blood glucose level towards a configurable physician-determined target range.
Food and Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993-0002
www.fda.gov
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Glucommander is indicated for use in adult and pediatric (ages 2 – 17 years) patients.
Glucommander logic is not a substitute for, but rather an adjunct to clinical reasoning. The measurements and calculations generated are intended to be used by qualified and trained medical personnel in evaluating patient conditions in conjunction with clinical history, symptoms, and other diagnostic measurements, as well as the medical professional’s clinical judgment. No medical decision should be based solely on the recommended guidance provided by this software program.
## C Special Conditions for Use Statement(s):
- Terminal patients: Glucommander is not appropriate for use with patients having a life expectancy less than 48 hours.
- Organ preservation: Glucommander is not intended for use in the preservation of donor-after-brain-death (DBD) organs.
- Severe Insulin Resistance: Patients receiving insulin at more than 500 units/hr are assumed to be extremely insulin resistant by the system. Glucommander will flash a warning message indicating possible severe insulin resistance, instructing the healthcare professional to contact the attending provider for treatment of insulin resistance before resuming the program.
- Patients with a known insulin allergy: do not use.
- Pediatric patients less than 2 years of age: do not use.
## III Device Description
Glucommander operates as a cloud-hosted clinical decision support software as a medical device. The software receives patient-specific inputs such as demographic information, glucose values, and prescribed treatment parameters. Data may be entered manually by the clinician or transferred electronically from connected hospital systems where configured.
Using these inputs, Glucommander applies a deterministic, rule-based algorithm to calculate recommended intravenous infusion rates or subcutaneous basal, bolus, and correction doses. The algorithm considers current and recent glucose values, insulin sensitivity, and prescriber defined parameters. Safety limits and clinical notifications are applied to each calculation to prevent excessive or unsafe dosing and to prompt clinical review when glucose values exceed defined thresholds.
Recommendations and alerts are displayed through a user interface for the clinician’s review. All insulin administration is performed manually by the treating clinician, who may accept, modify, or disregard the software’s recommendations based on clinical judgment. Glucommander does not autonomously deliver insulin or interface with any infusion device to control therapy.
When configured, Glucommander supports interoperability with hospital systems to streamline workflow, such as single sign-on (SSO) access from the electronic health record (EHR) or the transfer of patient context. If integrated data are unavailable or a network interruption occurs, clinicians can manually enter required data to continue operation safely.
K254102 - Page 2 of 6
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Glucommander operates within a cloud-hosted environment that is managed and maintained by Glytec under its quality-management and change-control processes. Authorized clinical users access the application through encrypted web connections, typically via browser launch or single sign-on (SSO) from the electronic health record (EHR).
In the current configuration, all clinical logic, processing, and data storage occur within the Glytec-managed cloud environment. The hospital network provides authenticated user access and, where configured, limited data exchange with the Glytec Interface Engine through encrypted connections.
## IV Substantial Equivalence Information:
A Predicate Device Name(s):
Glucommander
B Predicate 510(k) Number(s):
K152300
C Comparison with Predicate(s):
| Device & Predicate Device(s): | K254102 | K152300 |
| --- | --- | --- |
| Device Trade Name | Glucommander | Glucommander |
| General Device Characteristic Similarities | | |
| Intended Use/Indications For Use | Glucommander is a glycemic management tool intended to evaluate current as well as cumulative patient blood glucose values coupled with patient information including age, weight and height, and, based on the aggregate of these measurement parameters, whether one or many, recommend an IV dosage of insulin, glucose or saline or a subcutaneous basal and bolus insulin dosing recommendation to adjust and maintain the blood glucose level towards a configurable physician-determined target range. Glucommander is indicated for use in adult and pediatric (ages 2 – 17 years) | Same |
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| | patients. Glucomander logic is not a substitute for, but rather an adjunct to clinical reasoning. The measurements and calculations generated are intended to be used by qualified and trained medical personnel in evaluating patient conditions in conjunction with clinical history, symptoms, and other diagnostic measurements, as well as the medical professional’s clinical judgment. No medical decision should be based solely on the recommended guidance provided by this software program. | |
| --- | --- | --- |
| **General Device Characteristic Differences** | | |
| Cybersecurity controls | Updated to align with current FDA guidance | Best practice at time of clearance |
| Use Environment | Healthcare facility environment | Clinics, Hospitals |
**Predetermined Change Control Plan (PCCP):** In addition to the similarities and differences between the candidate and predicate devices listed in the table above, the candidate device has an authorized PCCP for updates to dose calculation logic and settings, for expanding alert contents, for enhancing record keeping, for modernizing user interface, and for adding another data input source. See Section VI.C for more information.
**V Standards/Guidance Documents Referenced:**
- ANSI AAMI ISO 14971: 2019 Medical devices - Applications of risk management to medical devices.
- ANSI AAMI IEC 62304:2006/A1:2016 Medical device software - Software life cycle processes [Including Amendment 1 (2016)].
- AAMI TIR57:2016 Principles for medical device security - Risk management.
- AAMI TIR97:2019 Principles for medical device security - Postmarket risk management for device manufacturers.
- ANSI AAMI SW91:2018 Classification of defects in health software.
- ISO 15223-1 Fourth edition 2021-07 Medical devices - Symbols to be used with information to be supplied by the manufacturer - Part 1: General requirements
- ISO 20417 First edition 2021-04 Corrected version 2021-12 Medical devices - Information to be supplied by the manufacturer
- ANSI AAMI IEC 62366-1:2015+AMD1:2020 (Consolidated Text) Medical devices Part 1: Application of usability engineering to medical devices, including Amendment 1
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VI Performance Characteristics:
A. Non-Clinical Performance
1. Software:
The software documentation provided was determined to be adequate to support substantial equivalence. Software Verification and Validation were conducted per FDA's 2023 device software guidance, IEC 62304, and ISO 14971, and demonstrated that the implemented software functions conform to their specified requirements for their intended clinical use.
2. Cybersecurity:
The cybersecurity documentation was provided per FDA's 2025 premarket cybersecurity guidance expectations for SPDF and TPLC documentation, and was determined to be adequate to support substantial equivalence. Cybersecurity verification for the device is complete and traceable across the threat-to-test chain for the evidence set claimed for the current release. Any open findings or partial evidence (if present) are documented with explicit remediation and retest requirements. Residual risk acceptability for associated threat scenarios remains contingent on completion of those actions. Lifecycle monitoring, vulnerability intake, and patch governance are managed under the manufacturer's QMS processes and will continue postmarket as part of TPLC commitments.
B. Clinical Studies:
Not applicable.
C. Other Supportive Device Performance Characteristics Data
3. Pre-Determined Change Control Plan (PCCP)
The PCCP documents how the device will be modified to update the dose calculation logic and settings and to expand alert contents to reflect evolving clinical practice, to modernize the user interface and to enhance record keeping for improving user experience, and to add another input source to enhance data accessibility.
The evaluation methods for validating each modification are described below:
| Modifications | Ver. | Reg. | Retro. | Expert | Usab. | Cyber. | Acc. |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Dose adjustment for nutrition | Yes | Yes | Yes | Yes | | | |
| Dose adjustment for insulin on board | Yes | Yes | Yes | | Yes | | |
| IV target range expansion | Yes | Yes | | Yes | | | |
| IV dose configurability with override controls | Yes | Yes | | | Yes | | |
| Potassium level monitoring and notification | Yes | Yes | | | Yes | | |
| User Interface Modernization | Yes | Yes | | | Yes | | Yes |
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| Accessory Mobile Interface | Yes | Yes | | | Yes | Yes | |
| --- | --- | --- | --- | --- | --- | --- | --- |
- Verification testing (Ver.) – Design verification testing (including algorithm/logic testing, configuration testing, boundary and threshold testing, and interface testing) will be conducted for each modification following prespecified procedures.
- Regression testing (Reg.) – Confirmatory testing demonstrating that the modification does not alter existing cleared device functionality when the module is inactive or in fallback.
- Retrospective Data Benchmarking (Retro.) – Retrospective application of the modification to historical de-identified patient episode data, comparing glycemic outcomes to baseline.
- Structured Expert Validation Study (Expert) – Blinded review of representative management scenarios by independent board-certified subject matter experts, evaluated against clinical standards.
- Human Factors / Usability Testing (Usab.) – Human factors and usability testing conducted per IEC 62366-1 and pre-defined usability SOPs.
- Cybersecurity testing (Cyber.) – Security testing will be conducted per the predefined SOPs.
- Accessibility Audit (Acc.) – Automated and manual audit against an internationally recognized set of digital accessibility standards.
- The pre-specified acceptance criteria include pass rates for the defined tests.
- Labeling and training updates will be developed and controlled under pre-defined SOPs.
- Final release and deployment will follow pre-defined SOPs.
## VII Proposed Labeling:
The labeling supports the finding of substantial equivalence for this device.
## VIII Conclusion:
The submitted information in this premarket notification is complete and supports a substantial equivalence decision.
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Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
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.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
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
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.