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510(k) Data Aggregation
(452 days)
The BioButton System is a remote monitoring wearable device intended for continuous collection of physiological data in home and healthcare settings while the patient is at rest. This could include heart rate, respiratory rate, skin temperature, and other data such as activity level, body position, sleep, and step and gait analysis.
Data are securely transmitted via wireless connection from the device for storage, review, and further analysis.
The data from the device are intended as an aid to diagnosis, diseases management, and treatment.
The device is intended for use on users who are 18 years of age or older.
The device is not intended to output physiological measurements while the user undergoes significant motion or is active.
The device is not intended for critical care.
The BioButton System is a wireless remote monitoring system intended for use by healthcare professionals for continuous collection of physiological data in home and healthcare settings while the patient is at rest. This can include heart rate, respiratory rate, and skin temperature. Data are transmitted from the BioButton for storage and analysis.
The BioButton System (the System), is an extension of the BioSticker System with the same/similar intended use. The System is intended to monitor physiological parameters of the patient/users for relatively long duration (up to 60 days with medical data collection). The System should now enable the connectivity with the BioButton sensor device (BBN) with the rest of the system components.
The BioButton System consists of a single-use wearable hardware sensor module to collect data from a patient and other Medical Device Data System (MDDS) components that enables remote transfer of collected data. See main components of the BioButton System. The main components of the BioButton System could include the following:
- BioButton Sensor -
- -BioButton adhesives (accessory)
- Data Exchange Hubs (MDDS) -
- -Cloud-based data platform (MDDS)
The BioButton System is used to collect physiological information from a patient using the BioButton Sensor for a set duration (as defined by different use cases) in home and healthcare settings. Physiological data is collected continuously while the patient is at rest¹. The medical physiological data collected includes:
- . Heart rate at rest,
- . Respiratory rate at rest, and
- Skin temperature
¹ "At Rest" means the device will not output measurement data to the user undergoes significant motion or is active.
There are other wellness parameters that can be collected by the device that include: activity level, body position, sleep, and step and gait analysis. These wellness data types are not discussed in detail the requlatory submission since they do not meet the definition of medical device.
Upon completion of a physiological data collection period, the data offload is conducted via wireless Bluetooth connection using the Offload Software. The data offloading is performed and qualified personnel. Also using the Offload Software, a report will be generated to be viewed by a healthcare professional. The report is not intended to be viewed by the patient.
The provided text describes the BioButton System, a remote monitoring wearable device, and its acceptance criteria as part of a 510(k) submission to the FDA. The information focuses on demonstrating substantial equivalence to a predicate device (BioSticker System K191614).
Here's a breakdown of the requested information based on the provided document:
1. Acceptance Criteria and Reported Device Performance
The acceptance criteria for performance are clearly stated in the "Performance / Effectiveness" section (page 8).
| Comparison Items | Acceptance Criteria (BioButton System) | Reported Device Performance (BioButton System) |
|---|---|---|
| Heart Rate Range | 40-125 Beats per Minute | 40-125 Beats per Minute (stated as "Same" as predicate, implying it meets this range) |
| Heart Rate Accuracy | Mean absolute error of less than 5 Beats per minute (<±5 Beats per minute) | Mean absolute error of less than 5 Beats per minute (<±5 Beats per minute) (stated as "Same" as predicate, implying it meets this criterion) |
| Respiratory Rate Range | 10-30 Breaths per Minute | 10-30 Breaths per Minute (stated as "Same" as predicate, implying it meets this range) |
| Respiratory Rate Accuracy | Mean absolute error of less than 3 Breaths per Minute (<±3 Breaths per Minute) at rest | Mean absolute error of less than 3 Breaths per Minute (<±3 Breaths per Minute) at rest (stated as "Same" as predicate, implying it meets this criterion) |
| Skin Temperature | Meets ASTM E1112-00 standard; Range 86°F - 107°F (30°C - 42°C) | Meets ASTM E1112-00 standard; Range 86°F - 107°F (30°C - 42°C) (stated as "Same" as predicate, implying it meets this criterion) |
Beyond these explicit performance measures, the document also mentions compliance with various safety, EMC, biocompatibility, and software standards as "acceptance criteria" through compliance.
2. Sample Size for the Test Set and Data Provenance
The document states: "Clinical studies was performed on general population to confirm the measurement performance for heart rate and respiratory rate." (page 9)
- Sample Size: The exact sample size for the clinical study (test set) is not specified in the provided text.
- Data Provenance:
- Country of Origin: Not specified.
- Retrospective or Prospective: Not specified. "Clinical studies was performed" generally implies prospective data collection, but this is not explicitly stated.
3. Number of Experts and Qualifications for Ground Truth
This information is not provided in the given text. The document refers to "clinical studies" for performance confirmation but does not detail how the ground truth in these studies was established (e.g., number or qualifications of experts).
4. Adjudication Method for the Test Set
This information is not provided in the given text.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
The document does not mention nor describe a multi-reader multi-case (MRMC) comparative effectiveness study, nor does it discuss human readers improving with or without AI assistance. The context is a device measuring physiological parameters, not an AI-assisted diagnostic imaging tool.
6. Standalone Performance (Algorithm Only without Human-in-the-Loop)
The study described for performance confirmation ("Clinical studies was performed on general population to confirm the measurement performance for heart rate and respiratory rate") seems to be evaluating the device's ability to measure these parameters directly. Since the device collects data and aids diagnosis, it inherently functions as a "standalone" measurement device in terms of its primary data collection function. The "report will be generated to be viewed by a healthcare professional" (page 5), implying a human-in-the-loop for interpretation, but the core measurement accuracy validation is for the device's output. Therefore, the "performance testing" described (clinical studies) can be considered a form of standalone performance evaluation for its measurement capabilities.
7. Type of Ground Truth Used
The document refers to "clinical studies" to "confirm the measurement performance for heart rate and respiratory rate." While not explicitly stated, in such device validation studies, the ground truth for physiological parameters like heart rate and respiratory rate would typically involve simultaneous measurement with a highly accurate, often gold-standard, reference device. This would be considered outcomes data / reference standard comparison rather than expert consensus or pathology in the context of physiological measurement devices.
For skin temperature, it states "Meets ASTM E1112-00 standard," which is a standard for electronic thermometry, implying comparison to a reference standard or validated methodology.
8. Sample Size for the Training Set
The document does not mention or differentiate between a training set and a test set, nor does it provide a sample size specifically for a training set. This suggests that the submission focuses on the performance of the final device based on clinical validation, rather than detailing the machine learning model development process (if applicable beyond basic signal processing).
9. How the Ground Truth for the Training Set Was Established
Since a training set is not explicitly mentioned or detailed, the method for establishing its ground truth is not provided. The submission details performance testing and verification, implying the device's algorithms or measurement methods were developed prior to these validation studies.
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