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510(k) Data Aggregation
(90 days)
Fresenius Liberty Select Cycler
The Fresenius Liberty Select Cycler is indicated for acute and chronic peritoneal dialysis.
The Liberty Select Cycler is a software-controlled electro-mechanical medical device designed to deliver Automated Peritoneal Dialysis (APD) therapy for the treatment of end-stage renal disease (ESRD). The Cycler is designed as a table-top unit that can be used to perform peritoneal dialysis (PD) in hospitals, dialysis clinics, and at home. The software has been updated from version 2.9.2 to introduce the Remote Therapy Programming feature. This feature allows the Cycler to securely receive prescription settings remotely from a Fresenius server.
The provided text describes a 510(k) premarket notification for the Fresenius Liberty Select Cycler, focusing on a software modification (Remote Therapy Programming feature). The document emphasizes that there are no changes to the fundamental device characteristics or performance specifications compared to the predicate device.
Here's an analysis of the acceptance criteria and study information based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document explicitly states that the Liberty Select Cycler has the same essential performance characteristics as the predicate device (K181108). The acceptance criteria are presented as "Specifications" for key features:
| Feature | Specification | Reported Device Performance (Implied) |
|---|---|---|
| Inflow | 45-316 mL/min | Meets specification |
| Outflow | Minimum: 30 mL/min | |
| Maximum: 286 mL/min | Meets specification | |
| Temperature | 37°C ± 1°C | Meets specification |
| Volume Accuracy, Fill | ± 2% of the fill volume | Meets specification |
| Volume Accuracy, Drain | ± 3% of the drain volume | Meets specification |
Note: The document doesn't provide specific "reported device performance" values for these characteristics for the modified device. Instead, it states that the modified device has the "same essential performance characteristics" as the predicate and that "test results demonstrate that the differences between the proposed and the predicate devices do not raise any new concerns with regard to safety or effectiveness." This implies that the modified device performs within these established specifications.
2. Sample Size Used for the Test Set and Data Provenance
The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). It mentions "Unit, software, regression (system verification), and validation testing" and "Human Factors Validation Testing" but does not detail the specifics of these test sets.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications
The document does not provide information on the number of experts used to establish ground truth or their qualifications. The testing mentioned (software verification, human factors) would typically involve engineering and possibly human factors specialists, but no details are given.
4. Adjudication Method for the Test Set
The document does not describe any specific adjudication method (e.g., 2+1, 3+1, none) for the test set.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size
No MRMC comparative effectiveness study is mentioned in the document. The submission focuses on substantial equivalence based on a software modification, not a comparison of human reader performance with and without AI assistance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
The device is a medical cycler, not an AI algorithm for diagnostic purposes. The "Remote Therapy Programming feature" is a software update that allows the device to receive prescription settings remotely. This is a functional update to the device's operational capabilities, not a standalone diagnostic algorithm. Therefore, "standalone" algorithm performance in the typical sense (e.g., for image analysis) is not applicable or discussed. The testing done covers software verification and validation, as well as human factors for the new feature.
7. Type of Ground Truth Used
For the specific performance characteristics listed (inflow, outflow, temperature, volume accuracy), the ground truth would be established by physical measurements and engineering standards against the device's design specifications. The document implies these were the basis for demonstrating equivalence to the predicate device.
For the software verification and validation testing, the "ground truth" would be the expected functional behavior and security requirements of the software, as defined in design documents and specifications, and adherence to relevant standards (e.g., ANSI/AAMI/IEC 62304 for medical device software lifecycle).
For the human factors validation testing, the "ground truth" would be safe and effective use of the device, particularly with the new remote programming feature, as measured against usability metrics and error rates.
8. Sample Size for the Training Set
The document does not mention a "training set" in the context of machine learning. The software modification described involves a new feature for remote programming, not a machine learning algorithm that requires training data.
9. How the Ground Truth for the Training Set Was Established
As no training set for machine learning is mentioned, this information is not applicable.
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(27 days)
Fresenius Liberty Select Cycler
The Fresenius Liberty Select Cycler is indicated for acute and chronic peritoneal dialysis.
The Liberty Select Cycler is an electro-mechanical medical device. Software controls the functions of the machine during peritoneal dialysis treatment, including fluid flow, heating, and alarms. Like the predicate device, the modified Liberty Select Cycler is a software-controlled electromechanical medical device designed as a table-top unit to deliver Automated Peritoneal Dialysis (APD) therapy for the treatment of end-stage renal disease (ESRD). The Liberty Select Cycler is used to perform continuous and intermittent peritoneal dialysis therapies. Treatment settings are programmed based on a physician's prescription.
Acceptance Criteria and Device Performance for Fresenius Liberty Select Cycler (K181108)
This submission (K181108) is for a modification to the Fresenius Liberty Select Cycler, specifically concerning software updates. The key point is that the essential performance characteristics, indications for use, materials, and other technological aspects remain the same as the predicate device (K171652). Therefore, the acceptance criteria and performance data presented here are based on the essential performance characteristics outlined for the device.
1. Table of Acceptance Criteria and Reported Device Performance
| Feature | Acceptance Criteria | Reported Device Performance (from K171652, essentially unchanged) |
|---|---|---|
| Inflow | 45–316 mL/min | 45–316 mL/min |
| Outflow | Minimum: 30 mL/min, Maximum: 286 mL/min | Minimum: 30 mL/min, Maximum: 286 mL/min |
| Temperature | 37°C ± 1°C | 37°C ± 1°C |
| Volume Accuracy, Fill | ± 2% of the fill volume | ± 2% of the fill volume |
| Volume Accuracy, Drain | ± 3% of the drain volume | ± 3% of the drain volume |
2. Sample Size Used for the Test Set and Data Provenance
The provided document does not specify a separate "test set" in the context of a clinical study with human subjects for this particular submission (K181108). This submission primarily focuses on software modifications to an already cleared device. The testing described is software verification and validation testing, which would typically involve testing on the device itself (hardware-in-the-loop) and simulated environments, rather than a human patient test set.
- Sample Size: Not applicable in the context of a human patient test set for this submission. The "sample size" would relate to the number of test cases or scenarios executed during software testing.
- Data Provenance: Not applicable in the context of clinical data provenance for this submission. The "data" comes from internal software testing and verification activities.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
Not applicable. This submission focuses on software changes and mechanical/electrical device performance, not on diagnostic accuracy based on expert interpretation. The "ground truth" for software testing would be the expected behavior or outcome defined by design specifications and requirements.
4. Adjudication Method for the Test Set
Not applicable. Adjudication methods are typically used in studies involving human interpretation or clinical outcomes where discrepancies among readers or evaluators need to be resolved. This submission describes software and mechanical performance testing.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No. An MRMC comparative effectiveness study was not done. This type of study is relevant for assessing the impact of AI on human reader performance in diagnostic tasks, which is not the purpose of this device or its modifications.
6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study
Not explicitly a "standalone study" in the common sense of an AI algorithm making a diagnosis. However, the software verification and validation testing described can be considered a form of standalone performance evaluation for the software's intended functions within the device. The software controls functions like fluid flow, heating, and alarms independently, and its modifications were tested to ensure these functions operate correctly.
7. Type of Ground Truth Used
The ground truth for the performance characteristics listed in the table (Inflow, Outflow, Temperature, Volume Accuracy) would be established by engineering specifications, calibration standards, and validated measurement techniques. For the software verification, the ground truth would be the defined software requirements and expected outputs based on those requirements.
8. Sample Size for the Training Set
Not applicable. This device is an electro-mechanical medical device with software controls, not a machine learning or AI algorithm that requires a "training set" in the traditional sense. The software is programmed based on defined logic and parameters, rather than trained on data.
9. How the Ground Truth for the Training Set Was Established
Not applicable. As there is no "training set" in the context of a machine learning algorithm, there is no ground truth established for it. The software's correct functioning is verified against its predefined specifications and requirements.
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(102 days)
Fresenius Liberty Select Cycler
The Fresenius Liberty Select Cycler is indicated for acute and chronic peritoneal dialysis.
The Liberty Select Cycler is an electro-mechanical medical device. Software controls the functions of the machine during peritoneal dialysis treatment, including fluid flow, heating, and alarms. The Liberty Select Cycler is designed as a table-top unit and is prescribed for use in both professional and home treatment settings. The Liberty Select Cycler is a software-controlled electro-mechanical medical device designed to deliver Automated Peritoneal Dialysis (APD) therapy for the treatment of end-stage renal disease (ESRD). The Liberty Select Cycler is used to perform continuous and intermittent peritoneal dialysis therapies. Treatment settings are programmed based on a physician's prescription. The Liberty Select Cycler accommodates three (3) accessory devices: Cassette and tubing set, IQdrive, Optional wireless modem.
This document describes the Fresenius Liberty Select Cycler, a peritoneal dialysis system. Since the device is a peritoneal dialysis cycler and not an AI/ML powered device, the provided information does not contain details about acceptance criteria, test sets, ground truth, or training sets in the context of AI/ML performance evaluation. These types of details are typically pertinent to the validation of AI/ML algorithms, which is not the subject of this 510(k) summary.
However, the document does list essential performance characteristics and outlines the performance data used to demonstrate substantial equivalence to predicate devices. Here's a breakdown of the relevant information provided:
1. A table of acceptance criteria and the reported device performance:
The document states "The Liberty Select Cycler has the same essential performance characteristics as the predicate device (K123630) as listed in Table 1." It then provides the following table:
| Feature | Specification |
|---|---|
| Inflow | 45-316 mL/min |
| Outflow | Minimum: 30 mL/min |
| Maximum: 286 mL/min | |
| Temperature | 37°C ± 1°C |
| Volume Accuracy, Fill | ± 2% of the fill volume |
| Volume Accuracy, Drain | ± 3% of the drain volume |
Note: These specifications serve as the "acceptance criteria" for the device's essential performance characteristics. The document implies that the reported device performance (i.e., the performance of the Liberty Select Cycler) meets these specifications, as it concludes the device is substantially equivalent to the predicate. The document does not provide separate "reported device performance" values if they are identical to the specifications.
2. Sample size used for the test set and the data provenance:
- This information is not explicitly provided in the context of AI/ML algorithm evaluation, as the device is not an AI/ML product.
- For the described performance parameters (inflow, outflow, temperature, volume accuracy), testing would typically involve a certain number of device units and repeated measurements. However, the specific sample size (e.g., number of cycles tested, number of devices) is not detailed in this summary.
- The data provenance (e.g., country of origin, retrospective/prospective) is also not applicable or specified for this type of medical device validation.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- This information is not applicable, as this device is not an AI/ML system requiring expert-established ground truth for algorithm performance.
- The "ground truth" for the performance characteristics would be established by validated measurement techniques and sensors inherent to the engineering and medical device testing processes.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- This information is not applicable for the type of device and validation described. Adjudication methods are typically used in clinical studies or AI/ML evaluations where there is subjective interpretation involved.
5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:
- No, an MRMC comparative effectiveness study was not done. This type of study is relevant for diagnostic or AI-assisted interpretation devices, which the Fresenius Liberty Select Cycler is not.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- This is not applicable as the device is not an AI/ML algorithm. The "standalone" performance here refers to the device's functional performance in an automated manner, which is what the essential performance characteristics refer to.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- For the device's essential performance characteristics (Inflow, Outflow, Temperature, Volume Accuracy), the "ground truth" is based on engineering measurements and calibration standards. For example, volume accuracy would be measured against known, accurately measured volumes; temperature against calibrated thermometers; and flow rates against calibrated flow meters.
8. The sample size for the training set:
- This information is not applicable, as the device is not an AI/ML system that undergoes a "training set" phase.
9. How the ground truth for the training set was established:
- This information is not applicable, as the device is not an AI/ML system with a "training set."
Summary of Study/Testing Methods (as provided in the document):
The performance testing requirements were determined through:
- Application of a risk management process.
- Applicable FDA guidance documents.
- Performance standards (21 CFR 876.5630).
Testing included:
- Software Verification and Validation Testing: Unit, integration, and system-level software verification to demonstrate effectiveness of software modifications and confirm machine operation.
- Mechanical and Acoustic Testing: Verified that noise level does not exceed 40 dBA during treatment.
- Human Factors Testing: Performed on modifications impacting Human Factors, including:
- Selectable drain exit criteria (70% or 85%).
- Selectable additional drain option with alert.
- STAT drain at the end of treatment.
- Implement Soft Alarms.
- The remaining essential and critical user tasks were determined to be equivalent to the predicate devices.
The conclusion states that the four new features tested are safe and effective for the intended users, uses, and use environments, and that the device is substantially equivalent to its predicate.
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