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510(k) Data Aggregation
(123 days)
Amia Automated PD System:
The Amia Automated PD System is intended for automatic control of dialysate solution exchanges in the treatment of adult renal failure patients undergoing peritoneal dialysis.
All therapies using the Amia Automated PD System must be prescribed and performed under the responsibility of a physician who is familiar and well informed about peritoneal dialysis.
Sharesource:
The Sharesource portal is intended for use by healthcare professionals to remotely communicate new or modified treatment parameters with compatible dialysis instruments and transfer completed treatment data to a central database to aid in the review, analysis, and evaluation of patients' historical treatment results. This system is not intended to be a substitute for good clinical management practices, nor does its operation create decisions or treatment pathways.
The Amia Automated PD System with Sharesource (hereafter "Amia/Sharesource System") device is intended for automatic control of dialysate solution exchanges in the treatment of adult renal failure patients undergoing peritoneal dialysis therapy. The system automatically cycles peritoneal dialysis fluid in the amounts and at the times prescribed by a clinician familiar and well informed about peritoneal dialysis. The clinician may use the optional Sharesource software accessory to remotely communicate with the Amia Automated PD System. Sharesource will allow the transfer of treatment data originating from the treatment device to the clinician for review of patient historical treatment results. It will also allow the clinician to adjust the device settings of the Amia Automated PD System remotely. Changes to device program by the physician require the patient to review and accept the changes prior to the change of the device program on the cycler. If the patient does not accept the changes, the device will not accept the modified program. The Amia Automated PD System with Sharesource does not include any real-time remote monitoring or real-time remote programming capabilities.
The provided document is a 510(k) summary for the Amia Automated PD System with Sharesource. It focuses on demonstrating substantial equivalence to predicate devices rather than proving the device meets specific acceptance criteria through a standalone study with detailed performance metrics.
Therefore, the document does NOT contain the following information:
- A table of acceptance criteria and reported device performance.
- Sample size used for a test set or its data provenance.
- Number of experts used to establish ground truth or their qualifications.
- Adjudication method for a test set.
- Information about a multi-reader multi-case (MRMC) comparative effectiveness study, including an effect size for human readers with and without AI assistance.
- A standalone performance study of the algorithm without human-in-the-loop.
- The type of ground truth used (e.g., pathology, outcomes data).
- Sample size for a training set.
- How ground truth for a training set was established.
However, the document does state the following about performance testing:
- Performance Data: "The device was tested to verify conformance with the design specifications and applicable industry standards and to verify compatibility and functionality with Sharesource. Complete system verification testing was performed to ensure that the device functions as intended and met all requirements."
- Human Factors Evaluation: "In addition, Human Factors evaluations for the Amia Automated PD System and the Sharesource software were conducted in simulated environments to ensure user needs and intended uses were met."
In summary, the document states generally that testing was performed to verify conformance to specifications and ensure the device functions as intended, and that a Human Factors evaluation was conducted. However, it does not provide specific acceptance criteria, detailed performance metrics, sample sizes, ground truth establishment, or details of a comparative effectiveness study that one might find for an AI/ML-driven diagnostic device.
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