(520 days)
The Dideco Compact-A and Compact-M Autotransfusion Systems are intended to process, shed or collect blood for autologuous transfusion. The Compact-A and Compact-M autotransfusion systems are to be used with disposables for the collection of shed blood and aspirated body fluids, and the separation of erythrocytes from other components of the aspirated blood prior to, during and/or after a surgical procedure. The systems are also recommended to collect platelet-rich plasma (PRP) and/or platelet-poor plasma (PPP) from the patient's whole blood immediately preoperative to a surgical procedure.
The Dideco Compact-A and Compact-M Autotransfusion Systems are composed of the a high-speed lightweight automatic autotransfusion system following equipment: including a rolling cart for the system with an IV pole and a portable vacuum pump module.
Here's an analysis of the provided text regarding the Dideco Compact-A and Compact-M Autotransfusion Systems:
This 510(k) submission focuses on demonstrating substantial equivalence to a predicate device (Electromedics AT-1000), rather than establishing new performance criteria through extensive clinical trials. Therefore, much of the requested information regarding detailed acceptance criteria, specific study designs, expert involvement, and ground truth for an AI-like device is not present in this document because it is not typically required or relevant for a substantial equivalence claim for this type of medical device.
Key takeaway: This is a traditional medical device submission, not an AI/ML device submission. The study described is for substantial equivalence to a predicate device, not for establishing new performance metrics against a defined acceptance criterion for a novel algorithm.
1. Table of Acceptance Criteria and Reported Device Performance
Given the nature of the submission (substantial equivalence to a predicate device), explicit, quantifiable "acceptance criteria" and direct "reported device performance" against those criteria are not presented in a table format as one might find for a novel device or AI/ML algorithm. Instead, the submission states that substantial equivalence was based on a comparison of test results from in-vivo functional tests between the Dideco systems and the predicate Electromedics AT-1000.
| Acceptance Criteria (Implied by Substantial Equivalence to Predicate) | Reported Device Performance (as stated in the submission) |
|---|---|
| Comparable plasma free Hgb levels | Comparable to Electromedics AT-1000 |
| Comparable ADP/Collagen levels | Comparable to Electromedics AT-1000 |
| Comparable pH testing results | Comparable to Electromedics AT-1000 |
| Comparable cell counting | Comparable to Electromedics AT-1000 |
| Comparable platelet counting | Comparable to Electromedics AT-1000 |
| Comparable process steps, method of operation, materials, and suggested flow rates | Identical to Electromedics AT-1000 |
The overall "acceptance criteria" is that the Dideco systems are functionally equivalent to the Electromedics AT-1000, as demonstrated by the comparable in-vivo test results and similar technological characteristics.
2. Sample Size Used for the Test Set and Data Provenance
The document states "Summary of In-Vivo Tests" but does not specify the sample size used for these tests. It also does not explicitly state the country of origin of the data or whether it was retrospective or prospective. Given that these are in-vivo functional tests for a physical medical device, it is highly likely that they are prospective tests performed on biological samples (likely human or animal blood).
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
This information is not provided in the document. For a substantial equivalence claim of this type of medical device, the ground truth is typically established by laboratory measurements or clinical observations, not necessarily by expert consensus in the way it applies to image interpretation or AI diagnostics. The "ground truth" would be the actual measured values of Hgb, ADP/Collagen, pH, cell counts, and platelet counts, derived from standard analytical methods.
4. Adjudication Method for the Test Set
The document does not describe any adjudication method. This concept (e.g., 2+1, 3+1) is typically relevant for studies where subjective expert review is required to establish ground truth for a diagnostic output, particularly in AI/ML performance evaluations. For functional comparisons of physical devices like autotransfusion systems, adjudication in this sense is usually not applicable.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size
No, an MRMC comparative effectiveness study was not done. This type of study framework is specific to evaluating diagnostic technologies, especially in fields like radiology where multiple human readers interpret cases and their performance is compared with and without AI assistance. The Dideco Compact-A and Compact-M Autotransfusion Systems are physical devices for processing blood, not diagnostic AI algorithms. Therefore, the concept of human readers improving with AI assistance does not apply.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
This question is not applicable as the Dideco systems are physical autotransfusion devices, not an AI algorithm. The performance described relates to the machine's ability to process blood and separate components, which is inherently a "standalone" machine process when considering its direct function. However, it's not an "algorithm only" performance claim in the context of AI.
7. The Type of Ground Truth Used
The ground truth used for the in-vivo functional tests (plasma free Hgb, ADP/Collagen, pH, cell and platelet counting) would be based on laboratory analytical measurements against established standards for those biological parameters. These are objective, quantifiable measurements.
8. The Sample Size for the Training Set
The document does not mention a training set. This concept (training set, validation set, test set) is fundamental to machine learning and AI development. Since this submission is for a traditional medical device demonstrating substantial equivalence, there is no AI algorithm being "trained."
9. How the Ground Truth for the Training Set Was Established
This question is not applicable as there is no training set mentioned or implied in the submission.
§ 868.5830 Autotransfusion apparatus.
(a)
Identification. An autotransfusion apparatus is a device used to collect and reinfuse the blood lost by a patient due to surgery or trauma.(b)
Classification. Class II (performance standards).