(139 days)
The ABMS is indicated for intraoperative and postoperative recovery of blood, washing of the processed blood, and pre-operative sequestration (with indirect and direct patient connection). Typical clinical applications of autotransfusion include the following surgical specialties: Cardiovascular, Orthopedics, Thoracic, Transplant Surgery, Emergency (Trauma), Neurosurgery, Obstetrics and Gynecology, and Urology.
The ABMS consists of hardware and disposables. It is an enhancement of the parent device, the Dideco Compact-A. It integrates the automated autotransfusion protocols of the STAT-P as well as adding automated sequestration protocols. The main elements of the hardware include the centrifuge, blood pump, three automatic clamps, control sensors, and a user interface (control panel). The modifications to the disposables are an increase in the thickness of the base of the bowl and a change in labeling to the new product codes.
This document describes the Dideco Autologous Blood Management System (ABMS) and its substantial equivalence to predicate devices, supported by a clinical study.
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria are implied by the comparison of the ABMS's automated sequestration results to those previously submitted for the manual sequestration process. The goal was to demonstrate similar performance with improved consistency.
| Characteristic | Acceptance Criteria (Implied: similar to Manual Process with improved consistency) | ABMS Performance (Automated Mode) | Manual Process Performance |
|---|---|---|---|
| PRP Platelet Yield (1011) Mean | Similar to 0.75 | 0.79 | 0.75 |
| PRP Platelet Yield (1011) SD | Lower than 0.32 (improved consistency) | 0.29 | 0.32 |
| PRP Platelet Collection Efficiency (%) Mean | Similar to 69.4 | 69.2 | 69.4 |
| PRP Platelet Collection Efficiency (%) SD | Lower than 20.3 (improved consistency) | 17.6 | 20.3 |
2. Sample Size Used for the Test Set and the Data Provenance
The document does not explicitly state the sample size used for the clinical study comparing automated and manual sequestration, nor does it specify the country of origin or whether the data was retrospective or prospective. It only mentions "a clinical study was conducted."
3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts
This information is not provided in the document.
4. Adjudication Method for the Test Set
This information is not provided in the document.
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
This is not a multi-reader, multi-case comparative effectiveness study. It's a device performance study comparing automated processing with manual processing for autotransfusion. There is no mention of human readers or AI assistance in this context.
6. If a Standalone (i.e. algorithm only without human-in-the loop performance) was Done
The study described is a standalone performance study of the ABMS in its automated sequestration mode, comparing its output to a manual process. The "algorithm only" aspect applies to the automated functions of the ABMS itself, as it performs the sequestration process autonomously after initiation.
7. The Type of Ground Truth Used
The ground truth implicitly used is the measured performance characteristics of the Platelet Rich Plasma (PRP) from both the automated ABMS process and the manual sequestration process. These characteristics include PRP Platelet Yield and PRP Platelet Collection Efficiency. The manual process results served as the reference for comparison.
8. The Sample Size for the Training Set
This information is not provided in the document. The ABMS is an enhancement of a parent device and integrates automated protocols; it's not a machine learning or AI algorithm in the contemporary sense that would typically involve a separate "training set."
9. How the Ground Truth for the Training Set was Established
This information is not provided. As noted above, the concept of a "training set" in the context of modern machine learning algorithms doesn't directly apply here. The device's protocols were likely developed through engineering design, experimentation, and optimization, rather than by training a model on a labeled dataset. The reference to "automated autotransfusion protocols of the STAT-P" suggests that existing successful protocols were integrated and enhanced.
§ 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).