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The autoLog™ is intended for use in the collection, concentration, washing, and reinfusion of autologous blood. Such areas of application may include, but are not limited to, the following: General, Cardiovascular, . Orthopedic, Vascular, Plastic/Reconstructive, Obstetric/Gynecologic and Neurosurgical Postoperative treatment areas

The autoLog™ is an autotransfusion apparatys (including disposable kit). The system is a centrifugal unit that is used to collect autologous blood peri-operatively and ppst-operatively into a collection reservoir with an appropriate amount of anticoagulant. This autologous blood is then processed by centrifugation separating the red cells from the plasma. Contaminating debris is subsequently washed out by the introduction of normal saline in a wash cycle. The resulting packed red cells, suspended in normal saline are pumped to a transfer bag that may be reinfused to the patient.

The Medtronic autoLog™ Autotransfusion System is a Class II device (21 CFR § 868.5830) intended for the collection, concentration, washing, and reinfusion of autologous blood during peri-operative and post-operative procedures.

The provided document does not contain a comprehensive list of acceptance criteria with numerical targets. Instead, the submission focuses on demonstrating substantial equivalence to predicate devices (Sequestra and ELMD 500 autotransfusion apparatus) based on comparative features and functional testing.

However, based on the information provided, we can infer the primary acceptance criteria:

1. A table of acceptance criteria and the reported device performance

Note on Quantitative Differences:
While many features are equivalent, some quantitative differences exist with predicate devices like Centrifuge Speed (autoLog™: 10000 RPM vs. Predicates: 1000-5600 RPM), Number of Programs (autoLog™: 1 vs. Predicates: 5 or 6), Data Input (autoLog™: Push Button vs. Predicates: Touch Screen), and Blood Pump Flow (autoLog™: 0-1000 ml/min vs. Predicates: 50-500 ml/min or 10-1000 ml/min). However, these differences were deemed not to raise new questions of safety or effectiveness by the FDA, as evidenced by the substantial equivalence determination.

2. Sample size used for the test set and the data provenance

The document does not specify a "test set" in terms of patient data or samples. The testing described is focused on the device's functional performance:

• Sample Size: Not applicable in the context of clinical or patient data.
• Data Provenance: Not applicable. The testing is internal to Medtronic Blood Management, likely conducted in a laboratory or simulated environment, rather than involving external data sources.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. The ground truth for the functional and software performance tests was based on the device's design specifications and expected operational behavior. There is no indication of expert panel review for establishing ground truth in the context of clinical outcomes.

4. Adjudication method for the test set

Not applicable. The testing described (risk analysis, functional qualification, black box testing) suggests an engineering and software validation approach, not a clinical trial needing adjudication of results.

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

Not applicable. The autoLog™ is an autotransfusion apparatus, not an AI-assisted diagnostic or imaging system. Therefore, MRMC studies and "human reader improvement with AI" are irrelevant in this context.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

The device is an automated medical apparatus, meaning it operates autonomously based on its programming for the processing of blood. So, in a sense, its primary function is "standalone" once initiated. The testing described (system/software risk analysis, functional system qualification, "system black box testing") evaluates this standalone performance. However, this is not in the context of an "algorithm" operating without human input in the way a diagnostic AI would be. Human operators manage the device and reinfuse the processed blood to the patient.

7. The type of ground truth used

The "ground truth" for the device's performance was established via design specifications and functional verification. This would involve:

• Engineering specifications: Defining expected centrifuge speeds, pump flow rates, sensor functionality, etc.
• Software requirements: Defining how the microprocessor should control each stage of the process (collection, concentration, washing, pumping).
• Risk analysis: Ensuring the system operates safely and effectively within predefined parameters.

8. The sample size for the training set

Not applicable. This device is not an AI/ML product developed using training data. Its functionality is based on direct engineering design and programming.

9. How the ground truth for the training set was established

Not applicable, as there is no training set for this device.

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