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The Quantum Micro-Cardioplegia Delivery System is intended for delivery of micro-cardioplegia to the heart during open heart surgery lasting up to six hours in duration. It is intended to be used by an experienced and trained clinician. It is not intended to be used by a patient or other untrained personnel.

The Quantum Micro-Cardioplegia Delivery System is intended to assist in arresting a beating heart during cardiac surgery through controlled delivery of cardioplegia. The system consists of the Quantum Workstation, Quantum Micro-Cardioplegia Delivery Module, Quantum Roller Pump 4", Quantum Diagnostic/Ventilation Module, and Quantum Pureflow Cardioplegia Heat Exchanger. The Quantum Micro-Cardioplegia Delivery Module was developed to supplement the existing Quantum technology and to allow users a more integrated way of managing microplegia delivery during cardiopulmonary bypass.

The Quantum Micro-Cardioplegia Delivery System is intended specifically for procedures where microplegia (i.e., whole blood cardioplegia) strategy is used. The Quantum Micro-Cardioplegia Delivery Module adds cardioplegia drugs (i.e., arrest agent and additive) to the main carrier solution (blood) which is circulated by a Quantum Roller Pump. The two devices, working in unison and controlled by the Quantum Workstation, deliver cardioplegia to the patient’s heart.

The premise of the Quantum Micro-Cardioplegia Delivery System is to interlock forward cardioplegia flow with medication delivery rates, ensuring delivery of arrest agent at a given ratio to the blood. The system includes a separate, dedicated heat exchanger, which is intended to be fitted in the cardioplegia blood line to allow for temperature regulation of the cardioplegia solution. The heat exchanger can be connected to any available heater cooler device which will manage the temperature regulation.

This document is an FDA 510(k) clearance letter for a medical device called the "Quantum Micro-Cardioplegia Delivery System." The letter grants clearance based on the device being "substantially equivalent" to legally marketed predicate devices.

Key takeaway regarding your request:

The provided document does not contain the specific acceptance criteria and study results in the detail you are requesting. It only states that a "suite of nonclinical tests was performed" and lists the types of tests conducted, concluding that these tests demonstrate substantial equivalence.

Therefore, I cannot provide a table of acceptance criteria and reported device performance, nor details like sample sizes for test/training sets, data provenance, number/qualifications of experts, adjudication methods, MRMC studies, or standalone algorithm performance.

The device described is a physical medical device (a system for delivering micro-cardioplegia during open-heart surgery), not an AI/software-driven diagnostic tool that would typically involve the type of performance metrics (sensitivity, specificity, AUC, human reader improvement, etc.) you are asking about.

I can, however, extract what is available about the non-clinical tests and the device's characteristics:

Acceptance Criteria and Reported Device Performance

The document does not specify quantitative acceptance criteria or detailed reported performance metrics. It only states that testing was performed to demonstrate substantial equivalence to the predicate device. The general performance aspects implicitly evaluated would be related to the device's functional integrity, safety (electrical, electromagnetic), and usability.

Study Details (Based on the provided document)

Since the device is a physical medical device, not an AI model, many of the requested fields are not applicable or not detailed in a typical FDA clearance letter for such a product.

1. Sample size used for the test set and the data provenance: Not mentioned. The testing involves bench and non-clinical evaluations of the device's hardware, software, and performance, not a clinical data set in the context of an AI study.
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable, as this refers to a physical device performance test, not a diagnostic interpretation. Clinical experts (experienced and trained clinicians) are the intended users, and Human Factors/Usability tests would involve them, but specifics are not provided.
3. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable for this type of device and testing.
4. 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. This is a physical medical device, not an AI diagnostic tool involving human readers.
5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable. This is a physical medical device. The "algorithm" refers to the device's control software, which performs its functions whether a human is "in-the-loop" as an operator or not during functional testing.
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable in the context of a diagnostic AI. For a physical device, the "ground truth" for functional testing would be the engineering specifications, regulatory standards (e.g., IEC 60601-1), and expected physical and chemical performance parameters (e.g., flow rate accuracy, temperature control).
7. The sample size for the training set: Not applicable. This is not an AI model requiring a training set in the machine learning sense.
8. How the ground truth for the training set was established: Not applicable.

In summary, the provided FDA clearance letter focuses on establishing substantial equivalence for a physical medical device (Quantum Micro-Cardioplegia Delivery System) through non-clinical bench testing, not on the performance of a diagnostic AI tool using clinical data. Therefore, most of the specific metrics and study design details you requested, which are pertinent to AI/software performance, are not present in this document.

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The Spectrum Medical Quantum Heater-Cooler is intended to provide temperature-controlled fluid to compatible Quantum PureFlow heat exchanger devices (cardiopulmonary bypass heat exchangers and cardioplegia heat exchangers) to warm or cool a patient during cardiopulmonary bypass procedures lasting six (6) hours or less.

The Quantum Heater-Cooler is only intended to be used by trained Clinicians in a clinical environment.

The Quantum Heater-Cooler consists of the following components:

• . Quantum Heater-Cooler Unit (including connection hoses and heat transfer fluid)
• . Quantum Pure Flow Heat Exchangers (standard and cardioplegia, manufactured by Qura S.r.l.)
• External cold storage charger .

The Quantum Heater-Cooler is intended for cooling and warming patients during cardiothoracic surgeries requiring cardiopulmonary bypass with extracorporeal circulation (procedures lasting six (6) hours or less). The system regulates the temperature of circulating blood and cardioplegia solution using a proprietary glycol-based heat transfer fluid. Thermal energy carried by the heat transfer fluid is transferred from the heater-cooler unit to the patient perfusion circuit via Quantum PureFlow heat exchangers specifically developed for both the arterial and cardioplegia circuits. The heater-cooler, in conjunction with the heat exchangers, forms a closed circuit and no direct contact with the patient's blood or body fluids takes place at any time.

Heating of the heat transfer fluid is accomplished through electrical heaters. For fluid cooling, the Quantum Heater-Cooler utilizes a Phase Change Material as a cold energy storage. This Phase Change Material needs to be routinely recharged between surgeries using an external cold storage charger outside of the operating room environment.

The provided text is a 510(k) summary for the Spectrum Medical Quantum Heater-Cooler. This document details the device's characteristics, intended use, and comparison to a predicate device to demonstrate substantial equivalence, a process which does not involve clinical studies or a study design with acceptance criteria, sample sizes, or expert adjudication as typically seen in efficacy or diagnostic accuracy studies for AI/ML medical devices.

The document is a submission for a Class II medical device (Cardiopulmonary bypass temperature controller), and the FDA has determined it is substantially equivalent to legally marketed predicate devices. This means that extensive clinical trials, as one might expect for a novel AI/ML diagnostic or prognostic tool, are not reported here.

Therefore, the requested information regarding acceptance criteria, study design for proving device performance (including sample sizes, expert involvement, and ground truth establishment), MRMC studies, or standalone performance evaluation is not applicable to this type of regulatory submission (510(k) for a hardware medical device without AI/ML components for diagnostic purposes).

The performance data listed in Section VII, "PERFORMANCE DATA - NON-CLINICAL TESTING," are engineering and safety tests, not clinical performance studies in the vein of diagnostic accuracy. These include:

• Electrical safety
• Electromagnetic compatibility (EMC)
• Electrosurgery interference
• Hardware testing
• Aerosolization testing
• Software verification and validation
• Evaluation of heat transfer fluid for microbial suppression
• Shelf life testing of heat transfer fluid
• Usability evaluation

These are standard engineering and safety tests for such a device, and the document explicitly states "No clinical data were submitted to support the substantial equivalence of the Quantum Heater-Cooler to the Sorin/LivaNova Stöckert Heater-Cooler System 3T."

In summary, none of the requested information regarding acceptance criteria for diagnostic performance, sample sizes for test sets, expert ground truth, adjudication methods, MRMC studies, or standalone performance studies are present in this 510(k) summary because it is not a submission for an AI/ML diagnostic product, but rather for a hardware medical device demonstrating substantial equivalence to a predicate.

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The Quantum Mini Ventilation Module is intended to provide independently regulated O2 outputs for a controlled flow of O2 into the ECC circuit. The Quantum Mini Ventilation Module is an accessory that works with the Quantum workstation.

The following parameters are provided by the Quantum Mini Ventilation Module:

· Control of gas flow (02)

· Extracorporeal gas flow measurements for O2

The Quantum Mini Ventilation Module is only to be used by an experienced and trained clinician. It is not intended to be used by a patient or other untrained personnel.

The Quantum Mini Ventilation Module is a gas control unit that is intended to provide independently regulated oxygen outputs for a controlled flow for procedures involving an extracorporeal circuit.

The Quantum Mini Ventilation Module provides a simplified patient ventilation capability. The device consists of a single O2 input port that receives oxygen from the hospital infrastructure and two (2) oxygen outputs that are designed to provide O2 sweep and pO2 Reg.

The device interfaces with and is powered by any model of the Quantum workstation. The Quantum Mini Ventilation Module does not contain any user interface; instead, all measurements are displayed on the Quantum workstation.

This is a summary of the acceptance criteria and study information for the Quantum Mini Ventilation Module, based on the provided FDA 510(k) summary.

Note: This device is a cardiopulmonary bypass gas control unit, which typically involves engineering performance testing rather than studies involving AI algorithms or human reader performance with medical images. Therefore, many of the requested fields related to AI, medical image analysis, and expert consensus for ground truth are not applicable to this type of device.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not applicable in the context of a "test set" for an AI algorithm or medical image data. The testing described is engineering performance testing of hardware and software.
• Data Provenance: Not applicable. The testing is internal engineering and software validation.

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

• Not applicable. This is not an AI/image-based device where expert consensus on "ground truth" for a test set is typically established.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

• Not applicable. This is not an AI/image-based device requiring an adjudication method for a test set.

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. This is a medical device for controlling gas flow in cardiopulmonary bypass, not an AI-assisted diagnostic tool.

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

• Not applicable. This device is a hardware module with embedded software for control and measurement, not a standalone AI algorithm. It operates under the control of an experienced and trained clinician via the Quantum workstation.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• Ground Truth Type: For engineering performance testing, the "ground truth" refers to established engineering standards, specifications, and validated measurement techniques ensuring the device functions as designed and meets regulatory requirements for safety and performance (e.g., accurate flow measurements against calibrated instruments, electrical safety compliance, EMC compliance).

8. The sample size for the training set:

• Not applicable. This device does not use a "training set" in the machine learning sense. Software development involves verification and validation against requirements and design specifications.

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

• Not applicable. This device does not have a "training set" in the machine learning sense.

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