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The intra-aortic balloon is placed in the descending aorta just below the subclavian artery and is intended to improve cardiovascular functioning during the following situations:
Refractory ventricular failure
Cardiogenic shock
Unstable refractory angina
Impending infarction
Mechanical complications due to acute myocardial infarction
Ischemic related intractable ventricular arrhythmias
Cardiac support for high risk surgical patients and coronary angiography or angioplasty patients
Septic shock
Weaning from cardiopulmonary bypass
Interoperative pulsatile flow generation
Support for failed angioplasty and valvuloplasty

The intra-aortic balloon is placed in the descending aorta just below the subclavian artery and is intended to improve cardiovascular functioning during the following situations: Refractory ventricular failure Cardiogenic shock Unstable refractory angina Impending infarction Mechanical complications due to acute myocardial infarction Ischemic related intractable ventricular arrhythmias Cardiac support for high risk surgical patients and coronary angiography or angioplasty patients Septic shock Weaning from cardiopulmonary bypass Interoperative pulsatile flow generation Support for failed angioplasty and valvuloplasty

The provided text describes a 510(k) premarket notification for a medical device, the Datascope 8Fr. Co-Lumen (CL) Intra-Aortic Balloon (IAB) & Accessories. This submission focuses on demonstrating substantial equivalence to previously cleared predicate devices, rather than a clinical study with specific acceptance criteria and detailed performance metrics as one might find for a novel device or AI/software.

Therefore, the information typically expected for "acceptance criteria and the study that proves the device meets the acceptance criteria" in the context of an AI/software device is not applicable or available in this document. This submission is for a physical medical device (Intra-Aortic Balloon) seeking clearance based on its similarity to existing cleared devices, not on a performance study against predefined numerical acceptance criteria.

However, I can extract the relevant information from the document that addresses the spirit of your questions as much as possible within the context of a 510(k) for a physical medical device.

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

For a 510(k) submission, "acceptance criteria" are generally that the new device must be "substantially equivalent" to a predicate device in terms of intended use, technological characteristics, and safety and effectiveness. The "reported device performance" is a demonstration that this equivalence holds.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided in the document. The submission references "in-vitro tests" but does not detail the sample size or provenance of data for these tests. For a physical device 510(k), these tests are typically benchtop or mechanical tests, not clinical performance studies with patient data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not applicable/provided. The concept of "ground truth" established by experts for a test set is typically relevant for diagnostic or AI/software devices where expert interpretation is part of the validation. For this physical IAB, the "ground truth" would be established by engineering specifications and in-vitro test results comparing against predicate device performance.

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

This information is not applicable/provided. Adjudication methods are typically used in clinical studies involving interpretation, which is not the primary mode of evaluation for this physical device in a 510(k) context.

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 information is not applicable. An MRMC study is designed for evaluating diagnostic devices, especially those with AI components, and the impact of AI on human reader performance. This submission is for a physical Intra-Aortic Balloon and does not involve AI or human readers for diagnostic tasks.

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

This information is not applicable. This question pertains to AI/software performance. The device is a physical intra-aortic balloon.

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

The document mentions "in-vitro tests" (Page 4). The "ground truth" for demonstrating substantial equivalence for a physical device like an IAB would primarily be based on engineering specifications, material science standards, and established performance characteristics of the predicate devices as measured through these in-vitro tests. There is no mention of expert consensus, pathology, or outcomes data being used as "ground truth" for the 510(k) submission itself, though clinical outcomes from general use of IABs would underpin the predicate devices' prior clearances.

8. The sample size for the training set

This information is not applicable. The device is a physical medical device, not an AI/machine learning algorithm that requires a "training set."

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

This information is not applicable. As it's not an AI/ML device, there is no training set or ground truth in that context.

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Datascope's 10 Fr. FlexiSheath™ Percutaneous Introducer is intended for the percutaneous introduction of Datascope's 9.5Fr. Intra-Aortic Balloon Catheters.

Datascope's 10 Fr. FlexiSheath™ Percutaneous Introducer is intended for the percutaneous introduction of Datascope's 9.5 Fr. Intra-Aortic Balloon Catheters.

The provided text describes a 510(k) premarket notification for Datascope's 10 Fr. FlexiSheath™ Percutaneous Introducer. In the context of 510(k) submissions, the concept of "acceptance criteria" and a "study that proves the device meets the acceptance criteria" in the traditional sense of a clinical trial with predefined performance metrics and statistical endpoints is generally not applicable for devices seeking substantial equivalence.

Instead, the submission aims to demonstrate that the new device is "substantially equivalent" to predicate devices already on the market. This is done by showing that the new device has similar technological characteristics and performance, and does not raise different questions of safety and effectiveness.

Here's an analysis of the provided text based on your request, interpreting "acceptance criteria" as the demonstration of substantial equivalence and "study" as the non-clinical testing performed:

1. Table of Acceptance Criteria (Substantial Equivalence Pillars) and Reported Device Performance

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: The document mentions "in-vitro tests" but does not specify the sample size used for these tests.
• Data Provenance: The document does not specify the country of origin of the data or whether the tests were retrospective or prospective. Given that this is an in-vitro study for a US submission, it's highly likely the tests were conducted in a US-based lab, but this is not explicitly stated. The tests are prospective in nature as they evaluate the device's characteristics.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• This information is not applicable or provided in the context of this 510(k) submission. For in-vitro tests demonstrating substantial equivalence, "ground truth" is typically established by engineering specifications, validated test methods, and comparison against the performance of predicate devices, not by expert consensus on clinical data. No human experts evaluating test results for "ground truth" are mentioned.

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

• This information is not applicable or provided. Adjudication methods are typically used in clinical studies involving interpretation of medical images or patient outcomes, not for in-vitro engineering tests.

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, an MRMC comparative effectiveness study was not done. This type of study is relevant for AI-powered diagnostic devices, which is not the nature of Datascope's 10 Fr. FlexiSheath™ Percutaneous Introducer.
• The device is a medical introducer sheath, a physical medical device, not an AI or imaging diagnostic tool. Therefore, the concept of human readers improving with AI assistance is not applicable.

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

• No, a standalone (algorithm only) study was not done. As mentioned, this is a physical medical device, not an algorithm or software. Its performance is evaluated through physical and material properties, and functionality in a lab setting.

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

• For the non-clinical tests, the "ground truth" would be established by engineering specifications, validated test methods, and direct comparison of performance metrics (e.g., tensile strength, flexibility, lubricity, burst pressure, flow rates, etc.) against the established performance of the predicate devices. It is not based on expert consensus, pathology, or outcomes data.

8. The sample size for the training set

• This information is not applicable or provided. The concept of a "training set" and "validation set" is relevant for machine learning algorithms. This submission is for a physical medical device and relies on engineering and material characteristic tests rather than data-driven model training.

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

• This information is not applicable or provided as there is no "training set" in the context of this device and its substantial equivalence submission.

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