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The IntraClude™ Intra-Aortic Occlusion device is indicated for use in patients undergoing cardiopulmonary bypass. The IntraClude Intra-Aortic Occlusion device occludes and vents the ascending aorta when the balloon is inflated. The device's central lumen allows delivery of cardioplegia to arrest the heart. The pressure lumen allows monitoring of the aortic root pressure.

The IntraClude Intra-Aortic Occlusion Device (model code ICF100) is a 10.5 Fr (3.5mm), triple-lumen, 100-cm-long catheter with an elastomeric balloon near its distal tip designed to occlude the ascending aorta in order to partition the aortic root from arterial circulation during cardiopulmonary bypass (CPB). The balloon expands to occlude a range of aorta sizes from 20 to 40 mm. The shaft is provided with an extended strain relief, which tapers from 10.5 Fr to the remaining 9 Fr catheter, and is designed to prevent kinking and to avoid compressing the shaft when the hemostasis valve of an arterial cannula introducer sheath is firmly closed around the catheter body.

The large central lumen of the IntraClude device serves three functions: accommodating the guidewire, delivering cardioplegia solution to the aortic root, and venting fluid and air from the aortic root. The two remaining lumens serve as conduits for balloon inflation/deflation and aortic root pressure monitoring. The hub has two flexible extension tubes with an integrated luer connection to provide access for accessories. The shaft is provided with markers to indicate the insertion depth. A Clamp-Lock™ device, provided on the extended strain relief portion, allows the IntraClude device to be locked in position to prevent balloon migration during aortic occlusion. The devices are provided sterile and non-pyrogenic; they are intended for single use only.

The provided text describes a 510(k) premarket notification for the "IntraClude Intra-Aortic Occlusion Device". This document is a regulatory submission for a medical device and does not describe an AI/ML-driven device. Therefore, many of the requested criteria related to AI/ML device performance (like acceptance criteria for AI algorithms, sample sizes for AI training/test sets, ground truth establishment by experts for AI, MRMC studies, or standalone algorithm performance) are not applicable to this document.

The document discusses validation for a physical medical device, not a software algorithm.

However, I can extract information regarding the functional/safety testing and general acceptance of the device described in the document.

Detailed Breakdown based on the provided document:

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

   Acceptance Criterion	Reported Device Performance
   Biocompatibility (per ISO 10993-1:2009/ Cor 1 for external communicating device, direct circulating blood path, duration ≤ 24 hours)	Tests included Cytotoxicity, Hemolysis, Systemic Toxicity, Irritation, and Sensitization. All data met pre-determined acceptance criteria.
   Tensile and Compression/friction testing (Confirmation of the strength of the catheter, lumens, and clamp lock)	All data met pre-determined acceptance criteria.
   Flow Rate (Inspection of catheter, lumens, and accessories for cardioplegia flow rate and pressure drop)	All data met pre-determined acceptance criteria.
   Catheter Bending/Kink Testing (Inspection of catheter, lumens, and accessories to confirm functionality after manipulation of the catheter)	All data met pre-determined acceptance criteria.
   Leakage Testing (Confirmation of device integrity after exposure to pressurized air and manipulation of the catheter, lumens, and accessories)	All data met pre-determined acceptance criteria.
   Design Validation by clinicians using a simulated use model (to verify that modifications meet user requirements and intended use)	All data met pre-determined acceptance criteria.

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

   • Sample size: Not explicitly stated for each test. The document generally states "All data met pre-determined acceptance criteria." without quantifying the sample sizes for the functional/safety tests.
   • Data provenance: Not specified. This document is a 510(k) submission to the FDA (USA), implying the testing was conducted to meet US regulatory standards, but the physical location or specific type of data (retrospective/prospective) for the tests are not detailed.

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)

   • For "Design Validation", it states it was "performed by clinicians". The number and specific qualifications (e.g., years of experience, specialty) of these clinicians are not provided. This validation is for user requirements and intended use, not establishing "ground truth" in the AI/ML sense.

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

   • Not applicable as this is not an AI/ML device requiring ground truth adjudication by multiple readers. The "Design Validation" involved "clinicians" but the adjudication method among them is not described.

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. This is not an AI/ML device.

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

   • Not applicable. This is not an AI/ML device. The functional/safety tests evaluate the physical device's performance standalone in a controlled environment or in simulated use.

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

   • For the physical device, "ground truth" is defined by pre-determined engineering and biological acceptance criteria for tests like biocompatibility, tensile strength, flow rate, bending, and leakage.
   • For "Design Validation", it's based on "user requirements and intended use" as verified by clinicians in a simulated environment.

8. The sample size for the training set

   • Not applicable. This is not an AI/ML device, so there is no "training set."

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

   • Not applicable. This is not an AI/ML device, so there is no "training set" or corresponding ground truth establishment process in that context.

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