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The Dade® PFA-100® Platelet Function Analyzer and associated reagents are in vitro diagnostic devices intended to aid in the detection of platelet dysfunction in citrated human whole blood.

To aid in the detection of platelet dysfunction in citrated human whole blood.

The PFA-100® system provides a tool for clinicians to use in the detection of platelet dysfunction induced by intrinsic platelet defects, von Willebrand factor (vWF) functional deficiencies, or exposure to platelet inhibiting agents. The PFA-100® system simulates, under high shear stress, the interaction of platelets with an injured blood vessel. These conditions allow the PFA-100® system to measure in vitro platelet function as related to primary hemostasis.

This 510(k) summary (K060489) for the Dade® PFA-100® Platelet Function Analyzer and Reagents primarily references existing literature to demonstrate performance, rather than presenting a single, dedicated device performance study with specific acceptance criteria. This is common for submissions claiming substantial equivalence to an already marketed device (K970505 and K002885).

Therefore, the acceptance criteria are not explicitly stated in a quantitative form with pass/fail thresholds for the new device itself. Instead, the submission uses existing studies to show that the device performs similarly to or confirms known clinical observations related to platelet function.

Here's an attempt to extract the requested information based on the provided text, acknowledging that some details are inferred or not explicitly stated for a "new device" performance study but rather for established device performance:

1. Table of Acceptance Criteria and Reported Device Performance

As noted, the acceptance criteria are not explicitly defined for a new device study. Instead, the performance is demonstrated through studies showing the PFA-100's ability to detect platelet dysfunction and assess DDAVP treatment, with specific sensitivity, specificity, and efficiency values reported for various conditions. These values, derived from previously published studies, implicitly serve as the "performance" against which the device's utility is being demonstrated.

2. Sample Size Used for the Test Set and the Data Provenance

The submission references multiple studies, each with its own sample size and provenance:

• Cariappa et al.: 52 patients. Data provenance not specified (retrospective/prospective, country).
• Lippi et al.: 52 patients. Data provenance not specified (retrospective/prospective, country).
• Fressinaud et al.: 41 patients (23 VWD Type I). Data provenance not specified (retrospective/prospective, country).
• Koscielny et al. (First Entry): 5649 patients. Data provenance not specified (retrospective/prospective, country).
• Koscielny et al. (Second Entry):
  • 256 impaired hemostasis in prospective study.
  • 5102 (317 impaired hemostasis) in retrospective study.
  • Combined total of 5649 patients in the overarching study.
  • Data provenance not specified for country of origin, but study design (prospective/retrospective) is mentioned for parts of it.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

The provided text does not contain any information regarding the number of experts or their qualifications used to establish ground truth for the cited studies. The "ground truth" was established using a combination of other diagnostic assays and clinical outcomes (e.g., bleeding history, aggregometry, bleeding time, VWF assays, PT, aPTT, FVIII activity, RIPA, VWF multimers, pathology for VWD/platelet defects, and observation of DDAVP treatment response).

4. Adjudication Method for the Test Set

The provided text does not contain any information on adjudication methods for establishing ground truth in the cited studies. Given the nature of the studies (often using multiple clinical and laboratory assessments to reach a diagnosis), it's likely a form of comprehensive clinical diagnosis rather than a single adjudication process for an AI model.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study focusing on human readers improving with AI vs. without AI assistance was not done or reported in this submission. The PFA-100 is an automated diagnostic device, not an AI-assisted interpretation tool for human readers in the context of what an MRMC study typically assesses for AI.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, the performance reported for the PFA-100 system is standalone performance. The PFA-100 is an automated instrument that directly measures platelet function and provides quantitative results (Closure Times). The "device performance characteristics" section describes the output solely based on the device's measurements in various clinical scenarios, without human-in-the-loop interaction in generating the primary analytical results.

7. The Type of Ground Truth Used

The ground truth for the cited studies was established using a combination of clinical diagnoses, other laboratory assays, and observed patient outcomes/responses to treatment.

Examples include:

• Clinical diagnosis of platelet defects or VWD: Based on bleeding history and a battery of tests (bleeding time, aggregometry, platelet count, PT, aPTT, VWF assays, VWF:Ag, VWF:RCo, FVIII activity, RIPA, VWF multimers, flow cytometry).
• Response to DDAVP treatment: Normalization of PFA CTs and assessment of blood usage.
• Impaired hemostasis: Based on clinical picture and a range of diagnostic tests.

8. The Sample Size for the Training Set

The device is a PFA-100 instrument and reagents, which functions based on physical principles (simulating shear stress and platelet-collagen interaction), not a machine learning or AI algorithm that typically requires a "training set" in the presented context. Therefore, the concept of a "training set sample size" as used for AI/ML development does not apply here. The device's operational parameters and reagents are developed through R&D and calibration rather than typical machine learning training.

9. How the Ground Truth for the Training Set Was Established

As explained above, since this is not an AI/ML device, the concept of a "training set" and establishing "ground truth for a training set" in that specific context is not applicable. The device's design and calibration would have been based on established hematological principles and empirical testing to ensure accurate and reproducible measurement of platelet function.

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