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The Datascope Chronic Dialysis catheter is indicated for use in attaining longterm access for hemodialysis and apheresis. It may be implanted percutaneously and is primarily placed in the internal jugular vein of an adult patient. Alternate insertion sites include the subclavian vein as required.

The Datascope Chronic Dialysis Catheter is double lumen polyurethane catheter (Carbothane) that provides two dedicated (arterial/venous) access lumens. Both lumens are "D" shaped, open at the distal tip, with and without side holes. Each lumen is connected through an extension line with female luer connectors. The transition between lumen and extension is housed within a molded hub.

The provided document is a 510(k) premarket notification for a medical device, the Datascope Chronic Dialysis Catheter. It outlines the device's description, intended use, and comparison to a predicate device, along with the FDA's clearance letter. However, this document does not contain information about acceptance criteria and a study proving the device meets those criteria in the context of an AI/ML medical device.

The "Performance Data" section explicitly states: "In vitro performance data... all demonstrate that this device is substantially equivalent to the legally marketed predicate device. Clinical studies were not deemed necessary since in vitro testing was sufficient to demonstrate safety and effectiveness by way of comparison to legally marketed predicate device."

Therefore, I cannot provide the requested information for an AI/ML device study.

If this were an AI/ML device, the typical sections I would look for to respond to your request would cover:

1. A table of acceptance criteria and the reported device performance: This would detail metrics like sensitivity, specificity, accuracy, AUC, F1-score, or agreement rates, along with predefined thresholds for acceptable performance (e.g., "sensitivity must be >90%").
2. Sample size used for the test set and the data provenance: Information on how many cases were in the test set, from what countries or institutions the data originated, and whether it was collected retrospectively or prospectively.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Details on how many human experts reviewed the test cases and their relevant medical qualifications (e.g., "three board-certified radiologists with an average of 15 years experience in oncology imaging").
4. Adjudication method for the test set: How disagreements among experts were resolved (e.g., "majority vote," "consensus panel," "senior expert tie-breaker").
5. If a multi-reader, multi-case (MRMC) comparative effectiveness study was done: Details on such a study, including the improvement in human reader performance with AI assistance (effect size).
6. If a standalone performance study was done: The results of the algorithm's performance without human intervention.
7. The type of ground truth used: Whether the ground truth was based on expert consensus, histopathology, long-term patient outcomes, or other definitive diagnostic methods.
8. The sample size for the training set: How many cases were used to train the AI model.
9. How the ground truth for the training set was established: The method used to label the training data, similar to the test set ground truth establishment.

Since the provided document is for a physical medical catheter and not an AI/ML device, these elements are not present.

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