Search Results

The XScribe/Q-Stress device is intended to acquire, process, record, archive, analyze, and output electrocardiographic data during physiologic stress testing. The device is intended for use in adult, adolescent, and children patient populations. The device is intended for use in a clinical setting by trained personnel under the supervision of a licensed physician.

The device may interface with equipment for pulmonary function testing and other devices, including a treadmill or ergometer for dynamic exercise evaluation, as well as non- invasive blood pressure equipment, functional arterial oxygen saturation (SpO2) equipment, and computer communications equipment.

The device is not intended to be used as a vital signs physiological monitor.

The XScribe / Q-Stress device is a diagnostic device capable of real time ECG display, heart rate measurement, ST analysis and ventricular ectopic beat detection using wired or wireless acquisition modules. The device is able to generate risk scoring via recognized protocols. The device is capable of obtaining a resting ECG with automatic interpretation. The device can interface with equipment for pulmonary evaluation. The device has several built in exercise protocols for connection and control of exercise equipment such as treadmills and ergometers. The device supports measurement of non-invasive blood pressure. The device can output analog ECG signals or digital QRS trigger signals for synchronizing an external device. The device supports a touch screen interface as well as a keyboard / mouse interface and a wired remote control unit. The device will store a complete record of diagnostic quality test data from which the user can generate and review stress test reports. The device can function as standalone workstation or can connect via network to a database server allowing for remote review capabilities. The device can communicate with electronic record keeping systems to obtain work lists and patient data, and to provide test result reports.

This document is a 510(k) premarket notification for the Mortara Instrument, Inc. XScribe and Q-Stress Stress Exercise Testing Systems. It asserts substantial equivalence to a predicate device, XScribe II (K022618).

The crucial point for the request is that this submission explicitly states that clinical data was NOT required or used to support substantial equivalence.

Therefore, I cannot provide the requested information regarding acceptance criteria, device performance, test set details (sample size, provenance, ground truth experts, adjudication), MRMC studies, standalone performance, or training set details because no such studies were performed or are presented in this document for the purpose of demonstrating substantial equivalence.

The document primarily relies on non-clinical data, software verification and validation, and compliance with applicable electrical and medical device standards to establish substantial equivalence.

Here's how I can address the prompts based on the provided text:

Acceptance Criteria and Study for DEVICE PERFORMANCE (Based on Available Information):

Since no clinical study data is provided in this 510(k) submission to demonstrate the device meeting performance acceptance criteria in a clinical setting against a ground truth, the "acceptance criteria" here refer to the engineering and regulatory compliance criteria used for a 510(k) submission based on substantial equivalence.

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Not Applicable / Not Provided for Clinical Performance. The submission explicitly states: "The subject of this premarket notification did not require clinical data to support substantial equivalence."
• For the non-clinical software and hardware testing, the specific sample sizes or data provenance (e.g., number of test cases, simulated data characteristics) are not detailed in this summary. These would typically be found in the full testing reports which are not part of this public summary.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

• Not Applicable / Not Provided. As no clinical performance study was conducted or presented to establish ground truth for clinical outcomes, no such experts were involved for this purpose in this submission.

4. Adjudication Method for the Test Set:

• Not Applicable / Not Provided. No clinical performance study requiring adjudication was presented.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

• No. The document explicitly states that clinical data was not required or utilized for this submission. Therefore, no MRMC study was conducted.

6. If a Standalone (algorithm only without human-in-the-loop) performance was done:

• Not Applicable / Not Provided as a distinct clinical study. The device "acquires, processes, records, archives, and outputs electrocardiographic data" and performs "ST analysis and ventricular ectopic beat detection." While these are algorithmic functions, their standalone performance in a clinical diagnostic context (e.g., sensitivity/specificity for detecting certain conditions) is not quantified or presented in this 510(k) submission through a dedicated study with clinical ground truth. The V&V activities would assess the technical correctness of these algorithms.

7. The Type of Ground Truth Used:

• For the non-clinical testing, the "ground truth" would be the engineering specifications, regulatory standards, and expected behavior of the software and hardware components. This is not clinical ground truth (e.g., pathology reports, expert consensus on patient diagnosis).
• For clinical performance, no ground truth was established or used, as no clinical study was performed.

8. The Sample Size for the Training Set:

• Not Applicable / Not Provided. This device is largely based on known physiological principles and signal processing, and it is asserting substantial equivalence to an existing predicate. The nature of this 510(k) (not a novel AI/ML device relying on extensive data training for a diagnostic claim) means that a "training set" in the context of machine learning model development is not relevant or disclosed here. Its processing capabilities are likely rule-based or parametrically driven from established algorithms.

9. How the Ground Truth for the Training Set was Established:

• Not Applicable / Not Provided. See point 8.

Ask a specific question about this device