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The BP-200 plus is intended to be used as an adjunct to exercise stress testing devices. It is intended to measure and display diastolic and systolic blood pressure, heart rate, percentage of oxygen saturation in arterial blood (SpO2) and pulse rate in adult or adolescent patients during stress tests. The measurement cuff of the BP-200 plus in intended to be placed on the upper right arm of the patient. The BP-200 plus can be used for patients of both sexes and all races. The BP-200 plus should not be used with neonates.

The BP-200 plus, a microprocessor based non invasive blood pressure monitor and oxygen saturation measurement system intended to be used with stress-test systems, uses Korotkoff sounds to determine blood pressure and an optical ear sensor for oxygen saturation. An internal electric pump is used to inflate the cuff, and deflation is controlled by a valve. The BP-200 plus has the ability to make blood pressure at predetermined intervals (normally from a schedule determined by the physician), or on demand. Saturation measurements are updated once per second.

The BP-200 plus is powered by an external power supply (input: 230/110 V; output: 9V dc), and as an option by using four "AA" rechargeable batteries (≥ 2500 mAh). The batteries must be recharged with an external battery charger.

The SCHILLER BP-200 plus is an automated non-invasive blood pressure monitor system with an oxygen saturation measurement option, intended for use as an adjunct to exercise stress testing devices. It measures and displays diastolic and systolic blood pressure, heart rate, percentage of oxygen saturation in arterial blood (SpO2), and pulse rate in adult or adolescent patients during stress tests. It is not to be used with neonates.

Here's a breakdown of the acceptance criteria and study information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document states that the BP-200 plus has satisfactorily passed all tests according to the listed standards. While quantitative acceptance criteria (e.g., specific error margins for blood pressure measurements) are not explicitly stated in the provided text, the reference to these standards implies that the device achieved compliance with their respective requirements.

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

The document does not explicitly state the sample size (number of patients) used for the clinical test set. The provenance of the data is implied to be clinical studies conducted in adherence to international standards. The country of origin of the data is not specified directly, but the device manufacturer is SCHILLER AG in Switzerland, and the standards are international (ANSI/AAMI, EN, ISO, European Society of Hypertension). The studies are clinical tests, and based on the context of premarket notification for a new device, they would be considered prospective in nature.

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

The document states that the measurements of the BP-200 plus were "compared with manual auscultatory measurements." This implies that human experts (e.g., trained medical professionals) performed the manual auscultatory measurements, which served as the ground truth. However, the number of experts, their qualifications, or any specific details about the individuals establishing the ground truth are not provided.

4. Adjudication Method for the Test Set

The document indicates that the BP-200 plus measurements were "compared with manual auscultatory measurements." This likely means that the manual measurements served as the reference standard. However, the specific adjudication method (e.g., if multiple human readers took manual measurements and how discrepancies were resolved, such as 2+1, 3+1, or none) 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

A MRMC comparative effectiveness study, particularly one involving AI assistance, is not described in the provided text. The device is a blood pressure monitor, not an AI-assisted diagnostic tool for image interpretation or similar tasks that would typically involve MRMC studies in this context. The comparison is between the device's automated measurements and traditional manual measurements.

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

Yes, the primary clinical test described is a standalone performance evaluation of the BP-200 plus device. The device's measurements (algorithm only, as it's an automated monitor) are compared against the gold standard of manual auscultatory measurements. There is no indication of a human-in-the-loop interaction where an operator's performance is 'assisted' by the device in the way an AI would assist a radiologist.

7. The Type of Ground Truth Used

The ground truth used for the clinical tests was manual auscultatory measurements. This is a well-established clinical standard for blood pressure measurement.

8. The Sample Size for the Training Set

The document does not provide information about a separate "training set" or its sample size. As this device is a medical monitoring instrument and not an AI/machine learning algorithm that requires extensive data for model training in the conventional sense, the concept of a training set as understood in AI/ML is not directly applicable. The device's internal algorithms and calibration would have been developed and refined through engineering and validation processes, but these details are not specified as a "training set" in this context.

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

Since a "training set" in the context of AI/ML is not mentioned or implied for this device, information on how its ground truth was established is not applicable/provided. The development and calibration of the device's algorithms would have relied on engineering principles and potentially internal testing, but not explicitly on an external "training set" with established ground truth as described for AI models.

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