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KD-7035B, KD-7921 and KD-7903 Fully Automatic Electronic Blood Pressure Monitor is for use by medical professionals or at home and is a non-invasive blood pressure measurement system intended to measure the diastolic and systolic blood pressures and pulse rate of an adult individual by using a non-invasive technique in which an inflatable cuff is wrapped around the wrist. The cuff circumference is limited to 14cm- 25cm.

KD-7035B, KD-7921 and KD-7903 Fully Automatic Electronic Blood Pressure Monitor is for use by medical professionals or at home and is a non-invasive blood pressure measurement system intended to measure the diastolic and systolic blood pressures and pulse rate of an adult individual by using a non-invasive technique in which an inflatable cuff is wrapped around the wrist. The cuff circumference is limited to 14cm-25cm. It is designed and manufactured according to ANSI/AAMI SP10-manual. electronic or automated sphygmanometers. The operational principle is based on oscillometric and silicon integrate pressure sensor technology, the result will be shown on a LCD with an electronic interface module, the result of KD-7035B can also be classified and displayed by the function of blood pressure classification indicator, the memory capability of KD-7035B, KD-7921 and KD-7903 are 2 × 60 times, 60 times and 3 × 40 times. If any irregular heartbeat is detected, it can be shown on the LCD of KD-7035B and KD-7921.

The provided text describes three models of Fully Automatic Electronic Blood Pressure Monitors (KD-7035B, KD-7921, and KD-7903) and their substantial equivalence to a predicate device (KD-738). The summary states that the devices conform to several standards, including AAMI SP10 which outlines performance requirements for sphygmomanometers. However, the document does not present a detailed study with specific acceptance criteria and reported device performance in table format. It also lacks explicit information regarding sample sizes, data provenance, ground truth establishment for test and training sets, the number and qualifications of experts, adjudication methods, or MRMC comparative effectiveness studies.

Based on the information provided in the 510(k) summary, here's what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

The document states that the devices conform to "AAMI SP10:2002, Manual, electronic or automated sphygmomanometers" and its amendments. AAMI SP10 typically sets accuracy requirements for blood pressure devices. While the exact acceptance criteria from AAMI SP10 are not explicitly listed in the submission, the standard generally requires that:

• Mean difference: The mean difference between the device reading and the reference standard should be within ±5 mmHg.
• Standard deviation: The standard deviation of the differences should be 8 mmHg or less.

However, the document does not provide a table with these specific acceptance criteria and the reported device performance results (e.g., actual mean difference and standard deviation achieved by KD-7035B, KD-7921, and KD-7903 during testing). It only states that "the test in this submission provides demonstration that these small differences do not raise any new questions of safety and effectiveness," implying that the devices met the relevant standards.

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

• Sample Size for Test Set: Not specified in the provided text.
• Data Provenance: Not specified, though the submitter is based in P.R. China, which might suggest data could originate from that region. The study is implicitly retrospective, as it's a premarket submission.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. For blood pressure device validation, experts are typically trained clinicians who manually measure blood pressure using auscultation with a reference sphygmomanometer.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified. In AAMI SP10 validation, typically two or more observers are used for reference measurements, and their readings are averaged or adjudicated if they differ significantly.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

• The device is a blood pressure monitor, not an AI-powered diagnostic imaging system. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not applicable and was not performed.

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

• The devices are standalone electronic blood pressure monitors designed to provide direct measurements. The performance implicitly refers to the standalone performance of the algorithm within the device compared to a reference standard (manual auscultation in a clinical trial). The document does not describe "human-in-the-loop" performance as it's not relevant for this type of device.

7. The Type of Ground Truth Used

• Type of Ground Truth: For blood pressure monitors conforming to AAMI SP10, the ground truth is typically established by simultaneous, direct auscultatory measurements performed by trained observers using a mercury sphygmomanometer or another validated reference device. This method provides the clinically accepted "true" blood pressure values for comparison.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not specified. For this type of device, a "training set" in the context of deep learning or complex AI algorithms is not typically used for basic oscillometric blood pressure monitors. The "training" or calibration would have occurred during the device's engineering and design phases, likely using internal data and algorithms based on known physiological principles.

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

• How Ground Truth for Training Set Was Established: Not specified. As noted above, the concept of a distinct "training set" with established ground truth, as defined in AI studies, is not directly applicable here. The device's algorithms are developed and refined based on engineering principles and potentially internal test data.

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