(237 days)
The Arm-type Fully Automatic Digital Blood Pressure Monitors are intended to measure blood Pressure (systolic and diastolic) of adults and adolescents over 12 years of age with circumference ranging from 22cm to 24cm.
The Arm-type Fully Automatic Digital Blood Pressure Monitor (BPM) series is automatic, non-invasive, blood pressure measurement system for over-the-counter (OTC) use at home and clinical environment . The systolic and diastolic pressures are determined using the oscillometric method, where the cuff is inflated with an integral controllable piezoelectric pump and deflates via an electric automatic rapid deflation valve. During inflation measurements, an electric pump within the main unit slowly inflates the arm cuff, generating cuff pressure which is monitored and from which pulse waveform data is extracted. This waveform data is analyzed by software algorithms within the microprocessor to determine systolic pressure, and diastolic pressure. The cuff can measure pressure range from 0 to 299mmHg.
Meanwhile, some models whith bluetooth function can be used as a stand-alone unit to finish the blood pressure measurement or in conjunction with the APP through embed a 2.4GHz BLE module that allow users to connect with nearby BT receiving terminal. Once measurement is over, the LCD of the device displays results. And the device will start to transmit data to the pair-up terminal automatically. This app is only intended to display measurement results from the blood pressure monitor device, which does not provide any diagnostic or measurement functions, and does not interpret or analyze the data for medical decision making. Unlimited readings can be stored in the app for archiving and review by the user.
There are total 7 arm-type blood pressure monitor models we submitted: DBP-62D0L, DBP-62D0B, DBP-61D0, DBP-61D0L, DBP-6293L, DBP-6193 and they are both bodyworn medical devices.
Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for the JOYTECH Arm-type Fully Automatic Digital Blood Pressure Monitor:
Acceptance Criteria and Reported Device Performance
The primary acceptance criteria for a non-invasive blood pressure monitor are its accuracy in measuring systolic and diastolic blood pressure. The relevant standard cited is ISO 81060-2:2018+AMD2020 (Non-invasive sphygmomanometers — Part 2: Clinical investigation of intermittent automated measurement type).
The document states: "The results showed the accuracy of the blood pressure monitor is within acceptable scope specified in ISO 81060-2:2018+AMD2020." While specific numerical performance values (e.g., mean difference and standard deviation for systolic and diastolic pressure) are not explicitly provided in this summary, the statement indicates that the device met the statistical requirements defined by this international standard for accuracy.
Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Based on ISO 81060-2:2018+AMD2020) | Reported Device Performance |
|---|---|
| Accuracy of Systolic Blood Pressure Measurement | Within acceptable scope specified in ISO 81060-2:2018+AMD2020 |
| Accuracy of Diastolic Blood Pressure Measurement | Within acceptable scope specified in ISO 81060-2:2018+AMD2020 |
Details of the Study Proving Device Meets Acceptance Criteria:
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Sample Size and Data Provenance:
- Test Set Sample Size: A total of 90 subjects were included in the clinical validation study.
- Data Provenance: The document does not explicitly state the country of origin of the data, but the manufacturer is based in China. The study appears to be prospective as it describes recruitment of subjects, their participation, and the measurement process.
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Number of Experts and Qualifications:
- The document mentions "The manual Mercury Sphygmomanometer was used as a reference device." This implies measurements were taken by trained observers (experts) using this reference device. However, the exact number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience" is not applicable here) are not specified in the provided text. For blood pressure clinical validation, these would typically be trained clinicians or technicians.
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Adjudication Method for the Test Set:
- The study used a "Same arm sequential method." This means the test device measurements and reference device measurements were taken sequentially on the same arm.
- The document does not describe a specific "adjudication method" in the sense of multiple experts reviewing and reaching consensus on an interpretation (as would be common in image-based AI studies). Instead, the comparison is made between the device readings and the reference standard readings.
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Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
- No, an MRMC comparative effectiveness study was not done. This type of study is typically performed for AI devices that assist human readers in interpreting medical images (e.g., AI for chest X-rays assisting radiologists). The device in question is a standalone blood pressure monitor, not an AI-assisted diagnostic tool that requires human interpretation of complex data.
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Standalone Performance:
- Yes, a standalone performance study was done. The clinical validation detailed here assesses the accuracy of the blood pressure monitor itself (the algorithm and hardware) against a recognized gold standard (manual mercury sphygmomanometer). The device operates independently to provide blood pressure measurements.
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Type of Ground Truth Used:
- The ground truth used was manual Mercury Sphygmomanometer measurements. This is a widely accepted reference standard for validating automated blood pressure monitors, considered an expert consensus/reference standard method in this context.
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Sample Size for the Training Set:
- The document does not provide information regarding the sample size of a training set. This is typical for a traditional medical device like a blood pressure monitor where the "algorithm" is often based on well-established non-AI methods (oscillometric principles) and validated through clinical testing rather than trained on large datasets like deep learning models. If there are software algorithms involved, they are likely more deterministic or based on classical signal processing rather than machine learning that requires a separate "training set."
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How Ground Truth for Training Set Was Established:
- As no "training set" in the context of typical AI/machine learning is mentioned, this information is not applicable and therefore not provided in the document. The device's underlying measurement principles (oscillometric method) use known physical principles, not a data-driven training process that requires a labeled ground truth for learning.
§ 870.1130 Noninvasive blood pressure measurement system.
(a)
Identification. A noninvasive blood pressure measurement system is a device that provides a signal from which systolic, diastolic, mean, or any combination of the three pressures can be derived through the use of tranducers placed on the surface of the body.(b)
Classification. Class II (performance standards).