(202 days)
The device is a digital monitor intended for use in measuring blood pressure and pulse rate in adult patient population with arm circumference ranging from 9 inches to 17 inches (22 cm to 42 cm). The device detects the appearance of irregular heartbeats during measurement and gives a warning signal with readings.
The device is a digital monitor intended for use in measuring blood pressure and pulse rate.
Here's a breakdown of the acceptance criteria and study information based on the provided document:
This document is a 510(k) clearance letter from the FDA for a blood pressure monitor, not a detailed scientific study report. Therefore, it does not contain granular data about specific acceptance criteria, statistical analysis, expert qualifications, or MRMC studies.
However, based on the context of FDA clearance for a medical device like a blood pressure monitor, we can infer some standard practices and regulatory requirements. I will fill in what can be directly extracted and indicate where information is typically found in such submissions but is not present in this letter.
Acceptance Criteria and Device Performance
The provided document does not explicitly list a table of acceptance criteria and reported device performance values. For a blood pressure monitor, typical acceptance criteria would be based on recognized standards like ISO 81060-2 (Non-invasive sphygmomanometers - Part 2: Clinical investigation of automated measurement type) or the AAMI/IEC 80601-2-30 standard. These standards define accuracy requirements (e.g., mean difference and standard deviation of blood pressure readings compared to a reference method).
In the absence of explicit data, a hypothetical table based on typical blood pressure monitor standards would look like this:
| Acceptance Criterion (Hypothetical based on ISO 81060-2/AAMI) | Reported Device Performance (Not in document, but would be in the submission) |
|---|---|
| Blood Pressure Accuracy | (e.g., Mean difference between test device and reference: ≤ ± 5 mmHg; Standard deviation of differences: ≤ 8 mmHg) |
| Systolic Blood Pressure: Mean Difference (Test - Reference) | e.g., +1.2 mmHg (within ± 5 mmHg) |
| Systolic Blood Pressure: Standard Deviation of Differences | e.g., 6.5 mmHg (within 8 mmHg) |
| Diastolic Blood Pressure: Mean Difference (Test - Reference) | e.g., +0.8 mmHg (within ± 5 mmHg) |
| Diastolic Blood Pressure: Standard Deviation of Differences | e.g., 5.9 mmHg (within 8 mmHg) |
| Pulse Rate Accuracy | |
| Pulse Rate: Mean Difference (Test - Reference) | (e.g., ≤ ± 5% or 5 beats/min) |
| Cuff Size Range Compatibility | |
| Arm circumference 9-17 inches (22-42 cm) | Confirmed by device indication for use. |
| Irregular Heartbeat Detection | Confirmed by device indication for use. |
Study Details
The provided document does not contain detailed information about the specific study methodology, sample sizes, or ground truth establishment. This information would be found in the 510(k) submission itself, typically under performance data.
Here's what can be inferred or stated about the missing information:
-
Sample size used for the test set and the data provenance:
- Sample Size: Not specified in the provided document. For clinical validation of a blood pressure monitor following ISO/AAMI standards, a minimum of 85 subjects is typically required, with specific distribution across blood pressure ranges.
- Data Provenance: Not specified. Clinical trials for FDA clearance can be conducted in various countries, and could be prospective. Retrospective studies are generally not applicable for initial clinical validation of a new blood pressure monitor's accuracy.
-
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Number of Experts: Not specified. For blood pressure measurements, ground truth is typically established by trained observers (often nurses or clinicians) using a mercury sphygmomanometer (or an alternative validated reference device) and a stethoscope, following specific protocols (e.g., auscultatory method). It's not usually "experts" in the sense of consensus panels, but rather skilled operators.
- Qualifications: "Trained observers" or "qualified personnel" experienced in auscultatory blood pressure measurement.
-
Adjudication method for the test set:
- Not specified. In blood pressure accuracy studies, adjudication for ground truth typically involves at least two trained observers making simultaneous readings, potentially blinded to each other's readings, and the average/consensus used as reference. Discrepancies might be resolved by a third observer or a predefined rule.
-
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:
- No, an MRMC study was almost certainly NOT done for this device. An OMRON Automatic Blood Pressure Monitor HEM-780N is a standalone measurement device. It does not involve "human readers" interpreting images or data where "AI assistance" would improve their performance. This type of study is relevant for diagnostic imaging AI algorithms.
-
If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Yes, implicitly. The device itself is the standalone algorithm/system. Its performance evaluation (accuracy against a reference standard) is by definition a standalone evaluation, as there isn't a human-in-the-loop interacting with its core measurement function in a way that would alter its fundamental accuracy. The user simply places the cuff and presses a button.
-
The type of ground truth used:
- Expert comparison/reference method. For blood pressure monitors, the ground truth is established by simultaneous measurements taken by trained observers using a validated reference method (e.g., auscultation with a mercury sphygmomanometer or an equivalent validated device) on the same subjects.
- Not pathology or outcomes data.
-
The sample size for the training set:
- Not specified. This document pertains to a hardware device with embedded algorithms, not a machine learning model where a distinct "training set" would be a primary component of the regulatory review in the way it is for AI/ML-based software as a medical device. The algorithms for signal processing and blood pressure estimation in such a device are typically developed and refined using engineering principles and proprietary datasets, not necessarily a "training set" in the common ML sense and are usually not detailed in FDA submissions unless they are novel AI/ML components.
-
How the ground truth for the training set was established:
- Not applicable in the conventional sense for this type of device and submission. If proprietary datasets were used during development, their ground truth would likely have been established similarly to the test set: comparison to a validated reference method.
§ 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).