Search Results
Found 2 results
510(k) Data Aggregation
(375 days)
The Prizma device is indicated for home users to measure, record, display and/or transmit ECG one-lead data, heart rate, peripheral oxygen saturation, pulse rate and body temperature data. The device utilizes a mobile platform to initiate user actions (test, display and data transfer by email) through the mobile application.
Intended population: adult patients.
The Prizma device described in this submission is based on the cleared Prizma device (K170181), and its physical design has not been modified.
The following algorithm functions have been added to the system, thereby extending the indications for use:
a) conversion of the previously cleared and reported skin temperature measurement into body temperature
b) Pulse rate calculation using the oximeter function.
The sensor capabilities of the new Prizma are as follows:
- . One lead ECG sensor - ECG rhythm recording and heart rate measurement
- . Photo-plethysmography - Peripheral capillary Oxygen Saturation (SPO2) measurement, pulse rate:
- . IR thermometer - skin temperature converted into body temperature.
Here's a summary of the acceptance criteria and the study details for the Prizma device, based on the provided text:
Acceptance Criteria and Reported Device Performance
The document does not explicitly present a table of acceptance criteria with corresponding device performance metrics. Instead, it refers to compliance with specific ISO standards related to device performance and outlines the comparison against reference measurements in a clinical validation.
However, based on the information provided, we can infer the primary performance evaluation was for the Prizma IR measured skin temperature to body temperature conversion.
The study aimed to demonstrate:
- Clinical bias
- Limits of agreement
- Clinical repeatability
These were "calculated in accordance with requirements in ISO 80601-2-56: 1st edition 2009."
Since the document concludes that "Performance data demonstrate that the Prizma is as safe and effective as the predicate devices," it implies that the device met the requirements of this ISO standard for these statistical characteristics.
Study Details:
-
Table of Acceptance Criteria and Reported Device Performance:
- Acceptance Criteria (Implicit, based on ISO 80601-2-56): Adherence to statistical characteristics (clinical bias, limits of agreement, clinical repeatability) as defined by ISO 80601-2-56 for body temperature measurement.
- Reported Device Performance: The document states that these statistical characteristics "were calculated" and that "Performance data demonstrate that the Prizma is as safe and effective as the predicate devices," implying successful adherence to the standard's requirements. No specific numerical values for bias, limits of agreement, or repeatability are provided in this document.
-
Sample Size Used for the Test Set and Data Provenance:
- Sample Size: 167 patients.
- Percentage of Febrile Patients: 57% of the patient cohort were febrile.
- Data Provenance: Not explicitly stated, but clinical validation suggests prospective data collection in a clinical setting. No country of origin is mentioned for the patient data.
-
Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:
- Not applicable. The ground truth method described does not involve expert consensus on interpretations but rather direct measurement comparisons.
-
Adjudication Method for the Test Set:
- Not applicable. The ground truth method involved a direct measurement comparison against a reference device, not an adjudication of interpretations.
-
If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done:
- No. This was a clinical validation study focused on the accuracy of a physiological measurement (temperature conversion), not a comparative effectiveness study involving human readers and AI assistance for diagnostic interpretation.
-
If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, for the temperature conversion aspect. The Prizma IR measured skin temperature was compared against a reference (mercury thermometer) to validate the algorithm's conversion to body temperature. The device is for home users to measure, record, display, and/or transmit data, suggesting standalone measurement capability.
-
The Type of Ground Truth Used:
- Clinical Validation / Reference Standard: Auxiliary temperature (under armpit) measured by a mercury thermometer over a 10-minute period.
-
The Sample Size for the Training Set:
- Not provided. The document focuses on the validation of the temperature conversion algorithm and does not detail the training phase or its dataset size.
-
How the Ground Truth for the Training Set was Established:
- Not provided. The document does not discuss the training set or its ground truth establishment.
Ask a specific question about this device
(91 days)
The Raisin™ Personal Monitor is a miniaturized, wearable data-logger for ambulatory recording of heart rate, activity, body angle relatively to gravity, and time-stamped, patient-logged events. The Raisin™ Personal Monitor enables unattended data collection for clinical and research applications. The Raisin™ Personal Monitor may be used in any instance where quantifiable analysis of eventassociated heart rate, activity, and body position is desirable.
The Raisin100 Personal Monitor (RPM) is a miniaturized, ambulatory, battery-operated datalogging device that is worn on the torso to record heart rate, activity, and patient-logged events. Patient-logged events can be extrinsic (e.g., dosing of a medication) or intrinsic (e.g., a symptom) and are time-stamped using a manual button on the device, in order to contextualize the physiologic measures. Subjective meaning of these events is assigned by the user. In addition to quantification of physical motion, signals from the device's accelerometer are used to determine body position relative to gravity. Electrode-to-electrode impedance is also measured to assess whether the device is attached properly to the user. RPM recorded data are transferred via Bluetooth telemetry to a general computing device for display and conversion for export to other programs. The RPM is available in two form factors to accommodate individual comfort preferences: one-piece and two-piece. The functionality, intended use, duration and location of wear, and fundamental scientific technologies are exactly the same between the two RPM form factors.
Here's a breakdown of the acceptance criteria and study information for the Raisin™ Personal Monitor, based on the provided text:
Acceptance Criteria and Device Performance
Parameter | Acceptance Criteria (Expected Results) | Reported Device Performance (Algorithm Results) |
---|---|---|
Heart Rate R-wave Detection (Bench Testing - ANSV/AAMI EC 13 standard) | ||
Default ECG waveform | 80 bpm | 80.0 bpm |
T-wave rejection (R-wave 1 mV, T-wave 0.4 mV) | 80 bpm | 80.0 bpm |
Ventricular bigeminy | 80 bpm | 79.9 bpm |
Slow alternating ventricular bigeminy | 60 bpm | 60.5 bpm |
Rapid alternating ventricular bigeminy | 120 bpm | 119.8 bpm |
Bidirectional systoles | 90 bpm | 90.1 bpm |
Default ECG waveform (Pacing pulse 2 mV, 2 ms width) | 80 bpm | 80.0 bpm |
Heart Rate R-wave Detection (Arrhythmia Database) | ||
Positive Detection Accuracy | Not explicitly stated (implied high) | Median: 99.7%, Standard Deviation: 5.9% |
False Positive Rate | Not explicitly stated (implied low) | Median: 0%, Standard Deviation: 1.7% |
Heart Rate R-wave Detection (Clinical Setting - Various Body Locations) | Not explicitly stated (implied near 100%) | Anterior Chest: 99.40% |
Xyphoid: 99.17% | ||
Stomach: 99.07% | ||
Lateral Chest: 98.82% | ||
(Average R-wave detection accuracy) | ||
Accelerometer (Bench Validation) | Known acceleration applied against each of its three axes to demonstrate linearity | Shown in scatter plots with strong linear correlation between measured and applied acceleration for X, Y, and Z axes. |
Accelerometer (Clinical Validation) | Capture expected features of subject movement (e.g., walking) | Demonstrated data from a representative walking test showing expected acceleration fluctuations. |
Study Information
-
Sample size used for the test set and the data provenance:
- Heart Rate R-wave Detection (Arrhythmia Database): All 48 test files from the MIT-BIH arrhythmia database.
- Heart Rate R-wave Detection (Clinical Setting - Various Body Locations): Data from 4 subjects (Subject 1, Subject 2, Subject 4, Subject 5) across different body locations. The provenance is not explicitly stated but implies prospective clinical data collection for these subjects.
- Accelerometer (Clinical Validation): Data from a "representative walking test" and "a representative subject." The exact number of subjects or tests is not explicitly stated.
- Accelerometer (Bench Validation): Not applicable for test set size as it's a bench validation with known inputs.
- Data Provenance: The MIT-BIH arrhythmia database is a publicly available, retrospective database. Clinical data for the heart rate and accelerometer validation appears to be prospectively collected (e.g., "representative walking test," "subject 1, chest location, sitting").
-
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Heart Rate R-wave Detection (Arrhythmia Database): Not specified in the provided text. The MIT-BIH arrhythmia database has widely accepted expert annotations, but the number and qualifications of the original annotators are not detailed here.
- Heart Rate R-wave Detection (Clinical Setting - Various Body Locations): Not specified. The reference is to "automatically identified R-waves highlighted" against the captured ECG waveform, implying a comparison against the raw ECG, but the method for establishing the true R-wave locations for accuracy calculation (e.g., manual expert review) is not detailed.
- Accelerometer (Bench Validation): Not applicable, as ground truth is the "known acceleration applied."
- Accelerometer (Clinical Validation): Not specified. The "expected features" of walking would likely derive from general biomechanical understanding, not specific expert annotations for each test.
-
Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not specified in the provided text for any of the studies.
-
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 comparative effectiveness study involving human readers with and without AI assistance was not performed or reported. This device is primarily a data logger and not an AI-assisted diagnostic tool for human readers.
-
If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Yes, the reported performance metrics (e.g., R-wave detection accuracy, false positive rate, accelerometer linearity) are all indicative of standalone algorithm performance. The device is described as an "unattended data collection" system.
-
The type of ground truth used (expert consensus, pathology, outcomes data, etc):
- Heart Rate R-wave Detection (Arrhythmia Database): R-wave locations were "annotated R-wave locations" from the MIT-BIH arrhythmia database, which typically involves expert-reviewed annotations.
- Heart Rate R-wave Detection (Bench Testing): "Expected Results (bpm)" based on the ANSV/AAMI EC 13 standard, implying predefined reference values for specific ECG waveforms.
- Heart Rate R-wave Detection (Clinical Setting - Various Body Locations): "Automatically identified R-waves highlighted" implies comparison against captured ECG, but the true ground truth for accuracy calculation is not explicitly stated (e.g., manual expert annotation of raw ECG).
- Accelerometer (Bench Validation): "Known acceleration applied" (physical inputs).
- Accelerometer (Clinical Validation): "Expected features" of movement (e.g., walking), based on general physiological understanding of movement patterns.
-
The sample size for the training set: Not specified. The studies describe validation testing but do not provide details on the training set used for developing the device's algorithms.
-
How the ground truth for the training set was established: Not specified, as training set details are not provided.
Ask a specific question about this device
Page 1 of 1