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The Nuubo Smart is indicated for use on patients who may be asymptomatic or who may suffer from transient symptoms such as palpitations, shortness of breath, dizziness, light-headedness, pre-syncope, fatigue, or anxiety. The Nuubo Smart continuously records and stores ECG and activity data for up to 30 days at a time. The Nuubo Smart detects arrhythmias at the end of each monitoring day upon download of the ECG data. The Nuubo Smart is Rx use device.

Nuubo Smart is composed of the following main components:
Nuubo30 (wearable) - The Nuubo30 is the wearable box that contains one Nuubo30 textile unit, ECG conductive cream, patient instructions and washing bag. Each textile can be used for 30 days.
NuuboREC (recorder). The Nuubo recorder is a small, lightweight device that records ECG continuously. The device records 2 Leads of ECG data up to 30 days. The device also records data from a 3-axis accelerometer located inside the device. The patient can activate the button while wearing the product to mark a symptom. To start and stop the recording the user will press the on/off button. The data is stored into a micro SD memory card.
Nuubo Dock - The Nuubo Dock is the element for downloading the data from the NuuboREC and uploads that data to the Nuubo Cloud. It consists of a plastic stand with a smartphone attached to it and a microUSB port where the NuuboREC is connected for recharge and data download. The smartphone is a single-purpose device that is intended to be always connected to the dock. The dock is intended to be connected to an outlet in the patient's home with the charger provided. The dock allows to charge the NuuboREC device, and during this charging, it downloads the data from the NuuboREC device into the smartphone. The smartphone is software restricted and can only run the Nuubo Dock App that transmits and receives data from the Nuubo Cloud.
Leonardo Smart - Is used for downloading, analysis, visualization and report of the ECG-data stored by the recorder device and for managing the recorder device. The new version Leonardo Smart allows a clinical professional previously authenticated in the platform, to download and review signals recorded by the NuuboREC device which have been uploaded using Nuubo Dock.

Here's a breakdown of the acceptance criteria and study information for the Nuubo Smart device, based on the provided text:

1. Acceptance Criteria and Reported Device Performance

The document primarily focuses on demonstrating substantial equivalence to predicate devices and compliance with harmonized standards. Direct, quantifiable acceptance criteria for arrhythmia detection performance are not explicitly stated for the new Nuubo Smart device in a separate table, but the performance of its underlying algorithm (inherited from Nuubo System K173461) is provided:

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

The document mentions that the Nuubo Leonardo Arrhythmia detection algorithm (same as Nuubo System K173461) was tested against standard databases:

• MIT-BIH database: A widely recognized standard for arrhythmia analysis.
• AHA database: Another standard for arrhythmia analysis.

The exact sample sizes (number of recordings or patients) from these databases used for testing are not explicitly stated in this document. These databases are generally retrospective collections of ECG data. The country of origin of the data is not specified for these public databases, but they are international benchmarks.

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

The document does not provide information on the number of experts or their qualifications used to establish the ground truth for the MIT-BIH and AHA databases. These databases typically have expert-annotated ground truth established by cardiologists or electrophysiologists as part of their creation.

4. Adjudication Method for the Test Set

The document does not specify the adjudication method (e.g., 2+1, 3+1) used for the ground truth in the MIT-BIH and AHA databases. This information is usually part of the detailed documentation for those databases.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

A multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with AI assistance vs. without AI assistance was not mentioned or described in the provided text. The evaluation focused on the standalone performance of the algorithm against standard ECG databases.

6. Standalone (Algorithm Only) Performance

Yes, a standalone (algorithm only without human-in-the-loop performance) evaluation was done for the arrhythmia detection algorithm. As stated: "The Nuubo Leonardo Arrhythmia detection algorithm (same as Nuubo System K173461) was tested per requirements of IEC60601-2-47, and showed a QRS Sensitivity and QRS Positive predictivity of over 99% against the MIT-BIH database, and over 97% against the AHA database."

7. Type of Ground Truth Used

The ground truth used for the standalone algorithm testing was based on expert-annotated arrhythmias within the MIT-BIH and AHA databases. These are widely used standard databases with established annotations.

8. Sample Size for the Training Set

The document does not provide information on the sample size used for the training set of the Nuubo Leonardo Arrhythmia detection algorithm.

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

The document does not provide information on how the ground truth for the training set was established. It only discusses the testing of the algorithm against public databases.

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The physlQ Heart Rhythm Module is intended for use by a physician or other qualified medical professionals for the calculation of heart rate and heart rate variability and the detection of atrial fibrillation using ambulatory ECG data. The physlQ Heart Rhythm Module supports receiving and analyzing single-lead ECG signals recorded in a compatible format from FDA-cleared ECG biosensor devices using "wet" electrode technology when assessment of rhythm is desired. The phys\Q Heart Rhythm Module is for use in subacute clinical settings for remote patient monitoring. The physlQ Heart Rhythm Module is not for use in patients requiring or life-sustaining systems or ECG Alam devices.

The physIQ Heart Rhythm Module (Version 1.0) is a computerized all-software callable function library in the Python programming language that is designed for calculating heart rate and heart rate variability and for detecting atrial fibrillation determined by automated analysis of any single electrocardiogram (ECG) channel collected by commercially-available ECG biosensor devices. This Heart Rhythm Module will be integrated by the customer organization into an end-to-end system (biosensor data collection to clinician display) that makes calls into the product, most typically via a Python middleware script. The "middleware" accesses the source ECG data from a customer's data collection system, most likely via its own application programming interface (API), and makes calls to the physIQ Heart Rhythm Module to input ECG for processing into the vital sign outputs of the product. These outputs are returned to the middleware, which may insert these results into a downstream monitoring system for clinical use.

Here's an analysis of the acceptance criteria and supporting studies for the physIQ Heart Rhythm Module (Version 1.0), based on the provided FDA 510(k) document:

phyIQ Heart Rhythm Module (Version 1.0) Acceptance Criteria and Performance

1. Table of Acceptance Criteria and Reported Device Performance

The provided document states that "Performance testing following guidelines of ANS/AAMI EC572012: Testing and Reporting Performance Results of Cardiac Rhythm and ST segment Measurement Algorithms has been applied to each of the algorithms. The performance testing results for all algorithms were compared to physIQ's defined acceptance criteria for performance testing. All algorithms met their corresponding acceptance criteria."

However, the specific quantitative acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) for each algorithm (Heartbeat Detector, Heart Rate, Heart Rate Variability, and Atrial Fibrillation Detector) are not explicitly detailed in the provided text. Similarly, the exact reported performance metrics (e.g., the achieved sensitivity/specificity values) are also not provided in a summarized table within this document. The document only confirms that "All algorithms met acceptance criteria."

Therefore, an exact table with numerical acceptance criteria and reported performance cannot be generated from the given text.

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

The document states:
"further supportive clinical validation testing of the physIQ Heart Rhythm Module was performed using electrocardiography (ECG) signals captured from ambulatory patients using a wearable single-lead biosensor device which were annotated by medical experts in cardiology."

• Sample Size for Test Set: Not explicitly stated. The document only refers to "ambulatory patients" without specifying the number of patients or the duration/amount of ECG data.
• Data Provenance: The ECG signals were "captured from ambulatory patients" using two commercially available FDA-cleared patches: HealthPatch (K152139) manufactured by VitalConnect Inc. and BodyGuardian (K121197; K151188) manufactured by Preventice Inc. The country of origin of the data is not specified, but the use of FDA-cleared devices typically implies data collected in regions where such devices are marketed, often the US. The data appears to be retrospective, as it was "captured from ambulatory patients" and then annotated.

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

• Number of Experts: Not explicitly stated. The document mentions "medical experts in cardiology."
• Qualifications of Experts: They were described as "medical experts in cardiology." Specific experience level (e.g., "10 years of experience") is not provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. The document only says the data was "annotated by medical experts in cardiology." It does not specify if multiple experts independently annotated and then reached consensus, or if a single expert provided the ground truth, or if a specific adjudication process (like 2+1 or 3+1) was used.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not mentioned or described in the provided text. The testing focused on the standalone performance of the algorithm against expert annotations.
• Effect Size of Human Improvement with AI Assistance: Not applicable, as no MRMC study was described.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance

• Standalone Performance: Yes, a standalone performance evaluation was conducted. The document states: "this testing did not use any patch-generated vitals, but instead compared physIQ Heart Rhythm Module outputs to annotations by cardiology experts using ECG captured from two commercially-available patches..." This indicates the algorithm's output was directly compared to the expert-derived ground truth without human intervention in the device's output.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert Consensus (or Expert Annotation). The document explicitly states the ECG signals were "annotated by medical experts in cardiology."

8. Sample Size for the Training Set

• Sample Size for Training Set: Not mentioned in the provided text. The document focuses on the performance testing and clinical validation rather than the development and training details of the algorithms.

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

• Ground Truth for Training Set: Not mentioned in the provided text. As with the training set size, the document does not delve into the methodology for establishing ground truth for any training data used for the algorithms.

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The MoMe™ Continuous ECG Monitor and Arrhythmia Detector System (MoMe™ System) is indicated for:

1. Patients who have demonstrated a need for cardiac monitoring and are at low risk of developing primary ventricular fibrillation or sustained ventricular tachycardia.
2. Patients with dizziness or lightheadedness.
3. Patients with palpitations.
4. Patients with syncope of unknown etiology.
5. Patients who require monitoring for non-life threatening arrhythmias, such as atrial fibrillation, other supraventricular arrhythmias, evaluation of various bradyarrhythmias and intermittent bundle branch block.
6. Patients recovering from coronary artery bypass graft (CABG) surgery who require monitoring for arrhythmias.
7. Patients requiring monitoring for arrhythmias inducing co-morbid conditions such as hyperthyroidism or chronic lung disease.
8. Patients with obstructive sleep apnea to evaluate possible nocturnal arrhythmias.
9. Patients requiring arrhythmia evaluation for etiology of stroke or transient cerebral ischemia, possibly secondary to atrial fibrillation.

The MoMeTM Continuous ECG Monitor and Arrhythmia Detector System (abbreviated to MoMe System in this section) is a remote physiologic monitoring system that detects non-life threatening arrhythmias. The MoMeTM System incorporates a front end device worn by the patient that collects and streams ECG, heart rate and motion (activity) to a dedicated smartphone that continuously transmits the data to remote server. The system then uses proprietary algorithms to continually analyze data and provide reports of detected events. These reports can be accessed anytime, anywhere by a physician using a standard browser or a MoMe iPad App.

The provided text includes a 510(k) Summary for the MoMe™ Continuous ECG Monitor and Arrhythmia Detector System, which details its performance data and the studies conducted to demonstrate substantial equivalence to predicate devices.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance:

The document refers to the FDA Guidance "Class II Special Controls Guidance Document: Arrhythmia Detector and Alarm" (October 2003) for the acceptance criteria, and states that the device was tested using "standard industry practices and in accordance" with this guidance. However, the specific acceptance criteria (e.g., minimum sensitivity, specificity for certain arrhythmia types) and corresponding reported device performance values are not explicitly detailed in the provided text. The document broadly states: "The MoMe Arrhythmia detection algorithm has been tested using standard industry practices and in accordance with the FDA Guidance 'Class II Special Controls Guidance Document: Arrhythmia Detector and Alarm', released October 2003. The Software Verification and Validation reports, MoMe System Verification and Validation report, Algorithm validation report, Transceiver Verification and Validation report, Usability test reports all demonstrate that the MoMe System meets its intended use and design input requirements."

Without the specific performance targets from the FDA Guidance and the numeric results from the MoMe system's validation reports, a detailed table cannot be created.

2. Sample size used for the test set and the data provenance:

The document mentions "Algorithm validation report" but does not specify the sample size of the test set or the data provenance (e.g., country of origin, retrospective/prospective nature).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

This information is not provided in the given text.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

This information is not provided in the given text.

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:

This information is not provided in the given text. The study mentioned is a "standalone" algorithm validation, not a comparative effectiveness study involving human readers with and without AI assistance.

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

Yes, a standalone algorithm validation was done. The document states: "The MoMe Arrhythmia detection algorithm has been tested..." and refers to an "Algorithm validation report." This implies testing the algorithm's performance independent of human intervention.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

The document does not explicitly state the type of ground truth used for the algorithm validation. For arrhythmia detection algorithms, ground truth is typically established by expert cardiologists reviewing the ECG tracings.

8. The sample size for the training set:

The document does not specify the sample size for the training set.

9. How the ground truth for the training set was established:

The document does not specify how the ground truth for the training set was established.

Summary of available information regarding the study:

• Study Type: Algorithm validation, software verification and validation, system verification and validation, transceiver verification and validation, usability testing.
• Standards Followed: IEC 60601-1, IEC 60601-1-2, IEC 60601-1-11, IEC 60601-2-47, ANSI/AAMI/ISO EC 57:1998/R(2008), ANSI/AAMI EC53:1995/(R)2008, ISO 10993 (various parts for biological evaluation).
• Compliance: The MoMe System complies with applicable clauses of IEC 60601 and was tested in accordance with the FDA Guidance "Class II Special Controls Guidance Document: Arrhythmia Detector and Alarm" (October 2003).

Missing Information (not found in the provided text):

• Specific quantitative acceptance criteria for arrhythmia detection (e.g., sensitivity, specificity, accuracy for different arrhythmia types).
• Specific quantitative reported performance metrics of the MoMe system against these criteria.
• Sample size of the test set.
• Data provenance (country of origin, retrospective/prospective).
• Number and qualifications of experts for ground truth establishment.
• Adjudication method for ground truth.
• Details of any MRMC comparative effectiveness study or human reader improvement data.
• Type of ground truth explicitly defined (though likely expert review for ECGs).
• Sample size of the training set.
• Method for establishing ground truth for the training set.

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