(138 days)
The Maternal Fetal Monitoring - Central Nurse System (hereinafter called "MFM-CNS") is a clinical data managing software application and is indicated for antepartum and intrapartum monitoring of pregnant women in a healthcare setting.
MFM-CNS is intended to manage perinatal monitoring data acquired from bedside monitors or manual input for viewing at the central nursing station. The system also produces an electronic medical record.
MFM-CNS has display fields for the following obstetric data:
- patient demographics
- provider notes
- fetal heart rate (FHR)
- uterine activity
- fetal movement
- maternal heart rate
- SpO2
- non-invasive blood pressure (NIBP)
- respiratory rate
- temperature
- pulse rate
MFM-CNS Lite (hereinafter called "Lite") is a clinical data managing software application and is indicated for antepartum monitoring of pregnant women in a healthcare setting.
Lite is intended to manage antepartum-monitoring data acquired from bedside monitors and produce electronic medical records.
Lite has display fields for the following obstetric data:
- patient demographics
- provider notes
- fetal heart rate (FHR)
- uterine activity
- fetal movement
The MFM-CNS v3.91 and MFM-CNS Lite v1.1 are clinical data managing software applications. Both applications manage clinical data of fetal monitoring and uterine activity, and the MFM-CNS v3.91 additional monitors maternal vital signs. Data are automatically acquired from bedside monitors, for the purpose of collecting, processing and saving the patient and/or clinical data that is normally provided on record papers and/or separate bedside monitors. They provide electronic medical records and operate with off-the-shelf software and hardware.
The MFM-CNS v3.91 and MFM-CNS Lite v1.1 are intended to be used in hospital clinical areas such as monitor units, delivery room, etc. They are intended to be operated by or under guidance of qualified healthcare professionals, not intended for home healthcare environment. During monitoring, the user should check the results on the bedside monitor in person, even though they could observe the results on the MFM-CNS v3.91 and MFM-CNS Lite v1.1 system interface. The user cannot only depend on the MFM-CNS v3.91 and MFM-CNS Lite v1.1 system to obtain monitoring data, because whether the data provided by the system are accurate depends on the stability of the operating system, the performance of PC station and the network.
The provided text is a 510(k) summary for a medical device (Edan Instruments, Inc.'s Central Monitoring System MFM-CNS Lite v1.1 and MFM-CNS v3.91). This type of submission focuses on demonstrating substantial equivalence to a predicate device rather than presenting a standalone study with defined acceptance criteria and performance metrics for the new device in the same way one might for a novel diagnostic algorithm.
Therefore, the document does not contain a table of acceptance criteria and reported device performance for the subject device, nor does it detail a clinical study proving the device meets specific performance criteria. Instead, it relies on demonstrating that the new devices (MFM-CNS v3.91 and MFM-CNS Lite v1.1) are substantially equivalent to a previously cleared predicate device (EDAN Instrument, Inc. Central Monitoring System, model MFM-CNS v3.82, K143695).
However, I can extract information related to the performance data provided to support the substantial equivalence claim.
Here's a breakdown of the available information based on your request:
1. A table of acceptance criteria and the reported device performance
The document does not provide a table of acceptance criteria and reported device performance for the subject device in clinical terms (e.g., sensitivity, specificity, accuracy). Instead, it states that "the non-clinical performance testing showed that the subject devices are as safe and as effective as the predicate device."
The "performance" described is in the context of software verification and validation, and compliance with standards.
| Acceptance Criteria (from testing performed) | Reported Device Performance (MFM-CNS v3.91 and MFM-CNS Lite v1.1) |
|---|---|
| Risk analysis according to ISO 14971: 2007 | Passed |
| Software life cycle management according to IEC 62304: 2006 | Passed all testing |
| Bench testing per IEC 60601-1-8: 2006 (Medical electrical equipment General requirements for basic safety and essential performance - Collateral standard: General requirements, tests and guidance for alarm systems in medical electrical equipment and medical electrical systems) | All results show pass |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
This information is not provided in the document, as it focuses on software verification and validation, not a clinical test set. The device is a "clinical data managing software application," meaning its primary function is to display and manage data from other monitors, not to make independent diagnoses or measurements.
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. The "ground truth" for software validation would typically be established by comparing the software's output to the expected output according to specifications and functional requirements, rather than expert interpretation of clinical cases.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not provided.
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
An MRMC comparative effectiveness study was not done. The device is a data management system, not an AI-assisted diagnostic tool for human readers.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
A standalone human-in-the-loop performance study was not done. The performance described is related to the software's functionality and safety, not its diagnostic accuracy in a clinical context. The document explicitly states: "During monitoring, the user should check the results on the bedside monitor in person, even though they could observe the results on the MFM-CNS v3.91 and MFM-CNS Lite v1.1 system interface. The user cannot only depend on the MFM-CNS v3.91 and MFM-CNS Lite v1.1 system to obtain monitoring data, because whether the data provided by the system are accurate depends on the stability of the operating system, the performance of PC station and the network." This indicates it's designed as an information display and management tool, not an independent diagnostic algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
The concept of "ground truth" in the clinical sense (e.g., pathology, outcomes data) is not applicable to the performance data presented. The "ground truth" for the software validation would be its adherence to established software requirements and industry standards.
8. The sample size for the training set
This information is not applicable/not provided. The device is a software application for data management; it does not explicitly mention machine learning or AI models that require a training set in the conventional sense. The "training" here refers to software development and testing cycles rather than model training.
9. How the ground truth for the training set was established
This information is not applicable/not provided for the reasons stated above.
§ 884.2740 Perinatal monitoring system and accessories.
(a)
Identification. A perinatal monitoring system is a device used to show graphically the relationship between maternal labor and the fetal heart rate by means of combining and coordinating uterine contraction and fetal heart monitors with appropriate displays of the well-being of the fetus during pregnancy, labor, and delivery. This generic type of device may include any of the devices subject to §§ 884.2600, 884.2640, 884.2660, 884.2675, 884.2700, and 884.2720. This generic type of device may include the following accessories: Central monitoring system and remote repeaters, signal analysis and display equipment, patient and equipment supports, and component parts.(b)
Classification. Class II (performance standards).