(203 days)
For thermal regulation of a patient's temperature to prevent hypothermia and/or reduce cold discomfort during and after surgical procedures. It is intended for use by appropriately trained healthcare professionals in clinical environments.
Snuggle Warm® Convective Warming Systems (SW-4000 and EQ-5000) that will include the proposed blankets (Model SW-2013, SW-2014R, SW-2015, SW-2016, SW-2018, SW-2018 & SW-2019). The Convective Warming Systems (SW-4000 and EQ-5000), are identified as a Thermal Regulation System by the FDA, consists of a Convective Warming Unit (temperature controller), a hose, and a single-use Convective Warming Blanket. The Convective Warming Systems' (SW-4000 and EQ-5000) single-use disposable Convective Warming Blankets are placed in contact with the patient and attached to a warming unit via a hose with hose-end temperature controls. The warming unit generates warm air that is distributed throughout the warming blanket to warm the patient during and after surgical procedures. It is intended for thermally regulating a patient's temperature to prevent hypothermia by a warm air heated blanket system to reduce cold discomfort during and after surgical procedures. The seven proposed blankets are single-use Convective Warming Blankets that will be added to the existing Convective Warming Blanket family. They are components of the Convective Warming Systems (SW-4000 and EQ-5000) of which the indications for use remain the same.
The provided text describes a 510(k) summary for several convective warming blankets. It does not contain information about acceptance criteria or a study proving performance against such criteria in the context of an AI/ML device.
However, based on the non-clinical data section, I can deduce the type of assessment performed. The device in question is a medical device (convective warming blankets), and its performance is evaluated against established standards and through comparison to predicate devices, rather than through an AI/ML model's statistical performance.
Therefore, many of the requested fields (sample size, experts, adjudication, MRMC, standalone, training set) are not applicable to this type of device submission.
Here's a breakdown of what can be extracted and what cannot:
1. A table of acceptance criteria and the reported device performance:
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Standards Compliance: | |
| ASTM F2196:2002 (Standard Specification for Circulating Liquid and Forced Air Patient Temperature Management) | Blankets designed to meet these requirements. |
| ISO 10993-1:2003 (Biological Evaluation of Medical Devices - Evaluation and Testing) | Blankets designed to meet these requirements. |
| Material and Manufacturing Similarity: | Proposed and predicate blankets are made of similar materials and employ similar manufacturing processes. |
| Functional Equivalence: | Bench testing demonstrated that the proposed convective warming blankets are substantially equivalent to existing Snuggle Warm® convective warming blankets. |
| Design Features: | The proposed SW-2016, SW-2018, and SW-2019 blankets have movable panels similar to the predicate Bair Hugger® model 610. |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):
- Not applicable for this type of device. The assessment was based on bench testing and comparison of design and materials, not a "test set" of data in the context of an AI/ML device. The document explicitly states "Clinical Data: Not required."
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):
- Not applicable. No ground truth establishment by external experts for a test set is mentioned. The evaluation was internal bench testing and compliance with standards.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- Not applicable. No adjudication method for a test set is mentioned.
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:
- Not applicable. This refers to AI/ML device studies, which this is not.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Not applicable. This refers to AI/ML device studies, which this is not.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
- Not applicable in the AI/ML sense. The "ground truth" here is compliance with engineering standards (ASTM F2196, ISO 10993-1) and demonstrated functional equivalence through bench testing to previously approved predicate devices.
8. The sample size for the training set:
- Not applicable. No training set is mentioned as this is not an AI/ML device.
9. How the ground truth for the training set was established:
- Not applicable. No training set is mentioned.
§ 870.5900 Thermal regulating system.
(a)
Identification. A thermal regulating system is an external system consisting of a device that is placed in contact with the patient and a temperature controller for the device. The system is used to regulate patient temperature.(b)
Classification. Class II (performance standards).