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The Bair Hugger temperature management system is intended to prevent and treat hypothermia. In addition, the temperature management system can be used to provide patient thermal comfort when conditions exist that may cause patients to feel too warm or too cold. The temperature management system can be used with adult and pediatric patients.

The 3MTM Bair Hugger™ Model 675 Total Temperature Management System consists of a Model 675 portable warming unit (with optional rolling cart) along with a 3M Bair Hugger warming blanket or warming gown. The Bair Hugger warming unit provides forced warm air using an electrical resistance heater, fan/blower and a user control interface. Warmed air flows from the warming unit into a Bair Hugger warming blanket or warming gown by means of a flexible connecting hose. The Bair Hugger warming blanket or warming gown is placed over, around or underneath the patient. Small perforations in the blanket or gown allows the forced warm air to be gently dispersed over a patient's skin to prevent and treat hypothermia, and/or to provide patient thermal comfort. The warming unit may be controlled to provide only ambient (non-warmed) air.

Based on the provided text, the device in question is the 3M Bair Hugger Model 675 Total Temperature Management System, which is a medical device designed to prevent and treat hypothermia and provide thermal comfort. This is not an AI/ML powered device, so many of the requested criteria related to AI/ML software validation (like MRMC studies, training/test set details, expert ground truth adjudication) are not applicable to this submission.

The acceptance criteria and the study proving the device meets them are focused on demonstrating substantial equivalence to an existing predicate device through nonclinical bench testing to recognized performance standards.

Here's a breakdown of the requested information based on the provided document, with notes where information is not applicable (N/A) due to the nature of the device:

Acceptance Criteria and Device Performance (Based on "Comparative Data for Determining Substantial Equivalence")

The acceptance criteria are implicitly that the new device performs "as well as" and has "equivalent" safety and performance features as the predicate device, specifically regarding warming performance and safety.

Study Details:

1. A table of acceptance criteria and the reported device performance:

   • See table above.

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

   • This submission relies on nonclinical bench testing to international standards (IEC 80601-2-35). Therefore, "sample size" in the context of human data or AI test sets is not applicable. The "test set" would refer to the physical units of the device tested in the lab. The document does not specify the number of units tested.
   • Data Provenance: The testing was nonclinical bench testing, presumably conducted by the manufacturer (3M Company). The document does not specify the country of origin of the data, but 3M Company is located in St. Paul, Minnesota, USA. The testing is retrospective in the sense that it evaluates a completed device design against established standards and a predicate.

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

   • N/A. This is a hardware medical device; the "ground truth" for its performance is established through objective physical measurements and adherence to engineering and safety standards, not through expert human interpretation as would be the case for an AI diagnostic device.

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

   • N/A. Adjudication methods like 2+1 or 3+1 are used for establishing human consensus ground truth in image analysis or similar tasks. This is a nonclinical bench test of a physical device.

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:

   • N/A. This device does not involve human readers or AI assistance. It's a stand-alone patient warming system.

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

   • N/A. While the device itself operates "standalone" in providing warmth, it's not an algorithm in the sense of AI/ML software. The performance demonstrated is the standalone performance of the physical device as measured by bench testing.

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

   • The "ground truth" for this device's performance and safety is its adherence to established engineering and safety standards (specifically IEC 80601-2-35) and its direct equivalency in functional characteristics (warming performance) and safety features to the predicate device, as determined by objective physical measurements during bench testing.

8. The sample size for the training set:

   • N/A. This is not an AI/ML algorithm that requires a "training set."

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

   • N/A. Not an AI/ML algorithm.

Summary of Approach:

The regulatory strategy for the 3M Bair Hugger Model 675 Total Temperature Management System was to demonstrate substantial equivalence to a previously cleared predicate device (Augustine Medical Bair Hugger Model 750 Total Temperature Management System, K001149). This was achieved primarily through:

• Comparison of Technological Characteristics: Showing the new device has the same design, materials, chemical composition, and energy source as the predicate.
• Nonclinical Bench Testing: Performing tests according to the recognized international standard IEC 80601-2-35 to objectively demonstrate equivalent warming performance and safety features compared to the predicate device.

The FDA's clearance (K171373) confirms that, based on this evidence, the device is considered substantially equivalent and raises no new questions of safety or effectiveness.

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The IOB Temperature Management system is indicated for hypothermic patients or normothermic patients for whom induced hyperthermia or localized increase in temperature is clinically indicated.

The IOB Temperature Management System consists of the IOB warming unit and the IOB warming blankets. The IOB warming unit draws ambient- temperature air through a 0.2 micron air filter. The filtered air is warmed to a selected temperature. The warmed air enters the IOB Warming Blanket through the hose and is distributed through delivery channels. Perforations on the patient side of the air delivery channels in the warming blanket gently disperse the warmed air over and around the patient. The warming unit has three temperature settings of 32°C, 38°C, and 43°C. These temperature settings are servo-controlled by a thermistor placed at the end of hose where the hose connects to the blanket. The unit can also delivers ambient-temperature air. The temperature indicated on the control panel is the temperature of air being delivered to the blanket at the end of the hose. A control thermistor in the warming unit adjusts the power applied to the heater to maintain the selected temperature. This enables the system to maintain the selected temperature under variations in ambient temperature. Besides, the warming unit has high and low air flow for choose.

The IOB Warming Blankets in this submission are the following:
Torso IOB-001 Lower Body IOB-002 Upper Body IOB-003 Full Body IOB-004 Pediatric Underbody IOB-005 Adult Underbody IOB-006 Pediatric Full Body IOB-007 Full Body Surgical IOB-008 Large Pediatric Underbody IOB-009 Spinal Underbody IOB-010 Lithotomy Underbody IOB-011 Pediatric Lower Body IOB-012 Pediatric Long IOB-014 Cath Lab IOB-015 Surgical Access IOB-016 Chest Access IOB-017 Multi-Access IOB-018 Dual Port Torso IOB-019 Cardiac Access IOB-020 XL Upper Body IOB-021 Outpatient Care IOB-022 Cardiac IOB-023
These blankets are single-use and disposable. Each blanket consists of two lavers of non-woven polypropylene fabric coated with a layer of polyethylene. The lavers are bonded together to form a distribution network of air delivery channels. The warm air is distributed around the blanket through the delivery channels and exits the blanket through a specially designed series of perforations in the patient side of the blanket.

The provided text is a 510(k) summary for the IOB Temperature Management System, which is a medical device for warming patients. It addresses substantial equivalence to a predicate device (Bair Hugger Temperature Management System) rather than outlining acceptance criteria and a study proving device performance in the context of an AI/ML powered device.

Therefore, the specific information requested about acceptance criteria and studies demonstrating performance as would be expected for an AI/ML driven diagnostic or assistive technology (e.g., sample sizes, ground truth establishment, expert adjudication, MRMC studies, standalone performance) is not present in the provided document.

The document focuses on the physical and functional characteristics of a thermal regulating system and its blankets, demonstrating its equivalence to a legally marketed predicate device.

However, I can extract the safety and performance characteristics mentioned in the document, which serve a similar purpose of demonstrating the device's suitability for its intended use, even if they are for a physical device rather than an AI/ML algorithm.

Here's the information that can be extracted, framed to best fit your request:

Acceptance Criteria and Study for the IOB Temperature Management System (as applicable to a physical thermal regulating device)

Since the IOB Temperature Management System is a physical device (a thermal regulating system) and not an AI/ML powered diagnostic or assistive technology, the typical "acceptance criteria" and "study" framework for AI/ML performance metrics (like sensitivity, specificity, AUC, etc.) and related study design details (sample size for test/training, ground truth methods, expert adjudication, MRMC) does not apply.

Instead, the provided document outlines the general "Safety and Performance Characteristics" and their verification methods to demonstrate substantial equivalence to a predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Not applicable / Not specified in the provided document for the types of tests described (temperature uniformity, accelerated stability, electrical safety, software validation, biocompatibility). These tests typically involve a defined number of units or samples under specific conditions rather than patient "test sets" as understood for AI/ML evaluation.

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

• Not applicable / Information not provided. The ground truth for these device performance tests is based on objective measurements and established standards (e.g., precise temperature readings, material degradation, electrical standards, ISO biocompatibility standards) rather than expert consensus on diagnostic interpretations.

4. Adjudication method for the test set

• Not applicable / Information not provided. Adjudication methods are not relevant for these types of engineering and material performance tests.

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

• No. This is a physical device, not an AI/ML diagnostic. A MRMC study is not relevant here.

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

• No. This is a physical device, not an AI/ML algorithm.

7. The type of ground truth used

• For Temperature Uniformity: Objective physical measurements against a standard for thermal performance (implied).
• For Shelf-Life: Objective laboratory testing for material degradation and functional integrity over time (accelerated aging).
• For Electrical Safety/EMC: Compliance with international safety standards (IEC60601-1, IEC 60601-1-2) through objective measurements.
• For Software Validation: Verification against design specifications and functional requirements.
• For Biocompatibility: Adherence to ISO 10993 standards (cytotoxicity, irritation, sensitivity) through laboratory testing.

8. The sample size for the training set

• Not applicable. There is no "training set" in the context of an AI/ML algorithm for this physical device.

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

• Not applicable. There is no "training set" or corresponding ground truth establishment mentioned for this device.

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