SBIR/STTR Research Award

SBIR Phase I: Development Of A Sensor System For Feedback Guided Manual Ventilation With A Bag Valve Mask.

Award identifier 544135469cc0574eebe4459ede1d5c4ec1639acc7dbe8405bfffe4eb9c342fbdAwarding agency National Science Foundation

Award amount

$223,363

Program and phase

SBIR · Phase I

Award year

2019

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
Pneumico, Inc.
Proposal award date
2019-07-01
Contract end date
2020-06-30
UEI
CWVJNYMPZTS6
Solicitation number
Not reported
Topic code
BM

Research abstract

Bag-valve masks are simple, squeezable tools for manual ventilation of patients and are the most common manual method used in both emergencies and routine surgical procedures. The simplicity and minimal cost of BVMs have contributed to their widespread use and sales of approximately 13.1 million disposable resuscitators annually in the U.S. alone. Despite the regular use of BVMs, providers of any training level regularly deliver ineffective ventilation or uncontrolled, inadvertent, forced over-inflation. Studies show that, while using a standard BVM, 88.4% of trained users exceeding the recommended pressure and 73.8% exceeding the recommended volume range2 Improper ventilation for even short periods can lead to two major complications 1) Lung injury from over-stretching (called volutrauma); too much air and 2) Lung injury from over-pressurization (called barotrauma); too much pressure. These conditions can lead to life-threatening conditions as well as prolonged and expensive hospital stays with costs approaching $170,000 (CAD) per injury. When used in emergency situations, execution of proper BVM technique is required to provide optimal oxygenation during CPR. Devices to monitor the depth and rate of CPR compressions are widely available and are soon to be required in all training centers by the AHA. However, no technologies exist to provide similar feedback for manual ventilation. It is likely that real-time feedback can reduce or eliminate the rate of dangerous ventilation among caregivers of all levels and that BVM training with feedback can lead to improved skill retention. Thus, a novel technique and complementary device have been developed to provide real time feedback to trained and training personnel and ensure they meet guidelines from the American Heart Association (AHA) and European Resuscitation Council (ERC). This project is a comprehensive system development pathway composed of three aims that will refine our sensor system and initiate studies into the benefits of real-time feedback during BVM usage. This method utilizes a unique sensor system that can be coupled to a bag valve mask to measure the air pressure and volume delivered to a patient. Using these metrics for each squeeze of the bag, this device can alert users of both excessive pressure and volume in real time. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Technology keywords

Not reported

Agency intelligence

National Science Foundation

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.