Reduce Respiratory Failure Risk

Emergency physicians use the AGM100 to help diagnose hypoxemia and imminent respiratory failure. 

AGM100 is an AI enhanced point-of-care cardiorespiratory assessment decision support tool that reduces clinical risk and diagnostic uncertainty by quickly showing respiratory severity that requires action (e.g., supplemental oxygen need or escalated care) and points to underlying conditions.

• Reduce unnecessary CT scans for patients with potential pulmonary embolism by 32% resulting in savings of 72 minutes average per patient.
• Improve triage of cardiorespiratory complaints and reduce average length of stay in your ED by 20%
• Reduce admission variability to improve throughput

AGM100 AUROC Performance (J. Clin. Med.  2025, 14, 5880)

*Note: This NIH-funded prognostic study (JCM2025), published in the Journal of Clinical Medicine, concludes that the AGM100's core metric "has high prognostic value with a fidelity that exceeds or matches the accuracy of the best predictive tools for respiratory failure in the published literature."   

Physician-scientists evaluated how well the AGM100 could predict imminent respiratory failure in the Emergency Department, using the need for oxygen therapy within 24 hours as a real-world proxy for patient decline. The study found that across a broad range of underlying conditions (including COPD, asthma, heart failure, and pulmonary embolism), the device's Oxygen Deficit (OD) metric predicted this outcome with outstanding accuracy (AUROC 0.937) and high statistical reliability (p<0.001). This represents the highest reported prognostic performance for any noninvasive respiratory metric. Essentially, the study confirms this tool gives clinicians an early and reliable warning sign of impending respiratory failure, allowing for more accurate triage and proactive intervention before a patient decompensates, which has major implications for improving patient safety and managing resource-intensive care.

As the only point of care tool which can accurately predict respiratory distress severity in about a minute, our clinicians are able to

• Reduce clinical risk: Provides assurance that you are making the right decision
• Reduce time to accurate diagnosis and disposition- admit/observe/release decision
• Potentially achieve 30x time advantage relative to traditional means