(untitled) (The Review of Diabetic Studies, 2025)
Background: Respiratory failure, classified as hypoxemic (type 1), hypercapnic (type 2), or mixed, represents a life-threatening prehospital emergency characterized by inadequate oxygenation or ventilation, often stemming from COPD exacerbations, pneumonia, or cardiogenic pulmonary edema. Paramedics are frontline providers uniquely positioned for early recognition through clinical signs like tachypnea, accessory muscle use, cyanosis, and tools such as pulse oximetry and capnography, where timely interventions can prevent deterioration, intubation, and mortality in high-burden EMS encounters comprising 11-12% of calls.​ Methods: This scientific review synthesizes evidence from EMS databases, prospective studies, and guidelines on paramedic roles in prehospital respiratory failure, focusing on adult nontraumatic cases. It appraises pathophysiology, assessment protocols (ABCDE approach, vital signs, early warning scores), interventions (oxygen titration, NIV like CPAP/BiPAP, bronchodilators, BVM), special populations (pediatrics, elderly, comorbidities), training standards, barriers, and emerging technologies via descriptive analysis of epidemiology, diagnostic accuracy (50-70%), and outcomes like ICU admissions.​ Results: Paramedics achieve moderate diagnostic concordance (50-60%) with hospital findings but excel in severity gauging (92% accuracy), deploying oxygen (target SpO2 88-94% in COPD), NIV reducing intubation needs, and pharmacotherapy improving vital trends. Prehospital NIV lowers mortality in COPD/CHF; capnography detects hypoventilation superiorly; challenges include environmental constraints, skill variability, and overlaps with cardiac/sepsis mimics, yet interventions correlate with better survival and system efficiency.​ Conclusions: Paramedics significantly enhance respiratory failure outcomes through systematic recognition and management, underscoring needs for expanded NIV protocols, simulation training, portable capnography/telemedicine, and research on special populations to bridge gaps in diagnostic precision and resource-limited settings.​