ReviewFrontiers in medicine2026
The impact of traumatic injury on the respiratory system; a narrative review of injury-associated and clinically-induced mechanisms of trauma-associated pneumonia.
Review in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
Pneumonia is a common complication after major trauma, affecting approximately one third of all traumatic injury victims. The pathophysiology of trauma associated pneumonia (TAP) is complex, with numerous, injury-associated mechanisms and clinically-induced risk factors, making early detection and precise diagnosis challenging. These features are coupled with extensive immune modulation and profound inflammation, which occur simultaneously after traumatic injury. When TAP occurs, it is associated with poor patient outcomes, including increased mortality rates, longer intensive care unit and hospital admissions and increased likelihood to be discharged to an ongoing care or rehabilitation facility. However, with rising prevalence of antibiotic resistance and multi-drug resistant strains of bacteria, the management of TAP is becoming increasingly complex. With profound effects upon patient recovery, long-term outcomes and healthcare associated costs, there is urgent need for increased understanding and awareness of TAP. In this narrative review we aim to deconstruct normal lung physiology, to understand the direct impact of major trauma upon the respiratory system. Specifically, we examine how major trauma, across a spectrum of injury subtypes, influences immune responses, ventilatory mechanics, neuromuscular control of breathing, airway protection, and brain-lung interactions, and how these processes contribute to the development of TAP. Finally, we highlight the diagnostic limitations of current clinical criteria and explore the emerging potential of artificial intelligence and machine learning to synthesise complex, heterogeneous data for the early and precise prediction of TAP.
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