ArticleFrontiers in physiology2026
Respiratory pyramid interface (RPI): a conceptual three-dimensional pyramid model for visualization of lung mechanics during mechanical ventilation.
Article in Frontiers in physiology, 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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Abstract
Mechanical ventilation is a life-saving intervention in critically ill patients; however, the real-time integration of its key physiological determinants elastic load, resistive load, and obstructive components remains cognitively demanding at the bedside. Current monitoring approaches present these parameters in fragmented formats, which may limit rapid interpretation. The Respiratory Pyramid Interface (RPI) is a conceptual, hypothesis-generating framework that integrates the fundamental variables of respiratory mechanics flow (F), pressure (P), and volume (V) into a unified three-dimensional geometric model. Within this structure, resistive, elastic, and obstructive components are represented along the pyramid edges, while derived indices including mechanical power (MP), driving pressure (DP), static compliance (Cstat), the Pyramid Asymmetry Index (PAI), and a pressure-based surrogate of the expiratory time constant (τ_est) are dynamically mapped. The framework was demonstrated across eleven simulated pathophysiological scenarios, each producing distinct geometric patterns corresponding to specific mechanical phenotypes. Importantly, RPI does not introduce new physiological variables but offers a structured visual integration of routinely available ventilator data, intended to support hypothesis generation, education, and pattern recognition rather than to provide direct treatment guidance. Limitations of the underlying single-compartment model including regional lung heterogeneity, pendelluft, spontaneous respiratory effort, and elevated neural respiratory drive are acknowledged. Prospective validation is required before the proposed indices can be considered for clinical decision support.
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