The mammalian lung is characterized by heterogeneity in both its structure and function, by incorporating an asymmetric branching airway tree optimized for maintenance of efficient ventilation, perfusion, and gas exchange. Despite potential benefits of naturally occurring heterogeneity in the lungs, there may also be detrimental effects arising from pathologic processes, which may result in deficiencies in gas transport and exchange. Regardless of etiology, pathologic heterogeneity results in the maldistribution of regional ventilation and perfusion, impairments in gas exchange, and increased work of breathing. In extreme situations, heterogeneity may result in respiratory failure, necessitating support with a mechanical ventilator. This review will present a summary of measurement techniques for assessing and quantifying heterogeneity in respiratory system structure and function during mechanical ventilation. These methods have been grouped according to four broad categories: (1) inverse modeling of heterogeneous mechanical function; (2) capnography and washout techniques to measure heterogeneity of gas transport; (3) measurements of heterogeneous deformation on the surface of the lung; and finally (4) imaging techniques used to observe spatially-distributed ventilation or regional deformation. Each technique varies with regard to spatial and temporal resolution, degrees of invasiveness, risks posed to patients, as well as suitability for clinical implementation. Nonetheless, each technique provides a unique perspective on the manifestations and consequences of mechanical heterogeneity in the diseased lung.
Keyphrases
- mechanical ventilation
- respiratory failure
- single cell
- acute respiratory distress syndrome
- intensive care unit
- extracorporeal membrane oxygenation
- room temperature
- primary care
- radiation therapy
- healthcare
- climate change
- newly diagnosed
- squamous cell carcinoma
- risk assessment
- computed tomography
- lymph node
- ejection fraction
- high resolution
- locally advanced
- carbon dioxide
- human health
- prognostic factors
- neural network
- patient reported