Simulation-based optimisation to quantify heterogeneity of specific ventilation and perfusion in the lung by the Inspired Sinewave Test
Abstract The degree of specific ventilatory heterogeneity (spatial unevenness of ventilation) of the lung is a useful marker of early structural lung changes which has the potential to detect early-onset disease. The Inspired Sinewave Test (IST) is an established noninvasive ‘gas-distribution’ type...
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/2ea56e7c104744e4bc0438e1496037c8 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Abstract The degree of specific ventilatory heterogeneity (spatial unevenness of ventilation) of the lung is a useful marker of early structural lung changes which has the potential to detect early-onset disease. The Inspired Sinewave Test (IST) is an established noninvasive ‘gas-distribution’ type of respiratory test capable of measuring the cardiopulmonary parameters. We developed a simulation-based optimisation for the IST, with a simulation of a realistic heterogeneous lung, namely a lognormal distribution of spatial ventilation and perfusion. We tested this method in datasets from 13 anaesthetised pigs (pre and post-lung injury) and 104 human subjects (32 healthy and 72 COPD subjects). The 72 COPD subjects were classified into four COPD phenotypes based on ‘GOLD’ classification. This method allowed IST to identify and quantify heterogeneity of both ventilation and perfusion, permitting diagnostic distinction between health and disease states. In healthy volunteers, we show a linear relationship between the ventilatory heterogeneity versus age ( $${R}^{2}=0.42$$ R 2 = 0.42 ). In a mechanically ventilated pig, IST ventilatory heterogeneity in noninjured and injured lungs was significantly different (p < 0.0001). Additionally, measured indices could accurately identify patients with COPD (area under the receiver operating characteristic curve is 0.76, p < 0.0001). The IST also could distinguish different phenotypes of COPD with 73% agreement with spirometry. |
|---|