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A machine-learning regional clustering approach to understand ventilator-induced lung injury: a proof-of-concept experimental study

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Autor:

Cruces, Pablo
Retamal, Jaime
Damián, Andrés
Lago, Graciela
Blasina, Fernanda
Oviedo, Vanessa
Medina, Tania
Pérez, Agustín
Vaamonde, Lucía
Dapueto, Rosina
González-Dambrauskas, Sebastián
Serra, Alberto
Monteverde-Fernandez, Nicolás
Namías, Mauro
Martínez, Javier
Hurtado, Daniel E.

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Artículo

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Palabras clave:

Mechanical ventilation
Ventilator-induced lung injury
Lung strain
Computed tomography
Diagnostic imaging

Descriptores:

VENTILADORES MECÁNICOS
RESPIRACIÓN ARTIFICIAL
LESIÓN PULMONAR IDUCIDA POR VENTILACIÓN MECÁNICA
DIAGNÓSTICO POR IMAGEN
TOMOGRAFÍA

Año de publicación:

2024

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Resumen:

Background The spatiotemporal progression and patterns of tissue deformation in ventilator-induced lung injury (VILI) remain understudied. Our aim was to identify lung clusters based on their regional mechanical behavior over space and time in lungs subjected to VILI using machine-learning techniques. Results Ten anesthetized pigs (27 ± 2 kg) were studied. Eight subjects were analyzed. End-inspiratory and end-expiratory lung computed tomography scans were performed at the beginning and after 12 h of one-hit VILI model. Regional image-based biomechanical analysis was used to determine end-expiratory aeration, tidal recruitment, and volumetric strain for both early and late stages. Clustering analysis was performed using principal component analysis and K-Means algorithms. We identified three different clusters of lung tissue: Stable, Recruitable Unstable, and Non-Recruitable Unstable. End-expiratory aeration, tidal recruitment, and volumetric strain were significantly different between clusters at early stage. At late stage, we found a step loss of end-expiratory aeration among clusters, lowest in Stable, followed by Unstable Recruitable, and highest in the Unstable Non-Recruitable cluster. Volumetric strain remaining unchanged in the Stable cluster, with slight increases in the Recruitable cluster, and strong reduction in the Unstable Non-Recruitable cluster. Conclusions VILI is a regional and dynamic phenomenon. Using unbiased machine-learning techniques we can identify the coexistence of three functional lung tissue compartments with different spatiotemporal regional biomechanical behavior.

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SpringerOpen

EN:

Intensive Care Medicine Experimental. 2024;12(1)

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Citación:

Cruces P, Retamal J, Damián A y otros. A machine-learning regional clustering approach to understand ventilator-induced lung injury: a proof-of-concept experimental study. Intensive Care Medicine Experimental [en línea]. 2024;12(1). 9 p.

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Licencia Creative Commons Atribución (CC - By 4.0)

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