Abstract

Background In patients requiring respiratory support, clinicians rely on physical exam, radiologic, laboratory, and ventilator-derived measures for the provision of sufficient support while minimizing ventilator and “work of breathing” induced lung injury. Point of care lung ultrasound (LUS) is a widely available tool in hospital and clinic environments. To date, LUS has not been used to evaluate lung strain.

Methods We collected LUS images in four anesthetized, neuromuscularly blocked, and mechanically ventilated pigs being used for another experiment. A feature tracking tool was developed which tracked echo-bright lung structures in ten second clips obtained in triplicate of the right and left, upper and lower lung fields using tidal volumes of 4, 6, 8, 10, and 12 mL/kg. Pleural lines were manually drawn and a program for quantifying lung strain developed with assistance from Anthropic Claude Artificial Intelligence tool. Structures were identified in inspiratory and expiratory frames and tracked bidirectionally with median strain per frame used for calculations.

Results Triplicate measures of lung ultrasound images in four pigs had a median coefficients of variation of 35% (23-47% IQR) and linear modeling of strain with tidal volumes of 4-12 mL/kg showed positive correlation with R2 value ranging from 0.89 to 0.97. Strain measurements were similar after bronchial administration of 1.5M hydrochloric acid.

Conclusions Regional lung strain quantification using LUS is a viable and potentially useful tool for respiratory support management.

Competing Interest Statement

BMV and UAMS Bioventures have applied for a non-provisional patent for the use of lung ultrasound strain measurements for the quantification of lung strain.

Funding Statement

This work was completed with funding support from Arkansas Biosciences Institute.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

IACUC of UAMS gave ethical approval for this work.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Footnotes

Competing interests BMV and UAMS Bioventures have applied for a non-provisional patent for the use of lung ultrasound strain measurements for the quantification of lung strain.

Funding This work was completed with funding support from Arkansas Biosciences Institute.

4-Drafting the work or reviewing it critically for important intellectual content; AND

5-Final approval of the version to be published; AND

6-Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Data Availability

Available upon request

List of AbbreviationsVILIVentilator Induced Lung InjuryARDSAcute Respiratory Distress SyndromePOCUSPoint of Care UltrasoundHClHydrochloric AcidLUSLung UltrasoundPEEPPositive End Expiratory PressureVCVolume ControlCVCoefficients of VariationFFTFast Fourtier TransformNCCNormalized Cross-Correlation